U.S. patent application number 15/496235 was filed with the patent office on 2018-03-15 for automated image cropping and sharing.
The applicant listed for this patent is Intel Corporation. Invention is credited to Marci Meingast, Shibani Kapoor Shah.
Application Number | 20180075065 15/496235 |
Document ID | / |
Family ID | 52689181 |
Filed Date | 2018-03-15 |
United States Patent
Application |
20180075065 |
Kind Code |
A1 |
Shah; Shibani Kapoor ; et
al. |
March 15, 2018 |
AUTOMATED IMAGE CROPPING AND SHARING
Abstract
Technologies for automated cropping and sharing of images
include an imaging device configured to capture a digital image,
recognize one or more subjects represented in the image, determine
the social network groups associated with the subjects of the
digital image, and crop the digital image to generate one or more
sub-images. Each of the sub-images includes subjects associated
with a particular social network group. Each of the sub-images may
be shared with the relevant social network group. Subjects may
include persons, objects, or events, and may be recognized through
computer vision techniques as well as using contextual data. The
imaging device may receive social network data from one or more
social network servers, which may be used to determine the relevant
social network groups. The imaging device may also exclude subjects
from the cropped sub-images based on one or more content policies.
Other embodiments are described and claimed.
Inventors: |
Shah; Shibani Kapoor; (San
Jose, CA) ; Meingast; Marci; (Berkeley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
52689181 |
Appl. No.: |
15/496235 |
Filed: |
April 25, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
14129651 |
Dec 27, 2013 |
9633046 |
|
|
PCT/US2013/060340 |
Sep 18, 2013 |
|
|
|
15496235 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 11/60 20130101;
G06F 16/951 20190101; G06T 3/40 20130101; H04N 5/225 20130101; G06K
9/00221 20130101; G06Q 50/01 20130101; H04L 65/601 20130101; G06K
9/00677 20130101; H04N 5/2628 20130101; G06F 16/24575 20190101;
G06K 9/3241 20130101; G06F 16/5854 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; H04N 5/262 20060101 H04N005/262; H04N 5/225 20060101
H04N005/225; H04L 29/06 20060101 H04L029/06; G06T 3/40 20060101
G06T003/40; G06K 9/32 20060101 G06K009/32; G06K 9/00 20060101
G06K009/00 |
Claims
1. An imaging device for automated image cropping, the imaging
device comprising: an image capture module to capture a digital
image; a subject recognition module to recognize a first subject
represented in the digital image; a social network module to
determine a first social network group to which the first subject
is associated; and an image crop module to crop, based on a content
rule policy, the digital image to generate a sub-image including
the first subject, the composition rule policy comprising a set of
rules that define the composition of the sub-image based on the
recognized first subject.
2. The imaging device of claim 1, further comprising an image
sharing module to share the sub-image with the first social network
group.
3. The imaging device of claim 1, wherein to recognize the first
subject comprises to: detect a position and characteristics of a
human face represented in the digital image; detect a position and
an identity of an object represented in the digital image; or
detect an event represented in the digital image.
4. The imaging device of claim 1, wherein to recognize the first
subject comprises to recognize an event represented in the digital
image based on contextual data received from a contextual sensor of
the imaging device.
5. The imaging device of claim 1, wherein to recognize the first
subject comprises to recognize an event represented in the digital
image based on contextual data generated by an application of the
imaging device.
6. The imaging device of claim 1, wherein the social network module
is further to receive social network data from a social network
server, the social network data to associate the first subject and
the first social network group; wherein to determine the first
social network group comprises to determine the first social
network group based on the social network data.
7. The imaging device of claim 1, wherein the subject recognition
module is further to: recognize a second subject represented in the
digital image; and determine that the second subject is not
associated with the first social network group; wherein to crop the
digital image further comprises to exclude the second subject from
the sub-image.
8. The imaging device of claim 7, wherein: the social network
module is further to determine a second social network group to
which the second subject is associated, wherein the first subject
is not associated with the second social network group; the image
crop module is further to crop, based on a composition rule policy,
the digital image to generate a second sub-image including the
second subject and excluding the first subject, the composition
rule policy comprising a set of rules that define the composition
of the second sub-image based on the recognized second subject; and
the imaging device further comprises an image sharing module to (i)
share the sub-image with the first social network group and (ii)
share the second sub-image with the second social network
group.
9. The imaging device of claim 1, wherein the subject recognition
module is further to recognize a second subject represented in the
digital image; wherein to crop the digital image further comprises
to exclude the second subject from the sub-image as a function of a
content policy stored on the imaging device, the content policy
comprising a set of rules to define whether to include or to
exclude the second subject based on the first social network
group.
10. The imaging device of claim 1, wherein: the subject recognition
module is further to recognize a second subject represented in the
digital image; and the social network module is further to
determine that the second subject is not associated with the first
social network group; wherein to crop the digital image based on
the composition rule policy comprises to: determine whether a rule
of thirds composition rule policy generates an acceptable sub-image
including the first subject and excluding the second subject;
determine whether a portrait composition rule policy generates an
acceptable sub-image including the first subject and excluding the
second subject; and apply a fallback composition rule policy in
response to a determination that the rule of thirds composition
rule policy does not generate an acceptable sub-image and a
determination that the portrait composition rule policy does not
generate an acceptable sub-image, wherein to apply the fallback
composition rule policy comprises to: crop the digital image to
generate the sub-image based on a smallest bounding box containing
the first subject; and obscure the second subject in the
sub-image.
11. A method for automated image cropping, the method comprising:
capturing, by an imaging device, a digital image; recognizing, by
the imaging device, a first subject represented in the digital
image; determining, by the imaging device, a first social network
group to which the first subject is associated; and cropping, by
the imaging device and based on a content rule policy, the digital
image to generate a sub-image including the first subject, the
composition rule policy comprising a set of rules that define the
composition of the sub-image based on the recognized first
subject.
12. The method of claim 11, further comprising: recognizing, by the
imaging device, a second subject represented in the digital image;
and determining, by the imaging device, that the second subject is
not associated with the first social network group; wherein
cropping the digital image further comprises excluding the second
subject from the sub-image.
13. The method of claim 11, further comprising: recognizing, by the
imaging device, a second subject represented in the digital image;
wherein cropping the digital image further comprises excluding the
second subject from the sub-image as a function of a content policy
stored on the imaging device, the content policy comprising a set
of rules to define whether to include or to exclude the second
subject based on the first social network group.
14. The method of claim 11, further comprising: recognizing, by the
imaging device, a second subject represented in the digital image;
and determining, by the imaging device, that the second subject is
not associated with the first social network group; wherein
cropping the digital image based on the composition rule policy
comprises: determining whether a rule of thirds composition rule
policy generates an acceptable sub-image including the first
subject and excluding the second subject; determining whether a
portrait composition rule policy generates an acceptable sub-image
including the first subject and excluding the second subject; and
applying a fallback composition rule policy in response to
determining that the rule of thirds composition rule policy does
not generate an acceptable sub-image and determining that the
portrait composition rule policy does not generate an acceptable
sub-image, wherein applying the fallback composition rule policy
comprises: cropping the digital image to generate the sub-image
based on a smallest bounding box containing the first subject; and
obscuring the second subject in the sub-image.
15. One or more computer-readable storage media comprising a
plurality of instructions that in response to being executed cause
an imaging device to: capture a digital image; recognize a first
subject represented in the digital image; determine a first social
network group to which the first subject is associated; and crop,
based on a content rule policy, the digital image to generate a
sub-image including the first subject, the composition rule policy
comprising a set of rules that define the composition of the
sub-image based on the recognized first subject.
16. The one or more computer-readable storage media of claim 15,
wherein to recognize the first subject comprises to recognize an
event represented in the digital image based on contextual data
received from a contextual sensor of the imaging device.
17. The one or more computer-readable storage media of claim 15,
wherein to recognize the first subject comprises to recognize an
event represented in the digital image based on contextual data
generated by an application of the imaging device.
18. The one or more computer-readable storage media of claim 15,
further comprising a plurality of instructions that in response to
being executed cause the imaging device to: recognize a second
subject represented in the digital image; and determine that the
second subject is not associated with the first social network
group; wherein to crop the digital image further comprises to
exclude the second subject from the sub-image.
19. The one or more computer-readable storage media of claim 15,
further comprising a plurality of instructions that in response to
being executed cause the imaging device to: recognize a second
subject represented in the digital image; wherein to crop the
digital image further comprises to exclude the second subject from
the sub-image as a function of a content policy stored on the
imaging device, the content policy comprising a set of rules to
define whether to include or to exclude the second subject based on
the first social network group.
20. The one or more computer-readable storage media of claim 15,
further comprising a plurality of instructions that in response to
being executed cause the imaging device to: recognize a second
subject represented in the digital image; and determine that the
second subject is not associated with the first social network
group; wherein to crop the digital image based on the composition
rule policy comprises to: determine whether a rule of thirds
composition rule policy generates an acceptable sub-image including
the first subject and excluding the second subject; determine
whether a portrait composition rule policy generates an acceptable
sub-image including the first subject and excluding the second
subject; and apply a fallback composition rule policy in response
to determining that the rule of thirds composition rule policy does
not generate an acceptable sub-image and determining that the
portrait composition rule policy does not generate an acceptable
sub-image, wherein to apply the fallback composition rule policy
comprises to: crop the digital image to generate the sub-image
based on a smallest bounding box containing the first subject; and
obscure the second subject in the sub-image.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application is a continuation application of
U.S. application Ser. No. 14/129,651, entitled "AUTOMATED IMAGE
CROPPING AND SHARING," which was filed on Dec. 27, 2013, and which
is a national stage entry under 35 U.S.C. .sctn. 371(b) of
International Application No. PCT/US2013/060340, which was filed on
Sep. 18, 2013.
BACKGROUND
[0002] Digital images and media have growing importance in many
people's lives. Images and media are used by many for communication
and for self-expression. Through the use of digital images and
media, a person may build emotional connections with other people,
including family, friends, and the public at large.
[0003] Many people have a growing desire to share their personal
experiences with other people, for example through social
networking websites. In particular, many people enjoy sharing
digital images on such websites. However, sharing images while at
the same time maintaining personal privacy are a concern for many
people. Typically a person must manually specify particular
contacts or groups of contacts prior to sharing an image.
Additionally, a person typically must manually crop or modify
digital images prior to sharing if required to ensure privacy.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The concepts described herein are illustrated by way of
example and not by way of limitation in the accompanying figures.
For simplicity and clarity of illustration, elements illustrated in
the figures are not necessarily drawn to scale. Where considered
appropriate, reference labels have been repeated among the figures
to indicate corresponding or analogous elements.
[0005] FIG. 1 is a simplified block diagram of at least one
embodiment of a system for automated image cropping and
sharing;
[0006] FIG. 2 is a simplified block diagram of at least one
embodiment of various environments that may be established by the
system of FIG. 1;
[0007] FIG. 3 is a simplified flow diagram of at least one
embodiment of a method for automated image cropping and sharing
that may be executed by an imaging device of the system of FIGS. 1
and 2;
[0008] FIG. 4 is a simplified flow diagram of at least one
embodiment of a method for image cropping using composition rules
that may be executed by the imaging device of FIGS. 1 and 2;
[0009] FIG. 5 is an illustration of an image that may be captured
by the imaging device of FIGS. 1 and 2; and
[0010] FIGS. 6A-6C are illustrations of sub-images that may be
generated by the imaging device of FIGS. 1 and 2.
DETAILED DESCRIPTION OF THE DRAWINGS
[0011] While the concepts of the present disclosure are susceptible
to various modifications and alternative forms, specific
embodiments thereof have been shown by way of example in the
drawings and will be described herein in detail. It should be
understood, however, that there is no intent to limit the concepts
of the present disclosure to the particular forms disclosed, but on
the contrary, the intention is to cover all modifications,
equivalents, and alternatives consistent with the present
disclosure and the appended claims.
[0012] References in the specification to "one embodiment," "an
embodiment," "an illustrative embodiment," etc., indicate that the
embodiment described may include a particular feature, structure,
or characteristic, but every embodiment may or may not necessarily
include that particular feature, structure, or characteristic.
Moreover, such phrases are not necessarily referring to the same
embodiment. Further, when a particular feature, structure, or
characteristic is described in connection with an embodiment, it is
submitted that it is within the knowledge of one skilled in the art
to effect such feature, structure, or characteristic in connection
with other embodiments whether or not explicitly described.
Additionally, it should be appreciated that items included in a
list in the form of "at least one of A, B, and C" can mean (A);
(B); (C): (A and B); (A and C); (B and C); or (A, B, and C).
Similarly, items listed in the form of "at least one of A, B, or C"
can mean (A); (B); (C): (A and B); (A and C); (B and C); or (A, B,
and C).
[0013] The disclosed embodiments may be implemented, in some cases,
in hardware, firmware, software, or any combination thereof. The
disclosed embodiments may also be implemented as instructions
carried by or stored on a transitory or non-transitory
machine-readable (e.g., computer-readable) storage medium, which
may be read and executed by one or more processors. A
machine-readable storage medium may be embodied as any storage
device, mechanism, or other physical structure for storing or
transmitting information in a form readable by a machine (e.g., a
volatile or non-volatile memory, a media disc, or other media
device).
[0014] In the drawings, some structural or method features may be
shown in specific arrangements and/or orderings. However, it should
be appreciated that such specific arrangements and/or orderings may
not be required. Rather, in some embodiments, such features may be
arranged in a different manner and/or order than shown in the
illustrative figures. Additionally, the inclusion of a structural
or method feature in a particular figure is not meant to imply that
such feature is required in all embodiments and, in some
embodiments, may not be included or may be combined with other
features.
[0015] Referring now to FIG. 1, in one embodiment, a system 100 for
automated image cropping and sharing includes an imaging device 102
and, in some embodiments, one or more social network servers 104
and an image repository server 106. The imaging device 102, social
network server 104, and image repository server 106 may be in
communication with each other over a network 108. In use, as
discussed in more detail below, the imaging device 102 captures a
digital image and processes the digital image to detect and
recognize subjects represented within the image. The subjects may
include persons, objects, or events depicted in the image. The
imaging device 102 associates the subjects recognized in the image
with relevant social network groups of a user of the imaging device
102. A social network group may be embodied as a collection of
contacts generated from the user's social network data (e.g.,
contacts on a social network site or in the user's contact list).
The social network data may be stored locally on the imaging device
102 or on one or more social network servers 104. After associating
social network groups, the imaging device 102 generates a sub-image
for each relevant social network group, cropping the image to
include related subjects and exclude unrelated subjects. The
imaging device 102 uses one or more composition rules to generate
aesthetically pleasing sub-images in an automated manner. After the
sub-images have been generated, the imaging device 102 shares the
sub-images with the relevant social network groups. For example,
the imaging device 102 may transmit the sub-images to the social
network server(s) 104 or the image repository server 106 to share
the sub-images.
[0016] Automated image cropping and sharing thus allows image
sharing without tedious, manual cropping and categorization.
Accordingly, the user may share more images with more recipients.
Additionally, users may be more confident about sharing images
because automated image sharing helps to confine shared images to
appropriate audiences, in particular preventing sharing of
objectionable content and thereby preserving privacy in some
cases.
[0017] The imaging device 102 is may be embodied as any type of
device capable of performing the functions described herein,
including, without limitation, a digital camera, a smartphone, a
cellular telephone, a handset, a computer, a tablet computer, a
laptop computer, a notebook computer, a messaging device, a vehicle
telematics device, a network appliance, a web appliance, a
distributed computing system, a multiprocessor system, a
processor-based system, and/or a consumer electronic device. As
shown in FIG. 1, the imaging device 102 includes a processor 120,
an input/output subsystem 122, a memory 124, a data storage device
126, and communication circuitry 132. Of course, the imaging device
102 may include other or additional components, such as those
commonly found in a mobile imaging device (e.g., various
input/output devices), in other embodiments. Additionally, in some
embodiments, one or more of the illustrative components may be
incorporated in, or otherwise form a portion of, another component.
For example, the memory 124, or portions thereof, may be
incorporated in the processor 120 in some embodiments.
[0018] The processor 120 may be embodied as any type of processor
capable of performing the functions described herein. For example,
the processor 120 may be embodied as a single or multi-core
processor(s), digital signal processor, microcontroller, or other
processor or processing/controlling circuit. Similarly, the memory
124 may be embodied as any type of volatile or non-volatile memory
or data storage capable of performing the functions described
herein. In operation, the memory 124 may store various data and
software used during operation of the imaging device 102 such as
operating systems, applications, programs, libraries, and drivers.
The memory 124 is communicatively coupled to the processor 120 via
the I/O subsystem 122, which may be embodied as circuitry and/or
components to facilitate input/output operations with the processor
120, the memory 124, and other components of the imaging device
102. For example, the I/O subsystem 122 may be embodied as, or
otherwise include, memory controller hubs, input/output control
hubs, firmware devices, communication links (i.e., point-to-point
links, bus links, wires, cables, light guides, printed circuit
board traces, etc.) and/or other components and subsystems to
facilitate the input/output operations. In some embodiments, the
I/O subsystem 122 may form a portion of a system-on-a-chip (SoC)
and be incorporated, along with the processor 120, the memory 124,
and other components of the imaging device 102, on a single
integrated circuit chip.
[0019] The data storage device 126 may be embodied as any type of
device or devices configured for short-term or long-term storage of
data such as, for example, memory devices and circuits, memory
cards, hard disk drives, solid-state drives, or other data storage
devices. The data storage device 126 may store one or more content
policies 128 used to identify potentially objectionable content, as
well as one or more composition rules 130 used for automated image
cropping.
[0020] The communication circuitry 132 of the imaging device 102
may be embodied as any communication circuit, device, or collection
thereof, capable of enabling communications between the imaging
device 102, the social network server(s) 104, the image repository
server 106, and/or other remote devices over the network 108. The
communication circuitry 132 may be configured to use any one or
more communication technology (e.g., wireless or wired
communications) and associated protocols (e.g., Ethernet,
Bluetooth.RTM., Wi-Fi.RTM., WiMAX, etc.) to effect such
communication.
[0021] In the illustrative embodiment, the imaging device 102
further includes a display 134, a number of contextual sensor(s)
136, and a camera 140. The display 134 of the imaging device 102
may be embodied as any type of display capable of displaying
digital information such as a liquid crystal display (LCD), a light
emitting diode (LED), a plasma display, a cathode ray tube (CRT),
or other type of display device. In some embodiments, the display
134 may be coupled to a touch screen to allow user interaction with
the imaging device 102.
[0022] The contextual sensor(s) 136 may be embodied as any type of
sensor(s) capable of generating digital data indicative of the
context of the imaging device 102 and/or the user of the imaging
device 102. For example, in the illustrative embodiment, the
contextual sensors 136 include location circuitry 138. The location
circuitry 138 may be embodied as any type of sensor or circuitry
capable of determining the precise or approximate location of the
imaging device 102. For example, the location circuitry 138 may be
embodied as a global positioning system ("GPS") receiver capable of
determining the precise coordinates of the imaging device 102. In
other embodiments, the location circuitry 138 may use trilateration
and/or triangulation to determine the position of the imaging
device 102 using distances and/or angles to cellular network
towers, or other structures, with known positions, provided by the
communication circuitry 132. In other embodiments, the location
circuitry 138 may determine the approximate position of the imaging
device 102 based on association to wireless networks and/or access
points with known positions, using the communication circuitry
132.
[0023] The camera 140 may be embodied as a digital camera or other
digital imaging device integrated with the imaging device 102 or
otherwise communicatively coupled thereto. The camera 140 may
include an electronic image sensor, such as an active-pixel sensor
(APS), e.g., a complementary metal-oxide-semiconductor (CMOS)
sensor, or a charge-coupled device (CCD). The camera 140 may be
capable of capturing still images and/or video. In some
embodiments, the camera 140 may be capable of capturing
three-dimensional depth imaging describing a scene, for example
using a depth camera, a stereoscopic camera, a structured light
sensor, a rangefinder, a light field sensor, or any other sensor
capable of capturing depth information.
[0024] Each of the social network servers 104 is configured to
store and allow retrieval of social network data of the user of the
imaging device 102. Each social network server 104 may be embodied
as any type of server computing device, or collection of devices,
capable of performing the functions described herein. As such, each
social network server 104 may be embodied as a single server
computing device or a collection of servers and associated devices.
For example, in some embodiments, each social network server 104
may be embodied as a "virtual server" formed from multiple
computing devices distributed across the network 108 and operating
in a public or private cloud. Accordingly, although the social
network server(s) 104 are illustrated in FIG. 1 as embodied as a
single server computing device, it should be appreciated that each
social network server 104 may be embodied as multiple devices
cooperating together to facilitate the functionality described
below. Further, each social network server 104 may include
components and features similar to the imaging device 102 and/or
typical data servers such as a processor, I/O subsystem, memory,
data storage, communication circuitry, and various peripheral
devices, which are not illustrated in FIG. 1 for clarity of the
present description.
[0025] The image repository server 106 is configured to store and
allow sharing of cropped images produced by the imaging device 102.
The image repository server 106 may be embodied as any type of
server computing device, or collection of devices, capable of
performing the functions described herein. As such, similar to the
social network servers 104, the image repository server 106 may be
embodied as a single server computing device or a collection of
servers and associated devices. For example, in some embodiments,
the image repository server 106 may be embodied as a "virtual
server" formed from multiple computing devices distributed across
the network 108 and operating in a public or private cloud.
Accordingly, although the image repository server 106 is
illustrated in FIG. 1 as embodied as a single server computing
device, it should be appreciated that the image repository server
106 may be embodied as multiple devices cooperating together to
facilitate the functionality described below. Further, the image
repository server 106 may include components and features similar
to the imaging device 102 and/or the social network server(s) 104,
such as a processor, I/O subsystem, memory, data storage,
communication circuitry, and various peripheral devices, which are
not illustrated in FIG. 1 for clarity of the present description.
Additionally or alternatively, in some embodiments the image
repository server 106 or the functionality of the image repository
server 106 may be incorporated in one or more of the social network
server(s) 104.
[0026] As discussed in more detail below, the imaging device 102,
the social network server(s) 104, and the image repository server
106 may be configured to transmit and receive data with each other
and/or other devices of the system 100 over the network 108. The
network 108 may be embodied as any number of various wired and/or
wireless networks. For example, the network 108 may be embodied as,
or otherwise include, a wired or wireless local area network (LAN),
a wired or wireless wide area network (WAN), a cellular network,
and/or a publicly-accessible, global network such as the Internet.
As such, the network 108 may include any number of additional
devices, such as additional computers, routers, and switches, to
facilitate communications among the devices of the system 100.
[0027] Referring now to FIG. 2, in an illustrative embodiment, the
imaging device 102 establishes an environment 200 during operation.
The illustrative environment 200 includes an image capture module
202, a subject recognition module 204, a social network module 210,
an image crop module 212, and an image sharing module 214. The
environment 200 further includes content policies 216, composition
rules 218, and in some embodiments, social network data 220. The
various modules of the environment 200 may be embodied as hardware,
firmware, software, or a combination thereof.
[0028] The image capture module 202 is configured to capture
digital images at the direction of the user. The image capture
module 202 may capture images directly using image capture hardware
such as the camera 140. Additionally or alternatively, the image
capture module 202 may capture images by receiving the images from
an external device such as a separate, dedicated digital camera, a
network file server, or other image source. In such embodiments,
the image capture module 202 may allow selection of an image for
capture, for example using a user interface displayed on the
display 134.
[0029] The subject recognition module 204 is configured to detect
and recognize subjects such as persons, objects, or events
represented in the digital image data. Subjects may be recognized
using computer vision techniques such as facial recognition
algorithms or object recognition and categorization algorithms, as
described further below. In some embodiments, those functions may
be performed by sub-modules, for example by a facial detection and
recognition module 206 or an object/event detection and recognition
module 208.
[0030] The social network module 210 is configured to associate
social network groups with subjects recognized in the digital
image. For example, the social network module 210 may associate
recognized persons with the social network groups of which they are
a member. The social network module 210 may also associate objects
or events with groups related to that object/event or class of
object/event. The social network groups may be defined in the
social network data 220, as described below.
[0031] The image crop module 212 is configured to generate
sub-images based on the captured digital image for the relevant
social network groups, as discussed in more detail below. For
example, for each relevant social network group, the image crop
module 212 generates crop inclusion targets and crop exclusion
targets listing the subjects to include and exclude from the
corresponding sub-image, respectively. The image crop module 212
may reference the content policies 216 to identify subjects for
exclusion from the sub-image, for example, to identify potentially
objectionable or offensive objects or events for each social
network group. When generating the sub-images, the image crop
module 212 applies one or more composition rules 218 to control the
appearance of the generated sub-image.
[0032] The social network data 220 may include any data describing
or categorizing the social contacts of the user of the imaging
device 102. The social network data 220 organizes the contacts of
the user into a collection of social network groups. The social
network groups may be defined by the user, for example by grouping
contacts into groups such as friends, families, colleagues, or
other groups. In some embodiments, the social network groups may be
determined based on relationships between contacts stored in the
social network data 220. Additionally, the social network data 220
may define associations between social network groups and various
objects or events. For example, a social network group may include
contacts having an affinity for a particular object or event, such
as sports fans, automobile enthusiasts, theater patrons, and other
similar associations. In some embodiments, the social network data
220 may be embodied as a directed graph, with nodes representing
particular contacts, objects, events, or social network groups, and
with edges representing the relationships between the nodes.
Additionally or alternatively, the social network data 220 may be
embodied as a database or flat list of contact information such as
a contact list, address book, or similar data structure. The social
network data 220 may be stored locally by the imaging device 102,
or may be stored on one or more remote social network servers
104.
[0033] The content policies 216 include a set of rules defining
whether to include or exclude recognized subjects from cropped
sub-images based on the associated social network groups. For
example, the content policies 216 may include a set of
objectionable, offensive, irrelevant, or otherwise undesirable
objects or events for each social network group. Thus, the image
crop module 212 may automatically filter inappropriate content from
sub-images based on the audience of the sub-image. The content
policies 216 may be configurable by the user of the imaging device
102 and/or learned or otherwise updated over time.
[0034] The composition rules 218 may be embodied as a set of rules
or policies describing aesthetically pleasing image compositions.
Each composition rule 218 may include rules or policies describing
appropriate positioning of subjects within the generated sub-image,
appropriate image resolution and aspect ratios, appropriate sizes
and numbers of subjects within the sub-image, and/or other aspects
of the composition of the generated sub-images. As discussed in
more detail below, the image crop module 212 may query the
composition rules 218 to determine which of the composition rules
218 would successfully generate an acceptable sub-image when
applied to a particular digital image. The composition rules 218
may be applied in a predetermined order of priority or as specified
by the user of the imaging device 102. The composition rules 218
may be based on traditional photographic techniques including, for
example, the rule of thirds and portraiture composition rules. The
composition rules 218 may also include a fallback rule when other
composition rules 218 are not capable of generating an acceptable
sub-image.
[0035] Still referring to FIG. 2, the imaging device 102 may
communicate with one or more social network servers 104, which each
may establish an environment 240 during operation. The illustrative
environment 240 includes the social network data 220 and may
include cropped images data 242. The social network data 220 is
configured to perform the same functions as described above. The
cropped images data 242 is configured to store the cropped
sub-images generated by and received from the imaging device 102.
The cropped images data 242 limits access to the sub-images to
members of the appropriate social network groups. The various
modules of the environment 240 may be embodied as hardware,
firmware, software, or a combination thereof.
[0036] Additionally, the imaging device 102 may communicate with an
image repository server 106, which may establish an environment 260
during operation. The illustrative environment 260 includes the
cropped images data 242, which is configured to perform the same
functions as described above. The various modules of the
environment 260 may be embodied as hardware, firmware, software, or
a combination thereof.
[0037] Referring now to FIG. 3, in use, the imaging device 102 may
execute a method 300 for automated image cropping and sharing. The
method 300 begins with block 302, in which the imaging device 102
captures a digital image. The imaging device 102 may capture a new
image using the camera 140, or the imaging device 102 may select an
existing image. The existing image may have been previously
captured by the imaging device 102 using the camera 140, or may
have been received from another device, such as a separate digital
camera.
[0038] In block 304, the imaging device 102 processes the digital
image. For example, in block 306, the imaging device 102 processes
the digital image to detect subjects represented in the digital
image. The detected subjects may include any feature represented by
the digital image that may provide a basis for organizing or
sharing, such as human faces, persons, objects, or events.
Detection of the subjects includes determining the presence of the
subject in the digital image as well as determining the relative
location of the subject in the digital image. The location of the
subject may be described by a bounding box, the smallest
rectangular region of the digital image containing the detected
subject. In block 308, the imaging device 102 processes the digital
image to recognize the detected subjects. The subjects may be
recognized by associating identifying information with the
particular subject. For example, detected persons may be recognized
by determining identifying information such as name or social
network profile. Detected objects or events may be recognized by
categorizing, labeling, or otherwise associating semantic
information with the detected subject (e.g., the shape of the
object). The imaging device 102 may perform any known algorithm to
detect and/or recognize subjects within the digital image. In some
embodiments, the imaging device 102 may use additional data beyond
the digital image data to detect and/or recognize subjects. For
example, the imaging device 102 may use context data received from
the contextual sensor(s) 136 or three-dimensional depth data
received from a depth camera.
[0039] In block 310, in some embodiments the imaging device 102 may
perform facial detection and recognition to detect and recognize
subjects represented the digital image. Any known machine learning
technique for facial detection and recognition may be used, such as
rapid detection from a boosted cascade of simple features. Example
facial detection and recognition algorithms are described in Paul
Viola & Michael J. Jones, Robust Real-Time Face Detection, 57
Int'l J. Comp. Vision 137 (2004); Andrew Wagner et al., Towards a
Practical Face Recognition System: Robust Alignment and
Illumination by Sparse Representation, 34 IEEE Transactions on
Pattern Analysis & Machine Intelligence 372 (2012). Recognized
faces may be associated with particular persons or profiles
described in the social network data 220.
[0040] In block 312, in some embodiments, the imaging device 102
may perform object and/or event recognition and/or detection. Any
known machine learning technique for detecting, classifying, or
identifying objects and events may be used. For example, objects
and events can be detected and recognized using algorithms based on
a bag of words of features and statistical techniques including,
for example, Naive Bayes, Latent Dirichlet Allocation, and Boosting
on a feature hierarchy, as described in Gabriella Csurka et al.,
Visual Categorization with Bags of Keypoints, Proc. of ECCV Int'l
Workshop on Statistical Learning in Computer Vision (2004); Li
Fei-Fei & Pietro Perona, A Bayesian Hierarchial Model for
Learning Natural Scene Categories, Proc. of IEEE Computer Vision
& Pattern Recognition 524 (2005). The imaging device 102 may
also use additional contextual data generated by the contextual
sensors 136 or by applications of the imaging device 102 to
recognize objects or events depicted in the image. For example,
consider that the image depicts objects that are recognized as
being associated with "sports" or "baseball," such as a field, a
stadium, a baseball player, etc. The imaging device 102 may use
location information generated by the location circuitry 138 to
determine the identity of the particular stadium represented in the
image. The imaging device 102 may use ticketing, payment, or
scheduling information to determine the particular sporting event
or sporting teams represented in the image. That contextual
information may be generated by an application executing on the
imaging device 102, such as a calendar application, a ticketing
application, or a mobile payment application.
[0041] Referring now to FIG. 5, an example digital image 500 is
illustrated including several recognized subjects. The image 500
includes three recognized faces 502, 504, 506. The face 502 has
been recognized as a friend of the user, the face 504 has been
recognized as the user, and the face 506 has been recognized as the
father of the user. The image 500 also includes two recognized
objects and/or events 508, 510. The object 508 has been recognized
as a baseball player. The event 510 has been recognized as a
baseball game. Of course, the baseball player 508 is also a person
and in some embodiments the face of the baseball player 508 may
also be a recognized subject.
[0042] Referring back to FIG. 3, in block 314, the imaging device
102 associates relevant social network groups to the digital image,
based on the recognized subjects. As described above, the social
network data 220 describes the relationships between persons known
to the user. Those relationships may include groupings of persons,
for example family and friends of the user. The social network data
220 may also describe attributes or interests of persons or groups
of persons. The imaging device 102 determines relevant social
network groups based on the subjects recognized in the image. As
described above, the imaging device 102 may receive the social
network data 220 from one or more social network servers 104.
[0043] In block 316, in some embodiments the imaging device 102
associates faces recognized in the image with social network
groups. The imaging device 102 may determine a social network
profile associated with each recognized face and in turn associate
all social network groups for that social network profile to the
recognized face. Those groupings may be predefined by the user, or
may be determined by the imaging device 102 based on the social
network data 220. For example, the imaging device 102 may determine
a group of persons with a direct relationship to each subject
represented in the digital image, based on relationships described
in the social network data 220. In other words, the imaging device
102 may determine all persons within the user's social network
having a relationship with a subject of the digital image. For
example, referring again to FIG. 5, the imaging device 102 may
associate the recognized friend 502 and the user 504 with a
"friends" social network group of the social network data 220. The
imaging device 102 may further associate the recognized user 504
and the father 506 with a "family" social network group of the
social network data 220.
[0044] Referring back to FIG. 3, in block 318, in some embodiments
the imaging device 102 may associate recognized objects and/or
events with social network groups. As described above, the social
network data 220 may associate objects, interests, locations, or
other semantic information with the social network groups. For
example, referring to FIG. 5, the imaging device 102 may associate
the baseball player 508 and the baseball game 510 with one or more
social network group associated with baseball. In some embodiments,
the imaging device 102 may associate the objects and events 508,
510 with several social network groups at different levels of
specificity: for example, a general sports group, a more-specific
baseball group, and a highly specific fan group for a particular
team of the baseball game 510.
[0045] Referring back to FIG. 3, in block 320, the imaging device
102 determines crop inclusion targets and crop exclusion targets
for each social network group identified as being associated with
the image. The crop inclusion and exclusion targets define the
particular subjects recognized in the image that will be included
in or excluded from each sub-image based on the associated social
network group. In block 322, in some embodiments the imaging device
102 includes and excludes subjects based on the social network data
220. For example, the imaging device 102 may include recognized
persons from the same social network group and exclude persons not
in the same social network group. Additionally or alternatively,
the imaging device 102 may include objects or events associated
with a particular social network group and exclude all prominent
faces in the image.
[0046] Referring now to FIG. 6A, an illustrative generated
sub-image 602 of the image 500 is shown. The sub-image 602 is
associated with the friends social network group described above.
Accordingly, the crop inclusion targets illustratively include the
friend 502 and the user 504, and the crop exclusion targets include
the father 506. The objects or events 508, 510 may or may not be
included in the crop exclusion targets. Referring now to FIG. 6B,
another illustrative generated sub-image 604 is associated with a
sports social network group as described above. Accordingly, the
crop inclusion targets for the sub-image 604 illustratively include
the baseball player 508 and the baseball game 510, and the crop
exclusion targets include all of the faces 502, 504, 506. Referring
now to FIG. 6C, an illustrative generated sub-image 606 is
associated with the family social network group described above.
Accordingly, the crop inclusion targets for sub-image 606
illustratively include the user 504 and the father 506, and the
crop exclusion targets include the friend 502. The objects or
events 508, 510 may or may not be included in the crop inclusion
targets.
[0047] Referring back to FIG. 3, in some embodiments, in block 324
the imaging device 102 may include and exclude objects based on
content policies 216 associated with each social network group. The
content policies 216 may define, for each social network group,
objectionable content, irrelevant content, or other objects that
the user does not wish to share with that social network group. For
example, the content policies 216 may define that alcohol-related
objects such as beer bottles should not be shared with the family
social network group. In such example, the imaging device 102 may
add detected beer bottles to the crop exclusion targets for
sub-images associated with the family social network group.
[0048] In block 326, the imaging device 102 crops the digital image
to generate sub-images for the relevant social network groups,
based on the crop targets. In block 328, the imaging device 102
applies one or more of the composition rules 218 while generating
the sub-images. The composition rules 218 allow the imaging device
102 to automatically generate aesthetically pleasing sub-images
based on the particular subjects to be included and/or excluded
from each sub-image. One embodiment of a method for applying the
composition rules 218 is described further below in connection with
FIG. 4.
[0049] In some embodiments, in block 330 the imaging device 102 may
present interactive image crop selection. The imaging device 102
may generate one or more preview representations of the sub-image
to be generated, and display the preview(s) using the display 134.
The preview sub-images may be generated using one or more of the
composition rules 218. The user may accept, reject, or modify a
proposed cropped sub-image using a user interface of the imaging
device 102. In some embodiments, in block 332 the imaging device
102 may add annotations to the generated sub-images. For example,
the imaging device 102 may add textual or image labels to the
generated sub-image based on the recognized subjects. In some
embodiments, the image may be annotated for humorous effect, for
example by adding cartoon characters, novelty statements, or the
like.
[0050] In block 334, the imaging device 102 shares the generated
sub-images. Sharing the generated sub-images makes the sub-images
available to a particular set of persons or a particular social
network group. To share the sub-images, the imaging device 102 may
transmit the sub-images to one or more social network servers 104
or to an image repository server 106. The generated sub-images may
be stored on the servers 104, 106 as the cropped images 242.
Alternatively or additionally, the generated sub-images may be
directly transmitted to the end user by the imaging device 102. For
example, the imaging device 102 may transmit the generated
sub-images to one or more computing devices associated with the
recipients by e-mail, instant message, text message, direct file
transfer, or similar technique. In some embodiments, in block 336
the imaging device 102 may share each generated sub-image with the
relevant social network group. For example, the imaging device 102
may share the generated sub-image with the social network group of
persons recognized in the sub-image. As another example, the
imaging device 102 may share the generated sub-image with a social
network group associated with the objects and/or events recognized
in the sub-image. In some embodiments, in block 338 the imaging
device 102 may share the generated sub-images with a user-defined
group of recipients. For example, the user may select an arbitrary
group of contacts with which to share a particular sub-image. After
sharing the sub-images, the method 300 loops back to block 302 to
capture additional images.
[0051] Referring now to FIG. 4, in use, the imaging device 102 may
execute a method 400 for generating sub-images using one or more
composition rules 218. As described above, the imaging device 102
may generate a set of crop inclusion targets and a set of crop
exclusions targets for each social network group relevant to the
digital image. The method 400 begins in block 402, in which the
imaging device 102 retrieves the next excluded subject for the
sub-image from the crop exclusion targets. As described above,
excluded subjects may include recognized persons who are not
associated with the particular social network group, prominent
faces to be excluded from a sub-image focusing on an object or
event, or objects to be excluded based on the content policies
216.
[0052] In block 404, the imaging device 102 reduces the bounding
box to crop the excluded subject. The bounding box is a
rectangular, or other geometrically shaped, region of the digital
image corresponding to the sub-image to be generated. The bounding
box is initially set as the entire digital image and thus includes
all of the crop inclusion targets as well as the crop exclusion
targets. The bounding box is reduced in size to exclude the
excluded subject while still including all of the crop inclusion
targets. In some embodiments, it may not be possible to exclude the
excluded subject while still including all crop inclusion targets
in the bounding box; for example, a person to be excluded may be
surrounded by persons to be included in the sub-image. In those
embodiments, the imaging device 102 may determine that the bounding
box does not exist. Additionally, in some embodiments, the imaging
device 102 may utilize other methodologies for cropping or removing
the excluded subject such as selected image blurring.
[0053] In block 406, the imaging device 102 determines whether
additional subjects to be excluded remain in the crop exclusion
targets. If additional subjects remain, the method 400 loops back
to block 402 to continue adjusting the bounding box. If no
additional subjects remain, the method 400 advances to block 408.
Although described and illustrated as occurring sequentially, in
some embodiments the exclusion list may be processed in
parallel.
[0054] In block 408, the imaging device 102 determines whether a
rule-of-thirds composition rule 218 matches the sub-image, that is,
whether the rule-of-thirds composition rule 218 generates an
acceptable sub-image based on the bounding box and the included
subjects. The so-called "rule of thirds" is a well-known guideline
for composing aesthetically pleasing images. According to the rule
of thirds, images with the subject or subjects positioned at about
one-third of the way along an image dimension tend to be
aesthetically pleasing. For example, referring to FIG. 6C,
illustrative sub-image 606 is composed according to the rule of
thirds. The subjects 504, 506 are positioned about one-third of the
way across a width 608 of the sub-image 606. Referring back to FIG.
4, the imaging device 102 may determine whether the rule of thirds
composition rule 218 generates an acceptable sub-image by
provisionally reducing the size of the bounding box until the rule
of thirds composition rule 218 is satisfied, and then determining
whether the generated sub-image has sufficient resolution, includes
all of the subjects of the crop inclusion list, or other image
criteria. If the bounding box does not exist, as described above,
the imaging device 102 may determine that the rule of thirds
composition policy does not match the sub-image. In some
embodiments, the imaging device 102 may present the provisional
sub-image to the user, for example using the display 134, and allow
the user to determine whether the rule-of-thirds composition rule
218 generates an acceptable sub-image. If the rule-of-thirds
composition rule 218 matches, the method 400 branches to block 410,
in which the imaging device 102 adjusts the bounding box according
to the rule-of-thirds composition rule 218. After adjusting the
bounding box, the method 400 advances to block 418, described
below. Referring back to block 408, if the rule-of-thirds
composition rule 218 does not match, the method 400 advances to
block 412.
[0055] In block 412, the imaging device 102 determines whether a
portrait composition rule 218 matches the sub-image, that is,
whether the portrait composition rule 218 generates an acceptable
sub-image based on the bounding box and the included subjects. The
portrait composition rule 218 specifies that subjects should be
roughly centered in the generated sub-image. For example, referring
to FIGS. 6A and 6B, illustrative sub-images 602, 604 are composed
according to the portrait composition rule 218. In the sub-image
602, the faces 502, 504 are centered about a center 610 of the
sub-image 602. In the sub-image 604, the baseball game 510 is
positioned about the center 610 of the sub-image 604. Referring
back to FIG. 4, the imaging device 102 may determine whether the
portrait composition rule 218 generates an acceptable sub-image by
provisionally reducing the size of the bounding box until the
portrait composition rule 218 is satisfied, and then determining
whether the generated sub-image has sufficient resolution, has an
acceptable image aspect ratio, includes multiple subjects that are
human faces, or other image criteria. If the bounding box does not
exist, as described above, the imaging device 102 may determine
that the portrait composition policy does not match the sub-image.
In some embodiments, the imaging device 102 may present the
provisional sub-image to the user, for example using the display
134, and allow the user to determine whether the portrait
composition rule 218 generates an acceptable sub-image. If the
portrait composition rule 218 matches, the method 400 branches to
block 414, in which the imaging device 102 adjusts the bounding box
according to the portrait composition rule 218. After adjusting the
bounding box, the method 400 advances to block 418, described
below. Referring back to block 412, if the portrait composition
rule 218 does not match, the method 400 advances to block 416.
[0056] In block 416, the imaging device 102 sets the bounding box
to include the smallest bounding box containing all subjects of the
crop inclusion list and obscures any excluded subjects within the
bounding box. The imaging device 102 may obscure excluded subjects
by blurring the excluded subjects or by drawing images over the
excluded subjects such as black bars, cartoon characters, novelty
images, or similar images. In other embodiments, the imaging device
102 may performs any other fallback operation to generate an
acceptable sub-image when none of the other composition rules 218
produce an acceptable sub-image.
[0057] In block 418, in some embodiments the imaging device 102 may
refocus the image to a depth layer corresponding to one or more of
the subjects included in the sub-image. For example, a light field
camera 140 or similar camera array may be capable of refocusing the
image at a particular depth, allowing included subjects to be
rendered in sharp relief while rendering other parts of the image
as out-of-focus. In some embodiments, the imaging device 102 may
algorithmically blur or sharpen portions of the image based on
depth information determined by a depth camera 140.
[0058] In block 420, the imaging device 102 crops the digital image
to generate the sub-image based on the bounding box. The imaging
device 102 may render a new image file for the sub-image, or may
record the bounding box and other parameters required to render the
sub-image. After generating the sub-image for the social network
group, the method 400 is completed. The method 400 may be called
numerous times for other social network groups.
[0059] The illustrative method 400 includes three composition rules
218--the rule of thirds, the portrait rule, and the fallback
rule--that are evaluated in a predetermined order of priority. In
other embodiments, the imaging device 102 may evaluate additional
composition rules 218. For example, the imaging device 102 may
evaluate a half-and-half composition rule 218 to generate a
sub-image with half of the sub-image dedicated to foreground
subjects and half of the sub-image dedicated to background
subjects. Additionally or alternatively, in some embodiments the
priority or applicability of the composition rules 218 may depend
on the social network group of the sub-image and/or the particular
subjects of the sub-image. For example, for a social network group
based on an affinity for sports, the imaging device 102 may prefer
the portrait composition rule 218 for sub-images including a sport
image. As another example, for a sub-image including a number of
persons in the same social network group (e.g. a group portrait),
the imaging device 102 may prefer the portrait composition rule
218.
Examples
[0060] Illustrative examples of the technologies disclosed herein
are provided below. An embodiment of the technologies may include
any one or more, and any combination of, the examples described
below.
[0061] Example 1 includes an imaging device for automated image
cropping, the imaging device comprising an image capture module to
capture a digital image; a subject recognition module to recognize
a first subject represented in the digital image; a social network
module to determine a first social network group to which the first
subject is associated; and an image crop module to crop, based on a
content rule policy, the digital image to generate a sub-image
including the first subject, the composition rule policy comprising
a set of rules that define the composition of the sub-image based
on the recognized first subject.
[0062] Example 2 includes the subject matter of Example 1, and
further including an image sharing module to share the sub-image
with the first social network group.
[0063] Example 3 includes the subject matter of any of Examples 1
and 2, and wherein to recognize the first subject comprises to
detect a position and characteristics of a human face represented
in the digital image; detect a position and an identity of an
object represented in the digital image; or detect an event
represented in the digital image.
[0064] Example 4 includes the subject matter of any of Examples
1-3, and wherein to recognize the first subject comprises to
recognize an event represented in the digital image based on
contextual data received from a contextual sensor of the imaging
device.
[0065] Example 5 includes the subject matter of any of Examples
1-4, and wherein to recognize the event comprises to recognize an
event based on a capture location of the digital image or a capture
date of the digital image.
[0066] Example 6 includes the subject matter of any of Examples
1-5, and wherein to recognize the first subject comprises to
recognize an event represented in the digital image based on
contextual data generated by an application of the imaging
device.
[0067] Example 7 includes the subject matter of any of Examples
1-6, and wherein to recognize the event based on the contextual
data comprises to recognize the event based on ticketing data,
mobile payment data, or calendar data generated by the application
of the imaging device.
[0068] Example 8 includes the subject matter of any of Examples
1-7, and wherein the social network module is further to receive
social network data from a social network server, the social
network data to associate the first subject and the first social
network group; wherein to determine the first social network group
comprises to determine the first social network group based on the
social network data.
[0069] Example 9 includes the subject matter of any of Examples
1-8, and wherein the subject recognition module is further to
recognize a second subject represented in the digital image; and
determine that the second subject is not associated with the first
social network group; wherein to crop the digital image further
comprises to exclude the second subject from the sub-image.
[0070] Example 10 includes the subject matter of any of Examples
1-9, and wherein the social network module is further to determine
a second social network group to which the second subject is
associated, wherein the first subject is not associated with the
second social network group; the image crop module is further to
crop, based on a composition rule policy, the digital image to
generate a second sub-image including the second subject and
excluding the first subject, the composition rule policy comprising
a set of rules that define the composition of the second sub-image
based on the recognized second subject; and the imaging device
further comprises an image sharing module to (i) share the
sub-image with the first social network group and (ii) share the
second sub-image with the second social network group.
[0071] Example 11 includes the subject matter of any of Examples
1-10, and wherein the subject recognition module is further to
recognize a second subject represented in the digital image;
wherein to crop the digital image further comprises to exclude the
second subject from the sub-image as a function of a content policy
stored on the imaging device, the content policy comprising a set
of rules to define whether to include or to exclude the second
subject based on the first social network group.
[0072] Example 12 includes the subject matter of any of Examples
1-11, and wherein to recognize the second subject comprises to
recognize an object represented in the digital image.
[0073] Example 13 includes the subject matter of any of Examples
1-12, and wherein the composition rule policy comprises a
rule-of-thirds composition rule policy; and to crop the digital
image based on the content rule policy comprises to size the
sub-image to position the first subject at a position one-third
along a dimension of the sub-image.
[0074] Example 14 includes the subject matter of any of Examples
1-13, and wherein the composition rule policy comprises a portrait
composition rule policy; and to crop the digital image based on the
composition rule policy comprises to size the sub-image to center
the first subject in the sub-image.
[0075] Example 15 includes the subject matter of any of Examples
1-14, and wherein the subject recognition module is further to
recognize a second subject represented in the digital image; and
the social network module is further to determine that the second
subject is not associated with the first social network group;
wherein to crop the digital image based on the composition rule
policy comprises to determine whether a rule of thirds composition
rule policy generates an acceptable sub-image including the first
subject and excluding the second subject; determine whether a
portrait composition rule policy generates an acceptable sub-image
including the first subject and excluding the second subject; and
apply a fallback composition rule policy in response to a
determination that the rule of thirds composition rule policy does
not generate an acceptable sub-image and a determination that the
portrait composition rule policy does not generate an acceptable
sub-image, wherein to apply the fallback composition rule policy
comprises to crop the digital image to generate the sub-image based
on a smallest bounding box containing the first subject; and
obscure the second subject in the sub-image.
[0076] Example 16 includes the subject matter of any of Examples
1-15, and wherein to obscure the second subject comprises to blur
the second subject or to draw an image over the second subject.
[0077] Example 17 includes the subject matter of any of Examples
1-16, and further including a depth camera, wherein to capture the
digital image comprises to capture depth data associated with the
digital image using the depth camera; and to recognize the first
subject comprises to recognize the first subject using the depth
data associated with the digital image.
[0078] Example 18 includes the subject matter of any of Examples
1-17, and wherein to crop the digital image further comprises to
re-focus the digital image to focus on the first subject.
[0079] Example 19 includes a method for automated image cropping,
the method comprising capturing, by an imaging device, a digital
image; recognizing, by the imaging device, a first subject
represented in the digital image; determining, by the imaging
device, a first social network group to which the first subject is
associated; and cropping, by the imaging device and based on a
content rule policy, the digital image to generate a sub-image
including the first subject, the composition rule policy comprising
a set of rules that define the composition of the sub-image based
on the recognized first subject.
[0080] Example 20 includes the subject matter of Example 19, and
further including sharing, by the imaging device, the sub-image
with the first social network group.
[0081] Example 21 includes the subject matter of any of Examples 19
and 20, and wherein recognizing the first subject comprises
detecting a position and characteristics of a human face
represented in the digital image; detecting a position and an
identity of an object represented in the digital image; or
detecting an event represented in the digital image.
[0082] Example 22 includes the subject matter of any of Examples
19-21, and wherein recognizing the first subject comprises
recognizing an event represented in the digital image based on
contextual data received from a contextual sensor of the imaging
device.
[0083] Example 23 includes the subject matter of any of Examples
19-22, and wherein recognizing the event comprises recognizing an
event based on a capture location of the digital image or a capture
date of the digital image.
[0084] Example 24 includes the subject matter of any of Examples
19-23, and wherein recognizing the first subject comprises
recognizing an event represented in the digital image based on
contextual data generated by an application of the imaging
device.
[0085] Example 25 includes the subject matter of any of Examples
19-24, and wherein recognizing the event based on the contextual
data comprises recognizing the event based on ticketing data,
mobile payment data, or calendar data generated by the application
of the imaging device.
[0086] Example 26 includes the subject matter of any of Examples
19-25, and further including receiving, by the imaging device,
social network data from a social network server, the social
network data to associate the first subject and the first social
network group; wherein determining the first social network group
comprises determining the first social network group based on the
social network data.
[0087] Example 27 includes the subject matter of any of Examples
19-26, and further including recognizing, by the imaging device, a
second subject represented in the digital image; and determining,
by the imaging device, that the second subject is not associated
with the first social network group; wherein cropping the digital
image further comprises excluding the second subject from the
sub-image.
[0088] Example 28 includes the subject matter of any of Examples
19-27, and further including determining, by the imaging device, a
second social network group to which the second subject is
associated, wherein the first subject is not associated with the
second social network group; cropping, by the imaging device and
based on a composition rule policy, the digital image to generate a
second sub-image including the second subject and excluding the
first subject, the composition rule policy comprising a set of
rules that define the composition of the second sub-image based on
the recognized second subject; sharing, by the imaging device, the
sub-image with the first social network group; and sharing, by the
imaging device, the second sub-image with the second social network
group.
[0089] Example 29 includes the subject matter of any of Examples
19-28, and further including recognizing, by the imaging device, a
second subject represented in the digital image; wherein cropping
the digital image further comprises excluding the second subject
from the sub-image as a function of a content policy stored on the
imaging device, the content policy comprising a set of rules to
define whether to include or to exclude the second subject based on
the first social network group.
[0090] Example 30 includes the subject matter of any of Examples
19-29, and wherein recognizing the second subject comprises
recognizing an object represented in the digital image.
[0091] Example 31 includes the subject matter of any of Examples
19-30, and wherein cropping the digital image based on the
composition rule policy comprises applying a rule-of-thirds
composition rule policy, wherein applying the rule-of-thirds
composition rule policy comprises sizing the sub-image to position
the first subject at a position one-third along a dimension of the
sub-image.
[0092] Example 32 includes the subject matter of any of Examples
19-31, and wherein cropping the digital image based on the
composition rule policy comprises applying a portrait composition
rule, wherein applying the portrait composition rule policy
comprises sizing the sub-image to center the first subject in the
sub-image.
[0093] Example 33 includes the subject matter of any of Examples
19-32, and further including recognizing, by the imaging device, a
second subject represented in the digital image; and determining,
by the imaging device, that the second subject is not associated
with the first social network group; wherein cropping the digital
image based on the composition rule policy comprises: determining
whether a rule of thirds composition rule policy generates an
acceptable sub-image including the first subject and excluding the
second subject; determining whether a portrait composition rule
policy generates an acceptable sub-image including the first
subject and excluding the second subject; and applying a fallback
composition rule policy in response to determining that the rule of
thirds composition rule policy does not generate an acceptable
sub-image and determining that the portrait composition rule policy
does not generate an acceptable sub-image, wherein applying the
fallback composition rule policy comprises cropping the digital
image to generate the sub-image based on a smallest bounding box
containing the first subject; and obscuring the second subject in
the sub-image.
[0094] Example 34 includes the subject matter of any of Examples
19-33, and wherein obscuring the second subject comprises blurring
the second subject or drawing an image over the second subject.
[0095] Example 35 includes the subject matter of any of Examples
18-34, and wherein capturing the digital image comprises capturing
depth data associated with the digital image using a depth camera
of the imaging device; and recognizing the first subject comprises
recognizing the first subject using the depth data associated with
the digital image.
[0096] Example 36 includes the subject matter of any of Examples
19-35, and wherein cropping the digital image further comprises
re-focusing the digital image to focus on the first subject.
[0097] Example 37 includes a computing device comprising a
processor; and a memory having stored therein a plurality of
instructions that when executed by the processor cause the
computing device to perform the method of any of Examples
19-36.
[0098] Example 38 includes one or more machine-readable storage
media comprising a plurality of instructions stored thereon that in
response to being executed result in a computing device performing
the method of any of Examples 19-36.
[0099] Example 39 includes a computing device comprising means for
performing the method of any of Examples 19-36.
* * * * *