U.S. patent application number 11/248166 was filed with the patent office on 2006-02-16 for passive touch system and method of detecting user input.
Invention is credited to Gerald Morrison, Manvinder Singh.
Application Number | 20060034486 11/248166 |
Document ID | / |
Family ID | 30444332 |
Filed Date | 2006-02-16 |
United States Patent
Application |
20060034486 |
Kind Code |
A1 |
Morrison; Gerald ; et
al. |
February 16, 2006 |
Passive touch system and method of detecting user input
Abstract
A passive touch system includes a passive touch surface and at
least two cameras associated with the touch surface. The at least
two cameras acquire images of the touch surface from different
locations and have overlapping fields of view. A processor receives
and processes images acquired by the at least two cameras to detect
the existence of a pointer therein and to determine the location of
the pointer relative to the touch surface. Actual pointer contact
with the touch surface and pointer hover above the touch surface
can be determined.
Inventors: |
Morrison; Gerald; (Calgary,
CA) ; Singh; Manvinder; (Calgary, CA) |
Correspondence
Address: |
KATTEN MUCHIN ROSENMAN LLP
525 WEST MONROE STREET
CHICAGO
IL
60661-3693
US
|
Family ID: |
30444332 |
Appl. No.: |
11/248166 |
Filed: |
October 13, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10408671 |
Apr 8, 2003 |
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11248166 |
Oct 13, 2005 |
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09610481 |
Jul 5, 2000 |
6803906 |
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10408671 |
Apr 8, 2003 |
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Current U.S.
Class: |
382/103 ;
345/175 |
Current CPC
Class: |
G06F 3/0425 20130101;
G06F 3/0428 20130101; G06F 3/042 20130101 |
Class at
Publication: |
382/103 ;
345/175 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G09G 5/00 20060101 G09G005/00 |
Claims
1-18. (canceled)
19. A camera based position tracking system comprising: a frame; a
region of interest at least partially within said frame; at least
two cameras associated with said frame, said at least two cameras
acquiring images of said region of interest from different
locations and having overlapping fields of view; a digital signal
processor associated with each camera, the digital signal
processors selecting pixel subsets of images acquired by said at
least two cameras and processing pixel data acquired by the
selected pixel subsets to generate pointer characteristic data when
a pointer exists in said acquired images; and a master digital
signal processor in communication with said digital signal
processors, said master digital signal processor receiving pointer
characteristic data from said digital signal processors and
triangulating the pointer.sub.-characteristic data to determine the
location of said pointer relative to said at least two cameras.
20. A method of tracking an object of interest, the method
comprising: acquiring a first image and a second image representing
different viewpoints of the object of interest; processing the
first image into a first image data set and the second image into a
second image data set; processing the first image data set and the
second image data set to generate a background data set associated
with a background; generating a first difference map by determining
differences between the first image data set and the background
data set and a second difference map by determining differences
between the second image data set and the background data set;
detecting a first relative position of the object of interest in
the first difference map and a second relative position of the
object of interest in the second difference map; and producing an
absolute position of the object of interest from the first and
second relative positions of the object of interest.
21. The method of claim 20 wherein processing the first image into
the first image data set and the second image into the second image
data set includes determining an active image region for each of
the first and second images, and extracting an active image data
set from the first and second images contained within the active
image region.
22. The method of claim 21 wherein extracting the active image data
set includes cropping the first and second images.
23. The method of claim 21 wherein extracting the active image data
set includes rotating the first and second images.
24. The method of claim 21 wherein extracting the active image data
set includes shearing the first and second images.
25. The method of claim 21 wherein extracting the active image data
set includes arranging the active image data set into an image
pixel array having rows and columns.
26. The method of claim 25 wherein extracting the active image data
set further includes: identifying the maximum pixel value within
each column of the image pixel array; and generating data sets
having one row whereby the identified maximum pixel value for each
column represents that column.
27. The method of claim 20 wherein processing the first image into
a first image data set and the second image into a second image
data set includes filtering the first and second images.
28. The method of claim 27 wherein filtering includes extracting
the edges in the first and second images.
29. The method of claim 27 wherein filtering further includes
processing the first image data set and the second image data set
to emphasize differences between the first image data set and the
background data set and to emphasize differences between the second
image data set and the background data set.
30. The method of claim 20 wherein processing the first image data
set and the second image data set to generate the background data
set includes generating a first set of one or more background data
sets associated with the first image data set, and generating a
second set of one or more background data sets associated with the
second image data set.
31. The method of claim 30 wherein generating the first set of
background data sets includes sampling the first image data set,
and generating the second set of background data sets includes
sampling the second image data set.
32. The method of claim 31 wherein generating the first set of one
or more background data sets includes maintaining multiple samples
of the first image data set within each background data set, and
generating the second set of one or more background data sets
includes maintaining multiple samples of the second image data set
within each background data set.
33. The method of claim 32 wherein generating each first background
data set includes selecting from the multiple samples one value
that is representative of the background for each element within
the first image data set, and generating each second background
data set includes selecting from the multiple samples one value
that is representative of the background for each element within
the second image data set.
34. The method of claim 31 wherein sampling occurs automatically at
predefined time intervals, where each sample may include data that
is not associated with the background.
35. The method of claim 20 wherein generating includes comparing
the first image data set to a subset of the background data set,
and comparing the second image data set to a subset of the
background data set.
36. The method of claim 20 wherein generating a first difference
map further includes representing each element in the first image
data set as one of two states, and generating a second difference
map further includes representing each element in the second image
data set as one of two states, where the two states represent
whether the value is consistent with the background.
37. The method of claim 20 wherein detecting includes identifying a
cluster in each of the first and second difference maps, each
cluster having elements whose state within its associated
difference map indicates that the elements are inconsistent with
the background.
38. The method of claim 37 wherein identifying the cluster further
includes reducing the difference map to one row by counting the
elements within a column that are inconsistent with the
background.
39. The method of claim 38 wherein identifying the cluster further
includes identifying the column as being within the cluster and
classifying nearby columns as being within the cluster.
40. The method of claim 39 wherein identifying the column as being
within the cluster includes identifying the median column.
41. The method of claim 37 wherein identifying the cluster further
includes identifying a position associated with the cluster.
42. The method of claim 41 wherein identifying the position
associated with the cluster includes calculating the weighted mean
of elements within the cluster.
43. The method of claim 37 wherein detecting further includes
classifying the cluster as the object of interest.
44. The method of claim 43 wherein classifying the cluster further
includes counting the elements within the cluster and classifying
the cluster as the object of interest only if that count exceeds a
predefined threshold.
45. The method of claim 43 wherein classifying the cluster further
includes counting the elements within the cluster and counting a
total number of elements classified as inconsistent within the
background within the difference map, and classifying the cluster
as the object of interest only if the ratio of the count of
elements within the cluster over the total number of elements
exceeds a predefined threshold.
46. The method of claim 37 wherein detecting further includes
identifying a sub-cluster within the cluster that represents a
pointing end of the object of interest and identifying a position
of the sub-cluster.
47. The method of claim 20 wherein the object of interest is a
user's hand.
48. The method of claim 20 further including controlling an
application program using the absolute position of the object of
interest.
49. The method of claim 20 further comprising: acquiring a third
image and a fourth image representing different viewpoints of the
object of interest; processing the third image into a third image
data set and the fourth image into a fourth image data set;
processing the third image data set and the fourth image data set
to generate the background data set associated with the background;
generating a third difference map by determining differences
between the third image data set and the background data set, and a
fourth difference map by determining differences between the fourth
image data set and the background data set; detecting a third
relative position of the object of interest in the third difference
map and a fourth relative position of the object of interest in the
fourth difference map; and producing the absolute position of the
object of interest from the first, second, third and fourth
relative positions of the object of interest.
50. The method of claim 49 wherein the object of interest is a
user's hand.
51. The method of claim 49 further including controlling an
application program using the absolute position of the object of
interest.
52. A method of tracking an object of interest controlled by a user
to interface with a computer, the method comprising: acquiring
images from at least two viewpoints; processing the acquired images
to produce an image data set for each acquired image; comparing
each image data set to one or more background data sets to produce
a difference map for each acquired image; detecting a relative
position of an object of interest within each difference map;
producing an absolute position of the object of interest from the
relative positions of the object of interest; and using the
absolute position to allow the user to interact with a computer
application.
53. The method of claim 52 further including: mapping the absolute
position of the object of interest to screen coordinates associated
with the computer application; and using the mapped position to
interface with the computer application.
54. The method of claim 52 further including: recognizing a gesture
associated with the object of interest by analyzing changes in the
absolute position of the object of interest; and combining the
absolute position and the gesture to interface with the computer
application.
55. A multiple camera tracking system for interfacing with an
application program running on a computer, the tracking system
comprising: two or more video cameras arranged to provide different
viewpoints of a region of interest and operable to produce a series
of video images; a processor operable to receive the series of
video images and detect objects appearing in the region of
interest, the processor executing a process to: generate a
background data set from the video images; generate an image data
set for each received video image; compare each image data set to
the background data set to produce a difference map for each image
data set; detect a relative position of an object of interest
within each difference map; and produce an absolute position of the
object of interest from the relative positions of the object of
interest and map the absolute position to a position indicator
associated with the application program.
56. The multiple camera tracking system of claim 55 wherein the
object of interest is a human hand.
57. The multiple camera tracking system of claim 55 wherein the
region of interest is defined to be in front of a video display
associated with the computer, and wherein the processor is further
operable to map the absolute position of the object of interest to
the position indicator such that the location of the position
indicator on the video display is aligned with the object of
interest.
58. The multiple camera tracking system of claim 55 wherein the
region of interest is defined to be any distance in front of a
video display associated with the computer, and wherein the
processor is further operable to map the absolute position of the
object of interest to the position indicator such that the location
of the position indicator on the video display is aligned to a
position pointed to by the object of interest.
59. The multiple camera tracking system of claim 55 wherein the
region of interest is defined to be any distance in front of a
video display associated with the computer, and wherein the
processor is further operable to map the absolute position of the
object of interest to the position indicator such that movements of
the object of interest are scaled to larger movements of the
location of the position indicator on the video display.
60. The multiple camera tracking system of claim 55 wherein the
processor is further configured to emulate a computer mouse
function.
61. The multiple camera tracking system of claim 55 wherein the
processor is further configured to emulate controlling buttons of a
computer mouse using gestures derived from the motion of the object
of interest.
62. The multiple camera tracking system of claim 55 wherein a
position of the object of interest triggers a selection action
within the application program.
63. The multiple camera tracking system of claim 55 wherein the
processor is further configured to emulate controlling buttons of a
computer mouse based on a position of the object of interest within
the region of interest.
64. The multiple camera tracking system of claim 55 wherein a
position of the object of interest within the bounds of an
interactive display region triggers a selection action within the
application program.
65. The multiple camera tracking system of claim 55 wherein the
processor is further configured to emulate controlling buttons of a
computer mouse based on a position of the position indicator within
the bounds of an interactive display region.
66. The multiple camera tracking system of claim 55 wherein the
background data set includes data points representing at least a
portion of a stationary structure.
67. The multiple camera tracking system of claim 66 wherein at
least a portion of the stationary structure includes a patterned
surface that is visible to the video cameras.
68. The multiple camera tracking system of claim 66 wherein the
stationary structure is a frame.
69. A multiple camera tracking system for interfacing with an
application program running on a computer, the tracking system
comprising: two or more video cameras arranged to provide different
viewpoints of a region of interest and operable to produce a series
of video images; a processor operable to receive the series of
video images and detect objects appearing in the region of
interest, the processor executing a process to: generate a
background data set from the video images; generate an image data
set for each received video image; compare each image data set to
the background data set to produce a difference map for each image
data set; detect a relative position of an object of interest
within each difference map; produce an absolute position of the
object of interest from the relative positions of the object of
interest; define sub regions within the region of interest;
identify a sub region occupied by the object of interest; associate
an action with the identified sub region that is activated when the
object of interest occupies the identified sub region; and apply
the action to interface with the application program.
70. The multiple camera tracking system of claim 69 wherein the
object of interest is a human hand.
71. The multiple camera tracking system of claim 69 wherein the
action associated with the identified sub region emulates the
activation of keys of a keyboard associated with the application
program.
72. The multiple camera tracking system of claim 69 wherein a
position of the object of interest in any sub region triggers the
action.
Description
FIELD OF THE INVENTION
[0001] The present invention related generally to input devices and
in particular to a passive touch system and method of detecting use
input.
BACKGROUND OF THE INVENTION
[0002] Touch systems are well known in the art and typically
include a touch screen having a touch surface on which contacts are
made using a pointer in order to generate user input. Pointer
contacts wit the touch surface are detected and are used to
generate corresponding output depending on areas of the contact
surface where the contacts are made. There are basically two
general types of touch systems available and they can be broadly
classified as "active" touch systems and "passive" touch
systems.
[0003] Active touch systems allow a user to generate user input by
contacting the touch surface with a special pointer that usually
requires some form of on-board power source, typically batteries.
the special pointer emits signals such as infrared light, visible
light, ultrasonic frequencies, electromagnetic frequencies, etc.
that activate the touch surface.
[0004] Passive touch systems allow a user to generate user input by
contacting the touch surface with a passive pointer and do not
require the use of a special pointer in order to activate the touch
surface. A passive pointer can be a finger, a cylinder of some
material, or any suitable object that can be used to contact some
predetermined area of interest on the touch surface.
[0005] Passive touch systems provide advantages over active touch
systems in that any suitable pointing device, including a user's
finger, can be used as a pointer to contact the touch surface. As a
result, user input can easily be generated. Also, since special
active pointers are not necessary in passive touch systems, battery
power levels and/or pointer damage, theft, or misplacement are of
no concern to users.
[0006] Passive touch systems have a number of applications relating
to computer operation and video display. For example, in one
interactive application, as is disclosed in U.S. Pat. No. 5,448,263
to Martin, assigned to the assignee of the present invention, the
passive touch system is coupled to a computer and the computer
display is projected onto the touch surface of the touch screen.
The coordinates representing specific locations on the touch
surface are mapped to the computer display. When a user contacts
the touch surface, the coordinates of the contact are fed back to
the computer and mapped to the computer display thereby allowing
the user to operate the computer in a manner similar to using a
computer mouse simply by contacting the touch surface. Furthermore,
the coordinates fed back to the computer can be recorded in an
application and redisplayed at a later time. Recording contact
coordinates is typically done when it is desired to record
information written or drawn on the touch surface by the user.
[0007] The resolution of a passive touch screen determines if the
touch system is suitable for recording information written or drawn
on the touch screen or only useful for selecting areas on the touch
screen mapped to large regions on the computer or video display in
order to manipulate the computer or video display. Resolution is
typically measured in dots per inch (DPI). The DPI is related to
the size of the touch screen and the sampling ability of the touch
system hardware and software used to detect contacts on the touch
surface.
[0008] Low-resolution passive touch screens only have enough DPI to
detect contacts on the touch surface within a large group of pixels
displayed by the computer or video system. Therefore, these
low-resolution passive touch screens are useful only for
manipulating the computer or video display.
[0009] On the other hand, high-resolution passive touch screens
have sufficient DPI to detect contacts that are proportional to a
small number of pixels or sub-pixels of the computer or video
display. However, a requirement for high-resolution touch screens
is the ability to detect when the pointer is in contact with the
touch surface. This is necessary for writing, drawing, mouse-click
operations, etc. Without the ability to detect pointer contact with
the touch screen, writing and drawing would be one continuos
operation, and mouse clicks would not be possible thereby making
computer display manipulation impossible. A secondary requirement
is the ability to detect when the pointer is "hovering" above the
touch surface. Although not required for writing or drawing,
today's computer operating systems are increasingly using hover
information to manipulate computer or video displays or pop-up
information boxes.
[0010] Passive touch screens are typically either of the analog
resistive type, Surface Acoustic Wave (SAW) type or capacitive
type. Unfortunately, these touch screens suffer from a number of
problems or shortcomings as will be described.
[0011] Analog resistive touch screens typically have a
high-resolution.
[0012] Depending on the complexity of the touch system, the
resolution of the touch screen can produce 4096.times.4096 DPI or
higher. Analog resistive touch screens are constructed using two
flexible sheets that are coated with a resistive material and
arranged as a sandwich. The sheets do not come into contact with
each other until a contact has been made. The sheets are typically
kept separated by insulating microdots or by an insulating air
space. The sheets are constructed from ITO, which is mostly
transparent. Thus, the touch screen introduces some image
distortion but very little parallax.
[0013] During operation of an analog resistive passive touch
screen, a uniform voltage gradient is applied in one direction
along a first of the sheets. The second sheet measures the voltage
along the first sheet when the two sheets contact one another as a
result of a contact made on the touch surface. Since the voltage
gradient of the first sheet can be translated to the distance along
the first sheet, the measured voltage is proportional to the
position of the contact on the touch surface. When a contact
coordinate on the first sheet is acquired, the uniform voltage
gradient is then applied to the second sheet and the first sheet
measures the voltage along the second sheet. The voltage gradient
of the second sheet is proportional to the distance along the
second sheet. These two contact coordinates represent the X-Y
position of the contact on the touch surface in a Cartesian
coordinate system.
[0014] Since mechanical pressure is required to bring both sheets
into contact, analog resistive touch screens can only detect
contact when there is sufficient pressure to bring the two sheets
together. Analog resistive passive touch screens cannot sense when
a pointer is hovering over the touch surface. Therefore, contact
events and positions can only be detected when actual contacts are
made with the touch surface.
[0015] Surface Acoustic Wave (SAW) touch screens typically provide
for medium resolution and are not suitable for recording good
quality writing. SAW touch screens employ transducers on the
borders of a glass surface to vibrate the glass and produce
acoustic waves that ripple over the glass surface. When a contact
is made on the glass surface, the waves reflect back and the
contact position is determined from the signature of the reflected
waves.
[0016] Unfortunately, SAW touch screens exhibit noticeable parallax
due to the thickness of the vibrating glass which is placed over
the surface of the video or computer display. Also, contact events
and positions can only be detected when actual contacts are made
with the glass surface. Furthermore, SAW touch screens do not scale
beyond a few feet diagonal.
[0017] Capacitive touch screens provide for low resolution because
contacts can only be determined in large areas (approximately
1/2''.times.1/2''). As a result, capacitive touch screens cannot be
used for recording writing or drawing and are suitable for
selecting areas on the touch screen corresponding to computer
generated buttons displayed on the video or computer display. These
touch screens also suffer disadvantages in that they are sensitive
to temperature and humidity. Similar to analog resistive touch
screens and SAW touch screens, capacitive touch screens can also
only detect contact events and positions when actual contacts are
made with the touch surface.
[0018] Scalability of passive touch screens is important since the
demand for larger electronic digitizers is increasing. Where
digitizers were once small desktop appliances, today they have
found there way onto electronic whiteboarding applications. The
need to build a passive touch sensitive "wall" has become a
requirement for new touch screen applications. Existing passive
touch screens of the types discussed above are all limited in the
maximum size where they are still functional.
[0019] As will be appreciated, improvements to passive touch
systems are desired. It is therefore an object of the present
invention to provide a novel passive touch system and method of
detecting user input.
SUMMARY OF THE INVENTION
[0020] According to one aspect of the present invention there is
provided a passive touch system comprising:
[0021] a passive touch surface;
[0022] at least two cameras associated with said touch surface,
said at least two cameras acquiring images of said touch surface
from different locations and having overlapping fields of view;
and
[0023] a processor receiving and processing images acquired by said
at least two cameras to detect the existence of a pointer therein
and to determine the location of said pointer relative to said
touch surface.
[0024] In a preferred embodiment, the at least two cameras are
two-dimensional image sensor and lens assemblies having fields of
view looking along the plane of the touch surface. The processor
determines the location of the pointer relative to the touch screen
using triangulation. The processor also determines when the pointer
is in contact with the touch surface and when the pointer is
hovering above the touch surface.
[0025] In one embodiment, the processor selects pixel subsets of
images acquired by the image sensor and lens assemblies and
processes the pixel subsets to determine the existence of the
pointer. The processor includes a digital signal processor
associated with each image sensor and lens assembly and a master
digital signal processor in communication with the digital signal
processors. The digital signal processors associated with each
image sensor and lens assembly select the pixel subsets and process
the pixel subsets to determine the existence of the pointer. The
master digital signal processor receives pixel characteristic data
from the digital signal processors and triangulates the pixel
characteristic data to determine the location of the pointer
relative to the touch surface.
[0026] According to another aspect of the present invention there
is provided a method of detecting the position of a pointer
relative to a passive touch surface comprising the steps of:
[0027] acquiring images of said touch surface from different
locations using cameras having overlapping fields of view; and
[0028] processing said images to detect the existence of a pointer
therein and to determine the location of said pointer relative to
said touch surface.
[0029] The present invention provides advantages in that the
passive touch system is of high resolution and allows actual
pointer contacts with the touch surface as well as pointer hovers
above the touch surface to be detected and corresponding output
generated. Also, the present passive touch system provides
advantages in that it does not suffer from parallax, image
distortion, pointer position restrictions, image projection and
scalability problems that are associated with prior art passive
touch systems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] An embodiment of the present invention will now be described
more fully with reference to the accompanying drawings in
which:
[0031] FIG. 1 is a schematic diagram of a passive touch system in
accordance with the present invention;
[0032] FIG. 2 is an isometric view of a touch screen forming part
of the passive touch system of FIG. 1;
[0033] FIG. 3 is an isometric view of a corner portion of the touch
screen of FIG. 2;
[0034] FIG. 4 is a schematic diagram of a camera assembly forming
part of the touch screen of FIG. 2;
[0035] FIG. 5 is a front elevational view of the touch screen of
FIG. 2 showing the fields of view of two camera assemblies;
[0036] FIG. 6 is a schematic diagram of a master controller forming
part of the passive touch system of FIG. 1;
[0037] FIG. 7 is case diagram showing functions executed by the
camera assemblies;
[0038] FIG. 8 is a flowchart showing the steps performed during
execution of a findPointerMotion( ) function;
[0039] FIG. 9 is a flowchart showing the steps performed during
execution of an autoSelectThres( ) function;
[0040] FIG. 10 is a flowchart showing the steps performed during
execution of an extractPointer function;
[0041] FIG. 11 is a flowchart showing the steps performed during
execution of a centerOfMass( ) function;
[0042] FIG. 12 is a flowchart showing the steps performed during
execution of a processROI( ) function;
[0043] FIG. 13 is a flowchart showing the steps performed during
execution of a getHighestRegion( ) function;
[0044] FIG. 14 shows an acquired image and a pixel subset of the
image that is processed;
[0045] FIG. 15 shows a region of interest within the pixel
subset;
[0046] FIG. 16 shows the triangulation geometry used to calculate a
pointer contact position on the touch surface of the touch
screen;
[0047] FIG. 17 shows an image acquired by an image sensor and lens
assembly including the pointer and its median line; and
[0048] FIG. 18 shows pointer contact and pointer hover for
different orientations of the pointer.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0049] Turning now to FIG. 1, a passive touch system in accordance
with the present invention is shown and is generally indicated to
by reference numeral 50. As can be seen, passive touch system 50
includes a touch screen 52 coupled to a master controller 54.
Master controller 54 is also coupled to a computer 56. Computer 56
executes one or more application programs and provides display
output that is projected onto the touch screen 52 via a projector
58. The touch screen 52, master controller 54, computer 56 and
projector 58 form a closed-loop so that user contacts with the
touch screen 52 can be recorded as writing or drawing or used to
control execution of application programs executed by the computer
56.
[0050] FIGS. 2 to 4 better illustrate the touch screen 52. Touch
screen 52 includes a touch surface 60 bordered by a frame 62. Touch
surface 60 is passive and is in the form of a rectangular planar
sheet of material. Camera subsystems are associated with each
corner of the touch screen 52. Each camera subsystem includes a
camera assembly 63 mounted adjacent a different corner of the touch
screen 52 by a frame assembly 64. Each frame assembly 64 includes
an angled support plate 66 on which the camera assembly 63 is
mounted. Supporting frame elements 70 and 72 are mounted on the
plate 66 by posts 74 and secure the plate 66 to the frame 62.
[0051] Each camera assembly 63, in this embodiment, includes a
camera in the form of a two-dimensional CMOS camera image sensor
and associated lens assembly 80, a first-in-first-out (FIFO) buffer
82 coupled to the image sensor and lens assembly 80 by a data bus
and a digital signal processor (DSP) 84 coupled to the FIFO 82 by a
data bus and to the image sensor and lens assembly 80 by a control
bus. A boot EPROM 86 and a power supply subsystem 88 are also
included.
[0052] In the present embodiment, the CMOS camera image sensor is a
Photobit PB300 image sensor configured for a 20.times.640 pixel
subarray that can be operated to capture image frames at rates in
excess of 200 frames per second. The FIFO buffer 82 is manufactured
by Cypress under part number CY7C4211V and the DSP 84 is
manufactured by Analog Devices under part number ADSP2185M.
[0053] The DSP 84 provides control information to the image sensor
and lens assembly 80 via the control bus. The control information
allows the DSP 84 to control parameters of the image sensor and
lens assembly 80 such as exposure, gain, array configuration, reset
and initialization. The DSP 84 also provides clock signals to the
image sensor and lens assembly 80 to control the frame rate of the
image sensor and lens assembly 80.
[0054] As shown in FIG. 5, each image sensor and lens assembly 80
has a 55.degree. field of view. The angle of the plate 66 is
selected so that the field of view of each image and lens assembly
80 includes at least the majority of a different peripheral edge of
the touch surface 60. In this way, the entire touch surface 60 is
within the fields of view of the image sensor and lens assemblies
80.
[0055] Master controller 54 is best illustrated in FIG. 6 and
includes a DSP 90, a boot EPROM 92, a serial line driver 94 and a
power supply subsystem 95. The DSP 90 communicates with the DSPs 84
of the camera assemblies 63 over a data bus through a serial port
96 and communicates with the computer 56 over a data bus through a
serial port 98 and the serial line driver 94. In this embodiment,
the DSP 90 is also manufactured by Analog Devices under part number
ADSP2185M. The serial line driver 94 is manufactured by Analog
Devices under part number ADM222.
[0056] The master controller 54 and each camera assembly 63 follow
a communication protocol that enables bi-directional communications
via a common serial cable similar to a universal serial bus (USB).
The transmission bandwidth is divided into thirty-two (32) 16-bit
channels. Of the thirty-two channels, five (5) channels are
assigned to each of the DSPs 84 in the camera assemblies 63 and to
the DSP 90 in the master controller 54 and the remaining seven (7)
channels are unused. The master controller 54 monitors the twenty
(20) channels assigned to the camera assembly DSPs 84 while the
DSPs 84 in the camera subsystems 63 monitor the five (5) channels
assigned to the master controller DSP 90. Communications between
the master controller 54 and the camera assemblies 63 are performed
as background processes in response to interrupts.
[0057] The general operation of the passive touch system 50 will
now be described. Each camera assembly 63 acquires images of the
touch surface 60 within the field of view of its image sensor and
lens assembly 80 at the frame rate established by the DSP clock
signals and processes the images to determine if a pointer is in
the acquired images. If a pointer is in the acquired images, the
images are further processed to determine characteristics of the
pointer contacting or hovering above the touch surface 60. Pointer
characteristics are then converted into pointer information packets
(PIPs) and the PIPs are queued for transmission to the master
controller 54.
[0058] The camera assemblies 63 also receive and respond to
diagnostic PIPs generated by the master controller 54.
[0059] The master controller 54 polls the camera assemblies 63 at a
set frequency (in this embodiment 70 times per second) for PIPs and
triangulates pointer characteristics in the PIPs to determine
pointer position data. The master controller 54 in turn transmits
pointer position data and/or status information to the personal
computer 56. In this manner, the pointer position data transmitted
to the personal computer 56 can be recorded as writing or drawing
or can be used to control execution of application programs
executed by the computer 56. The computer 56 also updates the
display output conveyed to the projector 58 so that information
projected onto the touch surface 60 reflects the pointer
activity.
[0060] The master controller 54 also receives commands from the
personal computer 56 and responds accordingly as well as generates
and conveys diagnostic PIPs to the camera assemblies 63.
[0061] Specifics concerning the processing of acquired images and
the triangulation of pointer characteristics in PIPs will now be
described with particular reference to FIGS. 7 to 13.
[0062] Initially, an alignment routine is performed to align the
image sensor and lens assemblies 80. During the alignment routine,
a pointer is held in the approximate center of the touch surface
60. Subsets of the pixels of the image sensor and lens assemblies
80 are then selected until a subset of pixels for each image sensor
and lens assembly 80 is found that captures the pointer and the
pointer tip on the touch surface 60. This alignment routine allows
for a relaxation in mechanical mounting of the image sensor and
lens assemblies on the frame assemblies 64. The identification of
the pointer tip on the touch surface 60 also gives a calibration
that determines the row of pixels of each image sensor and lens
assembly 80 that detects actual contacts made with the touch
surface. Knowing these pixel rows allows the difference between
pointer hover and pointer contact to be determined.
[0063] In this embodiment, since a computer display is projected
onto the touch surface 60, during the alignment routine several
known coordinate locations are also displayed and the user is
required to touch these coordinate locations in sequence using the
pointer so that the subset of pixels for each of image sensor and
lens assembly 80 includes all of the coordinate locations as well.
Calibration data is then stored for reference so that pointer
contacts on the touch surface 60 can be mapped to corresponding
areas on the computer display.
[0064] As mentioned above, each camera assembly 63 acquires images
of the touch surface 60 within its field of view. The images are
acquired by the image and lens assembly 80 at intervals in response
to the clock signals received from the DSP 84. Each image acquired
by the image and lens assembly 80 is sent to the FIFO buffer
82.
[0065] The DSP 84 in turn reads each image from the FIFO buffer 82
and processes the image to determine if a pointer is located in the
image and if so, to extract the pointer and related pointer
statistical information. To avoid processing significant numbers of
pixels containing no useful information, only the subset of the
pixels in the image determined during the alignment routine are
actually processed as is shown in FIG. 14.
[0066] In order to determine if a pointer is located in the image
and extract pointer and related pointer statistical information,
the DSP 84 executes a main findPointerMotion( ) function 120 that
calls a number of other functions, namely an autoSelectThres( )
function 122, an extractPointer function 124, a centerOfMass( )
function 126, and a processROI( ) function 128 (see FIG. 7). The
extractPointer( ) function 128 also calls a getHighestRegion( )
function 130.
[0067] The findPointerMotion( ) function 120 is used to extract the
pointer from the image. Turning now to FIG. 8, the steps performed
during execution of the findPointerMotion( ) function 120 is shown.
When the findPointerMotion( ) function is called, a check is made
to determine if a previous image iPrev including a pointer exists
(step 150). If no previous image iPrev exists, center of mass
parameters Cx and Cz are assigned zero values (step 152). The
current image iCurr being processed is then designated as the
previous image iPrev (step 154) to complete the findPointerMotion(
) function.
[0068] At step 150, if a previous image iPrev exists, the current
image iCurr is subtracted from the previous image iPrev and the
absolute value of the difference image iDiff is taken (step 156).
By forming the difference image iDiff, background features and
noise are removed. The autoSelectThres( ) function 122 is then
called to select a threshold value tValue for the difference image
iDiff (step 158) based on the histogram of the difference image
iDiff. The threshold iThres of the difference image iDiff is then
taken (step 160) to highlight further the pointer within the
current image iCurr. During thresholding a grayscale image is
mapped to the binary difference image iDiff. Pixels in the
difference image with values equal to or less than the threshold
value tValue are made black while all other pixels are made white.
The result is a binary image containing the pointer and some noise
both designated by white pixels.
[0069] Once the difference image has been thresholded, the
extractPointer function 124 is called (step 162) to extract the
pointer ptr from the difference image iDiff and ignore the noise.
The size of the pointer ptr is then examined to determine if it is
greater than a threshold value MIN_PTR_SIZE (step 164).
[0070] If the size of the pointer is greater than the threshold
value MIN_PTR_SIZE, the centerOfMass( ) function 126 is called
(step 166) to determine the center of the pointer. Following this,
the processROI( ) function 128 is called (step 168) to select a
region of interest ROI within the difference image iDiff and
extract the pointer from the region of interest.
[0071] Once the pointer has been extracted from the region of
interest ROI, the size of the extracted pointer is determined (step
170). If the pointer size is greater than zero, a medianLine
function is called (step 172). During execution of the medianLine
function, the median line of the pointer (i.e. the pointer location
within the region of interest) is calculated using linear least
squares. The current image iCurr is then designated as the previous
image iPrev to complete the findPointerMotion( ) function.
[0072] At step 164, if the pointer size is equal to zero, the
center of mass parameters Cx and Cz are examined (step 174). If
both of the center of mass parameters Cx and Cz have values greater
zero, the processROI( ) function 128 is called (step 168) to select
a region of interest ROI within the difference image iDiff and
extract the pointer from the region of interest. At step 174, if
one or both of the center of mass parameters Cx and Cz have values
equal to zero or at step 170, if the size of the pointer is less
than zero, a no pointer found condition is determined (step 176).
At this stage, the current image iCurr is designated as a
calibration image glRef. The findPointerMotion( ) function then
proceeds to step 152 where the center of mass parameters Cx and Cz
are assigned zero values.
[0073] As mentioned above, at step 158, when the findPointerMotion(
) function 120 calls the autoSelectThres( ) function 122, a
threshold value for the difference image iDiff is selected based on
the histogram of the difference image so that when the difference
image iDiff is thresholded, the pointer is further highlighted from
background features and noise. Selection of the threshold value in
this manner is more robust than hardcoding the threshold value.
[0074] Turning now to FIG. 9, the steps performed during execution
of the autoSelectThres( ) function 122 are illustrated. As can be
seen, in order to select the threshold level, a histogram of the
difference image iDiff is taken and the number of pixels in each
bin of the histogram are counted (step 200). The number of pixels
in the bin having the highest count is used as a peak parameter and
the threshold value is initially assigned a value of one (step
202). The number of bins having non-zero counts is then examined to
determine if more than eight (8) bins have non-zero counts (step
204). If less than eight (8) bins have non-zero counts, the
threshold value remains at its initially assigned value and the
autoSelectThres( ) function is completed.
[0075] At step 204, if more than eight (8) bins have non-zero
counts, the number of non-zero bins is checked again to determine
if an entire difference image is being processed (i.e. the
autoSelectThres( ) function was called by the findPointerMotion( )
function 120) or if a region of interest ROI within the difference
image is being processed (i.e. the autoSelecThres( ) function was
called by the processROI( ) function 128) (step 206). If the entire
difference image iDiff is being processed, a threshold minimum
parameter (tMin) is set to a value of twelve (12) and a Peak_Div
parameter is set to a value of eight (8) (step 208). A minimum
count parameter minCount is then calculated by dividing the peak
parameter determined at step 202 by the Peak_Div parameter (step
210). If a region of interest is being processed, the threshold
minimum parameter (tMin) is set to a value of forty (40) and the
Peak_Div parameter is set to a value of thirty-two (32) (step 212)
before proceeding to step 210.
[0076] Once minCount has been determined, the peak level is checked
to determine if it is greater than the threshold minimum tmin (step
214). Peak level is the grayscale level that contains the most
pixels. In the case of a tie, the grayscale level with the highest
numerical value (i.e. the closest to 255) is chosen. If the peak
level is greater than the threshold minimum tMin, a startLevel
parameter is assigned a value equal to the peak level+1 (step 216).
At step 214, if the peak level is less than the threshold minimum
tMin, the startLevel parameter is assigned a value equal to the
threshold minimum tMin (step 218).
[0077] Following step 216 or 218, a loop is entered. During the
loop, the levCount for each bin having a bin number between the
value of the startLevel parameter and two hundred and fifty-five
(255) is examined to determine if it is greater than zero and if it
is less than the minCount parameter determined at step 210 (step
220). If the condition is met during the loop, the loop is exited
and the threshold value is assigned a value equal to the bin number
having the levCount that resulted in the loop being exited+1 (step
222). If the condition is not met, the loop is exited after the
levCount for bin number 255 has been checked.
[0078] Once the loop has been exited, the threshold value is
checked to determine if it is less than the minimum threshold value
tMin (step 224). If not, a check is again made to determine if an
entire difference image is being processed or whether a region of
interest ROI is being processed (step 226). If the threshold value
is less than the minimum threshold value tMin, the threshold value
is set equal to the minimum threshold value tMin (step 228) before
the check is made to determine if an entire difference image is
being processed or whether a region of interest is being processed
(step 226).
[0079] At step 226, if a difference image iDiff is being processed,
the autoSelectThres( ) function is completed. However, if a region
of interest is being processed, a parameter p is assigned a value
corresponding to the first grayscale level at which 90% or more of
the pixels will go black (step 230). The parameter p is then
compared to the threshold level (step 232). If the parameter p is
less than the threshold level, the autoSelectThres( ) function is
completed. If the parameter p is greater than the threshold level,
the threshold value is set to the value of parameter p (step 234)
and the autoSelectThres( ) function is completed.
[0080] As mentioned above, at step 162 the findPointerMotion( )
function 120 calls the extractPointer( ) function 124 to extract
the pointer from the binary image and ignore the noise. This is
done by selecting the "white region" in the binary image that is
greater than or equal to a certain minimum size and is the highest
region (i.e. the largest in the y-axis (20 pixel axis)).
Specifically, when the extractPointer( ) function 124 is called,
the extractPointer( ) function calls the getHighestRegion( )
function 130 (step 250). The getHighestRegion( ) function 130 uses
the threshold value and tol parameters to select the appropriate
white region szRegion in the thresholded difference image. The tol
parameter is used to avoid situations where board surface noise is
mistaken as a pointer. FIG. 13 shows the steps performed during
this function.
[0081] Once the white region szRegion has been selected, the white
region szRegion is checked to see if it is greater than zero (step
252). If not, a no pointer condition is determined (step 254). If
the white region szRegion is greater than zero, morphological
operator of erosion and dilation are used on the white region to
reduce further noise (steps 256 to 264) and the extractPointer( )
function is completed.
[0082] As mentioned above, at step 166 the findPointerMotion( )
function 120 calls the centerOfMass( ) function 126 to determine
the center of the pointer. During this function, the black pixels
in the binary image are treated as having a mass of zero (0) and
the white pixel are treated as having a mass of one (1). The
physics formulae for center-of-mass are used. The equation below
gives the center of mass in the x-direction:
C.sub.x=sum((X.sub.i)/M where:
[0083] X.sub.i are the x-coordinates of the white pixels in the
binary image; and
[0084] M is the number of white pixels in the binary image.
[0085] Initially, once the centerOfMass( ) function is executed,
the center of mass parameters massX, massZ and a mass parameter are
assigned zero values (see step 300 in FIG. 11). A loop is then
entered to calculate the center of mass parameters massX and massZ
using the above equation and to calculate the mass parameter (step
302).
[0086] Upon exiting the loop, the mass parameter is checked to
determine if its value is greater than zero (step 304). If the
value of the mass parameter is equal to zero, the center of mass
parameters Cx and Cz are assigned values of zero (step 306) and the
centerOfMass( ) function 126 is completed. At step 304, if the
value of the mass parameter is greater than zero, the center of
mass coordinates Cx and Cz are calculated (step 308) using the
equations: Cx=massX/mass; and Cz=massZ/mass. Once the center of
mass coordinates have been calculated, the centerOfMass( ) function
126 is completed.
[0087] As mentioned above, at step 168 the findPointerMotion( )
function 120 calls the processROI( ) function 128 to process the
region-of-interest in a manner similar to the findPointerMotion( )
function 120 except, here the image size is 100.times.20 pixels and
a calibration image including only background (i.e. no pointer) is
used in place of the previous image. Upon execution of the
processROI( ) function 128, xLeft and xRight parameters are
calculated by subtracting and adding fifty (50) to the center of
mass parameter Cx (step 350). The value of parameter xLeft is then
checked to determine if it is less than one (1) (step 352). If the
parameter xLeft has a value less than one (1), the parameter xRight
is recalculated and the parameter xLeft is assigned a value of one
(1) (step 354) to define boundaries of the region of interest as
shown in FIG. 15. A difference image iDiff of the region of
interest is then calculated by subtracting the region of interest
of the current image from the region of interest of the calibration
image glRef determined at step 176 of the findPointerMotion( )
function 120 and taking the absolute value of the difference (step
356).
[0088] At step 352, if the parameter xLeft has a value greater than
one (1), the parameter xRight is checked to determine if it has a
value greater than 640 (step 358). If the parameter xRight has a
value greater than 640, the parameter xLeft is recalculated and the
parameter xRight is assigned a value of one (1) (step 360) to
define boundaries of the region of interest. The processROI( )
function 128 then proceeds to step 356 to calculate the difference
image iDiff of the region of interest. At step 358, if the
parameter xRight has a value less than 640, the processROI( )
function 128 proceeds directly to step 356 to calculate the
difference image iDiff of the region of interest.
[0089] Once the difference image iDiff of the region of interest
has been calculated, the autoSelectThres( ) function 122 is called
to select a threshold value for the difference image iDiff of the
region of interest (step 362) in the manner described above with
reference to FIG. 9. The difference image iDiff of the region of
interest is then thresholded (step 364). Following this, the
extractPointer( ) function 124 is called to extract the pointer
from the difference image iDiff of the region of interest (step
366) in the manner described above with reference to FIG. 10.
[0090] Once the acquired image has been processed in the above
manner, a PIP for the acquired image is created by the DSP 84. The
PIP is a five (5) word packet and has a layout including camera
identification, an LRC checksum to ensure data integrity and a
valid tag to ensure zero packets are not valid. The valid tag
indicates whether the PIP relates to a pointer characteristic
packet (10), a diagnostic packet for a specific camera assembly 63
(01) or a diagnostic packet for all camera assemblies 63 (11).
Table 1 below shows the PIP layout. TABLE-US-00001 TABLE 1 Word 15
14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 Pointer Characteristics packet
(generated by Camera) 0 Valid tag Camera # X intercept (at Y0) 1
Frame rate intensity/color 2 Packet # pointer area 3 Unused X
intercept (at Y19) 4 Unused Z position LRC checksum Diagnostic
Packet (generated by Camera or Master) 0 Valid tag Camera # 1 2
Packet # 3 4 LRC checksum
[0091] As mentioned above, each camera assembly 63 acquires and
processes an image in the manner described above in response to
each clock signal generated by its DSP 84. The PIPs created by the
DSPs 84 are only sent to the master controller 54 when the camera
assemblies 63 are polled by the master controller. The DSPs 84
create PIPs faster than the master controller 54 polls the camera
assemblies 63. PIPs that are not sent to the master controller 54
are overwritten.
[0092] When the master controller 54 polls the camera assemblies
63, frame sync pulses are sent to the camera assemblies 63 to
initiate transmission of the PIPs created by the DSPs 84. Upon
receipt of a frame sync pulse, each DSP 84 transmits the PIP to the
master controller 54 over the data bus. The PIPs transmitted to the
master controller 54 are received via the serial port 96 and
auto-buffered into the DSP 90.
[0093] After the DSP 90 has polled the camera assemblies 63 and has
received PIPs from each of the camera assemblies, the DSP 90
processes the PIPs using triangulation to determine the location of
the pointer relative to the touch surface 60 in (x,y) coordinates.
Specifically, the PIPs from pairs of camera assemblies 63 are
processed using triangulation.
[0094] FIG. 16 shows that two angles .phi..sub.1 and .phi..sub.2
are needed to triangulate the position (x.sub.0,y.sub.0) of the
pointer relative to the touch screen 60. The PIPs generated by each
camera assembly 63 include a number
.theta..epsilon.[0,sensorResolution-1] (see FIG. 17) identifying
the median line of the pointer. The sensorResolution, in the case
of the Photobit PB300 image sensor, is 640. The equations below
relate the angle .phi. to the position .theta. taking into account
the field-of-view of the image sensor and lens assembly 80: .PHI. =
.theta. sensorResolution .times. F ov - .delta. ( 1 ) .PHI. =
SensorResolution - .theta. sensorResolution .times. F ov - .delta.
( 2 ) ##EQU1##
[0095] The above equations subtract away an angle .delta. that
allows the image sensor and lens assembly 80 to have some overlap
with the frame 62. The overlap with the frame 62 is desired due to
mechanical tolerance issues in the frame assemblies 64 (i.e. the
angle of the plate 66 can have an error of 1.degree. to 2.degree.).
The angle .delta. is allowed to be negative, meaning that there is
no overlap with the frame 62, in fact part of the touch surface 60
along the frame 62 is missed. Equation 1 or 2 is used to determine
.phi., depending on the mounting and/or optical properties of the
image sensor and lens assembly 80. If the image acquired by the
camera assembly 63 is rotated as a result of the mounting and/or
optical properties of the image sensor and lens assembly 80, then
equation 2 is used. Equation 1 is used otherwise. In the present
embodiment, equation 1 is used with the camera assemblies 63
positioned at the top left and bottom right corners of the touch
screen 52 and equation 2 is used with the camera assemblies 63
positioned at the bottom left and top right corners of the touch
screen 52.
[0096] As discussed above, equations 1 and 2 allow the pointer
median line data included in the PIPs to be converted by the DSP 90
into an angle .phi. with respect to the x-axis. When two such
angles are available, the intersection of the median lines
extending at these angles from their respective camera assemblies
63 yields the location of the pointer relative to the touch surface
60.
[0097] In this embodiment, since the touch screen 52 includes four
camera assemblies 63, six pairs of camera assemblies can be used
for triangulation. The following discussion describes how a pointer
position is determined by triangulation for each pair of the camera
assemblies 63.
[0098] In order to determine a pointer position using the PIPs
received from the camera assemblies 63 along the left side of the
touch screen 52, the following equations are used to determine the
(x.sub.0,y.sub.0) coordinates of the pointer position given the
angles .phi..sub.0 and .phi..sub.1 for the upper and lower camera
assemblies: x 0 = h w .times. 1 tan .function. ( .PHI. 0 ) + tan
.function. ( .PHI. 1 ) ( 3 ) y 0 = tan .function. ( .PHI. 0 ) tan
.function. ( .PHI. 0 ) + tan .function. ( .PHI. 1 ) ( 4 ) ##EQU2##
where:
[0099] h is the height of the touch screen 52 i.e. the vertical
distance from camera assembly focal point-to-focal point;
[0100] w is the width of the touch screen 52 i.e. the horizontal
distance from camera assembly focal point-to-focal point; and
[0101] .phi..sub.i is the angle with respect to the horizontal,
measured using camera assembly i and equation 1 or 2.
[0102] For the camera assemblies 63 along on the right side of the
touch screen 52, the following equations are used to determine the
(x.sub.0,y.sub.0) coordinates of the pointer position given the
angles .phi..sub.2 and .phi..sub.3 for the upper and lower camera
assemblies: x 0 = 1 - h w .times. 1 tan .function. ( .PHI. 2 ) +
tan .function. ( .PHI. 3 ) ( 5 ) y 0 = 1 - tan .function. ( .PHI. 2
) tan .function. ( .PHI. 2 ) + tan .function. ( .PHI. 3 ) ( 6 )
##EQU3##
[0103] The similarity between equations 3 and 5, i.e. equation
5=1-equation 3 once .phi..sub.2 and .phi..sub.3 have been
substituted into equation 3 for .phi..sub.1 and .phi..sub.2
respectively should be apparent. Equations 4 and 6 are related in a
similar manner.
[0104] In order to determine a pointer position using the camera
assemblies 63 along the bottom of the touch screen 52, the
following equations are used to determine the (x.sub.0,y.sub.0)
coordinates of the pointer position given the angles .phi..sub.0
and .phi..sub.3 for bottom left and bottom right camera assemblies:
x 0 = tan .function. ( .PHI. 3 ) tan .function. ( .PHI. 0 ) + tan
.function. ( .PHI. 3 ) ( 7 ) y 0 = w h .times. tan .function. (
.PHI. 3 ) tan .function. ( .PHI. 0 ) + tan .function. ( .PHI. 3 )
.times. tan .function. ( .PHI. 0 ) = w h .times. x 0 .times. tan
.function. ( .PHI. 0 ) ( 8 ) ##EQU4##
[0105] In order to determine a pointer position using the camera
assemblies 63 along the top of the touch screen 52, the following
equations are used to determine the (x.sub.0,y.sub.0) coordinates
of the pointer position given the angles .phi..sub.1 and
.phi..sub.2 for the top left and top right camera assemblies: x 0 =
tan .function. ( .PHI. 2 ) tan .function. ( .PHI. 1 ) + tan
.function. ( .PHI. 2 ) ( 9 ) y 0 = 1 - w h .times. tan .function. (
.PHI. 2 ) tan .function. ( .PHI. 1 ) + tan .function. ( .PHI. 2 )
.times. tan .function. ( .PHI. 1 ) = 1 - w h .times. x 0 .times.
tan .function. ( .PHI. 1 ) ( 10 ) ##EQU5##
[0106] The similarity between equations 7 and 9, i.e. equation
9=equation 7 once .phi..sub.1 and .phi..sub.2 have been substituted
into equation 7 for .phi..sub.0 and .phi..sub.3 should be apparent.
Equations 8 and 10 have the following relationship: equation
10=1-equation 8 once .phi..sub.1 and .phi..sub.2 have been
substituted into equation 8 for .phi..sub.0 and .phi..sub.3
respectively.
[0107] In order to determine a pointer position using the camera
assemblies 63 across the bottom left to top right corner diagonal,
the following equations are used to determine the (x.sub.0,y.sub.0)
coordinates of the pointer position given the angles .phi..sub.0
and .phi..sub.2 for bottom left and top right camera assemblies: x
0 = h w - tan .function. ( .PHI. 2 ) tan .function. ( .PHI. 0 ) -
tan .function. ( .PHI. 2 ) ( 11 ) y 0 = 1 - w h - tan .function. (
.PHI. 2 ) tan .function. ( .PHI. 0 ) - tan .function. ( .PHI. 2 )
.times. tan .function. ( .PHI. 0 ) ( 12 ) ##EQU6##
[0108] In order to determine a pointer position using the camera
assemblies 63 across the bottom right to top left diagonal, the
following equations are used to determine the (x.sub.0,y.sub.0)
coordinates of the pointer position given the angles .phi..sub.1
and .phi..sub.3 for the bottom right and top left camera
assemblies: x 0 = h w - tan .function. ( .PHI. 3 ) tan .function. (
.PHI. 1 ) - tan .function. ( .PHI. 3 ) ( 13 ) y 0 = 1 - 1 - w h -
tan .function. ( .PHI. 3 ) tan .function. ( .PHI. 1 ) - tan
.function. ( .PHI. 3 ) .times. tan .function. ( .PHI. 1 ) ( 14 )
##EQU7##
[0109] The similarity between equations 11 and 13, i.e. equation
13=equation 11 once .degree.1 and .degree.3 have been substituted
into equation 11 for .phi..sub.0 and .phi..sub.2 should be
apparent. Equations 12 and 14 have the following relationship:
equation 14=1-equation 12 once .phi..sub.1 and .phi..sub.3 have
been substituted into equation 12 for .phi..sub.0 and .phi..sub.2
respectively.
[0110] As will be appreciated, the above equations generate the
coordinates x.sub.0 and y.sub.0 on a scale of [0, 1]. Therefore,
any appropriate coordinate scale can be reported by multiplying
x.sub.0 and y.sub.0 by the maximum X and maximum Y values
respectively.
[0111] In the present embodiment, the DSP 90 calculates the pointer
position using triangulation for each camera pair excluding the
diagonal pairs. The resulting pointer positions are then averaged
and the resulting pointer position coordinates are queued for
transmission to the personal computer 56 via the serial port 98 and
the serial line driver 94. Since the rows of pixels of the image
sensor and lens assemblies 80 that correspond to actual contacts
with the touch surface 60 are known, any Z-position in a PIP that
does not correspond with one of these rows is by definition a
pointer hover event.
[0112] If desired, pointer velocity and angle can be calculated by
the DSP 90 as shown in FIG. 18. The velocity of the pointer is
calculated by examining the changes in the Z-position (or
X-intercept) of the pointer in successive PIPs and knowing the
camera frame rate. For example, if the camera frame rate is 200
frames per second and the Z-position changes by 1 pixel per frame,
the pointer velocity is 200 pixels per second.
[0113] The angle of the pointer can be determined due to the fact
that the PIP pincludes the X-intercept at pixel rows 0 and 19 of
the median line. Since the X distance (the difference between
X-intercepts) and the Y distance (the number of pixel rows) are
known, all of the information necessary to calculate the pointer
angle is available.
[0114] The present invention provides advantages in that the
passive touch system 50 does not suffer parallax and/or image
distortion problems due to the fact that a glass or other
transparent overlay over a computer or video display is not
required. In addition, the present passive touch system 50 allows
both pointer contact and pointer hover over the touch surface 60 to
be detected by using two-dimensional image sensor and lens
assemblies 80 in the plane of the touch surface 60. Pointer contact
with the touch surface 60 is defined only when the pointer is in
very close proximity of the touch surface. The present invention
also provides advantages in that the pointer position with respect
to the touch surface is not restricted since the image sensor and
lens assemblies 80 look along the plane of the touch surface
60.
[0115] With respect to resolution, the resolution of the passive
touch system is a function of the distances of the pointer with
respect to the image sensor and lens assemblies 80, the number of
pixel elements in the image sensor and lens assemblies and the
fields of view of the image sensor and lens assemblies. Since image
sensor and lens assemblies are available with pixel elements that
range in number from tens of thousand to many millions and since
the number of pixel elements in image sensors and lens assemblies
of this nature is only expected to increase, the resolution of the
present passive touch system 50 is high.
[0116] The passive touch system 50 also provides advantages in that
alignment is automatically corrected since only pixel subsets of
images that include the touch surface and the pointer are
processed. In addition, the present passive touch system allows for
very fast acquisition of image data since the image sensor and lens
assemblies can be triggered to capture images at rates exceeding
two hundred frames per second.
[0117] The present passive touch system 50 is scaleable and can
include a touch surface 60 of arbitrary size. When used in
conjunction with a projected computer image, the number of pixels
of the image sensor and lens assemblies should be proportional to
the number of pixels being displayed on the touch surface 60. For
example, if a projected computer image is 1024.times.768 pixels,
the size of the projected image is not be of concern provided the
image sensor and lens assemblies 80 are able to resolve the (x,y)
coordinates with sufficient accuracy with respect to the displayed
pixels.
[0118] Although the passive touch system 50 is shown including
camera assemblies 63 associated with each corner of the touch
screen 52, those of skill in the art will appreciate that only two
camera assemblies are required. In this case, the fields of view of
the image sensor and lens assemblies are preferably selected so
that the entire touch surface 60 is encompassed since the locations
of pointer contacts are determined only when they occur within the
overlapping fields of view of the camera assemblies 63.
[0119] Also, although the passive touch system 50 is described as
including a projector to display the computer display output onto
the touch surface 60, this is not required. No information need be
displayed on the touch surface.
[0120] Although a preferred embodiment of the present invention has
been described, those of skill in the art will appreciate that
variations and modifications may be made without departing from the
spirit and scope thereof as defined by the appended claims.
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