U.S. patent application number 11/102078 was filed with the patent office on 2006-07-20 for card game system with automatic bet recognition.
Invention is credited to Bo U. Curry, Otho D. Hill.
Application Number | 20060160600 11/102078 |
Document ID | / |
Family ID | 36684637 |
Filed Date | 2006-07-20 |
United States Patent
Application |
20060160600 |
Kind Code |
A1 |
Hill; Otho D. ; et
al. |
July 20, 2006 |
Card game system with automatic bet recognition
Abstract
An exemplary embodiment of the invention includes a bet
recognition system for use with a card game. A table is provided
for playing a card game including a bet location for each player.
An image capture device is positioned in proximity to the bet
location and configured to capture an image of a player's bet. An
image processor is coupled to the image capture device and
configured to process the image by locating at least one chip and
creating a signal representing the chip, comparing the signal to a
plurality of stored signatures, and when a match occurs, generating
a signal representing the bet. In one aspect, the image processor
is configured to generate an error signal when unable to match a
candidate signature with at least one of the stored signatures.
Inventors: |
Hill; Otho D.; (Las Vegas,
NV) ; Curry; Bo U.; (Redwood City, CA) |
Correspondence
Address: |
IPSG, P.C.
P.O. BOX 700640
SAN JOSE
CA
95170-0640
US
|
Family ID: |
36684637 |
Appl. No.: |
11/102078 |
Filed: |
April 8, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60644208 |
Jan 14, 2005 |
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Current U.S.
Class: |
463/17 |
Current CPC
Class: |
G07F 17/32 20130101;
G07F 17/3293 20130101 |
Class at
Publication: |
463/017 |
International
Class: |
A63F 9/24 20060101
A63F009/24 |
Claims
1. A card delivery and bet recognition system comprising: a housing
configured to store a plurality of playing cards and configured for
dispensing cards to a number of players; a scanner configured to
selectively scan the cards in the housing and to generate a scanner
signal representative of the identity of each scanned card; at
least one camera positioned to view the bet location for each
player and generate a bet signal representative of the bet for each
player; and a processor coupled to the scanner and each camera, and
configured to process the scanner signal to identify each of the
cards dispensed to each of the players playing the card game, and
to process the image by locating at least one chip and creating a
signal representing the chip, comparing the signal to a plurality
of stored signatures, when a match occurs, generating a signal
representing the bet.
2. The card delivery and bet recognition system of claim 1,
wherein: the player's bet includes a plurality of chips having at
least two different denominations; and the image processor is
configured to identify edges of the chips, segment the image into a
plurality of individual candidate chips, generate a signature for
each of the candidate chips, identify each of the candidate chips
by comparing the signature of each candidate chip to a plurality of
stored signatures representing valid chips and associated
denominations, and add denominations associated with each of the
valid chips to determine the wager.
3. The card delivery and bet recognition system of claim 1, further
comprising: a switch coupled to the processor for the dealer to
indicate the start of a new game; and wherein the processor is
configured to periodically store images from each camera and to
compare the images to one another, and when a change occurs during
play of the game to generate an alarm signal.
4. The card delivery and bet recognition system of claim 2, further
comprising: a switch coupled to the processor for the dealer to
indicate the start of a new game; and wherein the processor is
configured to periodically store images from each camera and to
compare the images to one another, and when a change occurs during
play of the game to generate an alarm signal.
5. The card delivery and bet recognition system of claim 1, further
comprising: a central processor coupled to the processor, and
configured to receive information regarding at least one player and
calculate a theoretical win of the casino; and wherein the central
processor is configured to generate a worth signal representative
of the player's true worth.
6. The card delivery and bet recognition system of claim 2, further
comprising: a central processor coupled to the processor, and
configured to receive information regarding at least one player and
calculate a theoretical win of the casino; and wherein the central
processor is configured to generate a worth signal representative
of the player's true worth.
7. The card delivery and player evaluation system of claim 1,
wherein: the central processor is configured to generate a worth
signal representative of the player's true worth, and generate a
comp value for the player.
8. The card delivery and player evaluation system of claim 1,
further comprising: a reader coupled to the processor and
configured to read player tracking cards issued by a casino having
information regarding the players.
9. The card delivery and player evaluation system of claim 1,
further comprising: a keyboard coupled to the processor for the
player to enter game-related information.
10. The card delivery and player evaluation system of claim 1,
further comprising: a chip tray image capture device positioned in
proximity to a chip tray and configured to capture an image of the
contents of the chip tray; and an image processor coupled to the
chip tray image capture device and configured to process the image
by locating at least one chip and creating a signal representing
the chip, comparing the signal to a plurality of stored signatures,
when a match occurs, generating a signal representing the contents
of the chip tray.
11. The card delivery and player evaluation system of claim 10,
further comprising: a reader coupled to the processor and
configured to read dealer tracking cards issued by a casino having
information regarding the dealers.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Prov. No.
60/644,208 filed Jan. 14, 2005, incorporated herein by
reference.
FIELD
[0002] The invention relates to systems for monitoring casino card
games and card game players whereas the system includes a means for
automatic bet recognition. In particular, the invention provides a
technique for acquiring an image of a bet at particular times
during the play of a card game, processing the image, and
monitoring the bets made by the game players during the play of the
game. The system may further include one or more data input or
acquisition devices to input other information such as the
denomination and quantity of chips comprising the bet, the
denomination and quantity of chips comprising the inventory of the
game table's chip tray, to identify all positions for which game
players are actively engaged in the play of the game and scan and
identify the card value, rank and suit and disposition of all cards
dealt from the game deck to a game hand, a game player and/or a
game dealer engaged in the play of the game, and a means for
determining the outcome, win/loss, of each game hand according to
the game rules and so forth.
BACKGROUND
[0003] Billions of dollars are wagered annually on card games,
therefore a means to accurately monitor, record and identify the
amount of each bet and to subsequently determine the outcome
(win/loss) of each wager according to the game rules is vital to
the successful operation of such card games, as is a real time
means for monitoring the inventory of the chip tray associated with
the game being played. The amounts bet and the wins and the losses
of the games and game players can be significant. Unscrupulous
players and employees may be inclined to attempt to cheat during
play of the game and/or steal chips from the chip tray. Likewise,
it is also possible that an unscrupulous dealer may cheat, or act
in cooperation with a player who is cheating. Casinos could save
significant amounts of money lost to cheaters and dealer theft with
a system that could automatically detect the amount of each bet and
then determine the outcome of each game hand, the proper win and
loss for each game hand played during a game round or playing
session, and a means for reconciling the chip inventory of the chip
tray at the beginning of a game round with the chip inventory of
the chip tray at the end of each game round in accordance with the
amounts won or lost by the players, as determined by the automatic
bet recognition system, during a game round.
[0004] Conventional types of automatic bet recognition techniques
are known. For example, a casino may embed radio frequency
identification (RFID) tags in their chips, which are then read by
an RFID reader proximate to a betting area. While this technique
may be advantageous, it may result in erroneous bets since it may
not be able to accurately direct the RFID sensing in a limited
region of the table. Patents describing this technique include U.S.
Pat. Nos. 5,735,742 and 5,651,548. Another type of automatic bet
recognition uses a camera in an attempt to image and then determine
the amount of each wager. However, conventional systems of this
type use cameras mounted relatively distant from the betting area
or use a single camera attempting to image the entire game table,
which does not provide an accurate and reliable way to determine
the bet. Patents describing this technique include U.S. Pat. No.
6,758,751.
SUMMARY
[0005] What is needed is a reliable system for acquiring an image
of one or more bets associated with a particular seat, position or
game player and then processing the image(s) to accurately
determine the value of the wager(s). Preferably the chip(s)
comprising the bet would be stacked in a vertical position. Such a
system could accurately determine the amount of each player's
bet(s) and whether or not a bet was won or lost, which would
potentially save casinos significant amounts of money. Further, a
reliable means for acquiring an image of the game tables chip tray
inventory, and accurately calculating the number and denomination
of each chip contained in the chip tray, and determining the total
value of the chip tray's inventory, in real time, is also needed.
The chip tray would preferably be transparent and any chip tray
imaging device would be capable of capturing an accurate image of
chips placed horizontally in one or more one-half cylinder shaped
tubes comprising the chip tray; and whereas the tubes would be
slightly angled upward, from the back of the chip tray closest to
the dealer, toward the front of the chip tray which would be
closest to the players; and whereas the upward angle of each tube
would cause the chips to remain flush against one another
increasing the accuracy of the chip tray's imaging device.
Integrating the automatic bet recognition device and the automatic
chip tray imaging device into the system enables the system to
generate a real time tabulation of the win/loss of each player, the
win/loss of the game table, and to activate an audible or visual
alert when a players bet at the end of a game round is inconsistent
with the amount that the bet should be, according to the game
rules, at the end of a game round; and/or activate an audible or
visual alert when the inventory of the chip tray is inconsistent
with the amount of chips that should be in the chip tray when the
settlement of the bets for the current game round is completed.
Further, the system includes a means for the game dealer to enter
into the system the amounts of any cash or call bets that would not
be identified by the imaging device.
[0006] The invention overcomes a number of aforesaid limitations of
conventional systems and provides a system and method that can
reliably acquire an image of a bet and then process the image to
accurately determine the amount wagered. Aspects of the invention
can include one or more data input or acquisition devices to input
other information such cash or call bets, the denomination and
quantity of chips comprising the inventory of the game table's chip
tray, debit and credit transactions (fills, chips returned to
casino cage, credit issued to and paid by game players), positions
or seats occupied by players engaged in the play of the game, cards
dealt to each player hand, each players strategy proficiency, the
outcome of each game round and so forth.
[0007] An exemplary embodiment of the invention includes a bet
recognition system for use with a card game. A game table is
provided for playing a card game including a bet location for each
player. An image capture device is positioned in proximity to the
bet location and configured to capture an image of a player's bet.
An image processor is coupled to the image capture device and
configured to process the image by locating at least one chip and
generating a signal representing the chip, comparing the signal to
a plurality of stored signatures, and when a match occurs,
generating a signal representing the bet. In one aspect, the image
processor is configured to generate an error signal when unable to
match a candidate signature with at least one of the stored
signatures.
[0008] An exemplary embodiment of processing an image representing
a plurality of chips having a plurality of different denominations
comprises the steps of capturing an image representing the
plurality of chips, identifying edges of the plurality of chips,
segmenting the image into a plurality of individual candidate
chips, generating a signature for each of the candidate chips,
identifying each of the candidate chips by comparing the signature
of each candidate chip to a plurality of stored signatures
representing valid chips and associated denominations, and adding
denominations associated with each of the valid chips to determine
the wager.
[0009] An exemplary embodiment of training an image processor to
process an image representing a plurality of chips having a
plurality of different denominations comprises the steps of
capturing a plurality of images representing chips having different
denominations, generating a signature for each of the chips having
different denominations, and storing the signatures.
[0010] The invention provides numerous aspects and advantages to a
card game with automatic bet recognition. Advantages of the
invention include the ability to identify chips wagered by a player
to automatically determine the player's bet for a game hand and the
player's win or loss during a playing session. The invention can
also track bets during the course of a game to automatically
determine the player's betting strategy relative to one or more
card count systems programmed into the system.
[0011] An exemplary embodiment of the invention includes a chip
tray inventory recognition system for use with a card game. A game
table is provided for playing a card game including a chip tray to
contain the game table's bankroll. An image capture device
positioned in proximity to the chip tray and configured to capture
an image of the chips comprising the inventory of the chip tray. An
image processor coupled to the image capture device and configured
to process the image by locating at least one chip and creating a
signal representing the chip, comparing the signal to a plurality
of stored signatures, when a match occurs, generating a signal
representing the bet. In one aspect, the image processor is
configured to generate an error signal when unable to match a
candidate signature with at least one of the stored signatures.
[0012] An exemplary embodiment of processing an image representing
a plurality of chips in a chip tray having a plurality of different
denominations comprises the steps of capturing an image
representing the plurality of chips, identifying edges of the
plurality of chips, segmenting the image into a plurality of
individual candidate chips, generating a signature for each of the
candidate chips, identifying each of the candidate chips by
comparing the signature of each candidate chip to a plurality of
stored signatures representing valid chips and associated
denominations, and adding denominations associated with each of the
valid chips to determine the amount of the chip tray inventory.
[0013] An exemplary embodiment of training an image processor to
process an image representing a plurality of chips in a chip tray
having a plurality of different denominations comprises the steps
of capturing a plurality of images representing chips having
different denominations, generating a signature for each of the
chips having different denominations, and storing the
signatures.
[0014] The invention provides numerous aspects and advantages to a
card game with automatic chip tray inventory recognition.
Advantages of the invention include the ability to identify the
chip tray inventory during real time and provide a running count of
the chip tray's inventory and to automatically determine the game
tables win or loss during the play of the game; and/or to
automatically determine the win or loss of a plurality of game
tables coupled to the system during real time.
DRAWINGS
[0015] These and other features and advantages will become better
understood with reference to the description, claims and
drawings.
[0016] FIGS. 1A-B depict a gaming table system including peripheral
components, a computer, memory and peripheral interfaces according
to an embodiment of the invention.
[0017] FIG. 2 is a flowchart depicting a method according to an
embodiment of the invention.
[0018] FIG. 3 is a digital image of two stacks of gaming chips on a
table, acquired with a conventional digital camera, at a resolution
of 960.times.1280.times.3 bytes (24-bit color).
[0019] FIGS. 4A-B are exemplary flowcharts showing methods to
process digital images of chips according to embodiments of the
invention.
[0020] FIG. 5 depicts intersecting lines of vertical and horizontal
edges detected in the image of FIG. 3 following flowchart 450 step
468.
[0021] FIG. 6 depicts a final segmentation of the image of FIG. 3,
after flowchart 450 step 472. Grey areas are filled regions
identified as possible chip stacks. White lines are borders of the
potential chip stacks, as determined by the method.
[0022] FIG. 7 depicts images of the three identified potential chip
stacks from FIG. 3, after the completion of the method described
flowcharts 400 and 450. The first candidate stack is an edge of a
soup can and will be eliminated from consideration in subsequent
steps of the method.
[0023] FIG. 8 is an exemplary flowchart showing an exemplary method
to segment an image of a stack of chips into individual chip
images.
[0024] FIG. 9A depicts an original digital image of an isolated
single stack of chips, and the output of intermediate stages of the
method shown in FIG. 8. FIG. 9B depicts the results of horizontal
edge detection. FIG. 9C shows the most likely interchip boundaries
determined by the method.
[0025] FIG. 10 depicts images of several individual chips from the
stack of FIG. 9, identified in the chip location step.
[0026] FIG. 11 is an exemplary flowchart showing a method to locate
color bands in the image of an individual gaming chip, and generate
a list of band colors and widths for input to a chip identification
method.
[0027] FIG. 12 depicts an original digital image of a single gaming
chip, including some background areas at the edges of the chip,
which is the output of an exemplary method. FIG. 12A depicts the
original digital image. FIG. 12B shows the edge boundaries of
vertically divided regions of the chip edge, as determined by the
method. The smooth black line marks the 3.times. s.d. of the
background, a recommended threshold for determining which peaks are
significant. FIG. 12C shows the observed colors of the bands of the
chip in 12A, after merging adjacent regions and excluding edge
regions. FIG. 12D shows the bands after brightness normalization.
The four color bands depicted will be input to the neural network
classifier of the method.
[0028] FIG. 13 depicts a standard signature pattern of the example
chip of FIG. 12, as computed in the exemplary method.
[0029] FIG. 14 depicts an exemplary architecture for a radial basis
function neural network, suitable for matching lists of transformed
colors and patterns to those expected for authorized chip patterns.
The input layer of the network includes nodes for each entry in the
standard signature pattern of any chip. In this example, the chip
signatures include the normalized red and green intensities, and
the radial width, of up to four color bands. More bands, and the
sizes and colors of enclosed symbols, are included in the signature
pattern in other embodiments. The RBF layer comprises
two-dimensional Gaussian functions, which are centered at the
positions of the various standardized colors of authorized chips.
Each input band (three node group) is connected to each RBF node.
The RBF nodes are activated proportionately to the probability that
the color represented by the node is present among the input bands.
The output layer is trained to report the probability that the
input band colors and widths match the pattern of an authorized
chip denomination. Each output node reports the probability that
the input chip is a chip of the denomination represented by that
node. The value of the output node is the sum of its inputs, each
weighted by a weight associated with each line in FIG. 14, added to
a "bias" constant, and operated upon by a "transfer function."
Various linear and nonlinear transfer functions are commonly
employed. The preferred embodiment used a sigmoid transfer function
(tanh or equivalent), which allows the output values to be
interpreted as probabilities.
[0030] FIG. 15 depicts the radial basis functions of the second
layer of a RBF neural network trained to recognize 15 distinct
colors. The number of distinct colors recognized in any particular
embodiment will depend on the variety of authorized chip patterns
used to train the network, as taught in the method. As clarified
from FIG. 15, 15 distinct colors are readily distinguished by this
method.
[0031] FIG. 16 depicts the output of the neural network depicted in
FIG. 14, when presented as input with the standard signature
patterns of some of the chip images of FIG. 10. Chips 5, 15, and
18, counting from the top of the stack, are chips of authorized
denomination $5, and their outputs are shown as blue bars. Chips 19
and 21 in the stack of FIG. 8 are chips of authorized denomination
$1000, shown as green bars. Chips 1 and 3 of the stack of FIG. 8
are unauthorized chips, shown as red bars. The neural network
correctly classifies all of the chips.
DETAILED DESCRIPTION
[0032] The invention is described with reference to specific
apparatus and embodiments. Those skilled in the art will recognize
that the description is for illustration and to provide the best
mode of practicing the invention. As referred to herein, a game
constitutes one or more hands of cards.
[0033] A. Card Game System
[0034] FIG. 1A depicts a gaming table system 100 according to an
exemplary embodiment of the invention. The table includes a card
area 102 for placement of the cards during play of the game, and a
betting area 104 for the player to place a wager. FIG. 1B is an
overhead view of the table system 100 showing the player positions
130a-e, the card area 102 and the respective betting areas 104a-e
for the player stations.
[0035] The dealer station includes a card shoe 106 for storing the
cards that the dealer deals to each of the players. The exemplary
card shoe includes a card scanner of the type described in U.S.
Pat. Nos. 5,362,053; 5,374,061; 5,722,893; 6,039,650; 6,299,536 and
6,582,301 all incorporated herein by reference in their entirety.
The card scanner is connected to an interface 108 that provides a
signal to a computer 110, which determines the cards dealt to the
players.
[0036] The dealer station also includes a chip tray 120 where the
dealer stores chips for paying out players when they win and for
collecting their bets when they lose. In one aspect of the
invention, the chip tray is transparent and a camera 122 is
positioned under the chip tray to capture images of the chips in
the chip tray 120 during the course of the game, including before
and after each round. A light can be included under the tray to
provide illumination to the chips. The chip tray camera 122 is
connected to an interface 128 that provides information regarding
the chips stored in the tray to the computer 110. In an exemplary
embodiment, the chip tray camera periodically scans the chip tray
and communicates images of the contents of the chip tray to the
computer, which can then determine the content of the chip trap by
performing image processing as described herein. In one aspect,
dealers are issued dealer tracking cards and a dealer tracking card
(DTC) reader 126 is coupled to the processor and configured to read
dealer tracking cards issued by a casino having information
regarding the dealers. In this manner, the invention can track the
contents of the chip tray during each dealer's shift and ensure
that the wins and losses are correctly paid into and out of the
chip tray.
[0037] The player station 130 can include an exemplary display to
provide player information including betting and game information,
casino information and other information via interface 132. A
player tracking card (PTC) reader 134 is provided for the player to
enter a card and provide betting information to the casino computer
for gambling credits and so forth. PTC readers are known in the art
and include magnetic, optical and radio frequency identification
type systems. A camera 140 is provided in the player station to
capture an image of the player's wager in the betting area 104.
Also, since a casino may have inconsistent lighting, in one aspect,
a light 144 is provided to illuminate the wager in betting area
104. The light provides a consistent image illumination that
improves image capture and image processing in some
circumstances.
[0038] An optional separate camera 148 can also be coupled to
system. When the system determines that one or more predetermined
criteria relative to the play of the game or the game player has
been achieved the camera 148 is programmed to automatically focus
in on the play area or seat occupied by the game player and
photograph the subject player. The player's photo will be
automatically transmitted and stored within a commercially
available biometric database maintained by the host casino and
activate an audible or visible alert for the benefit of the dealer
and or management. The predetermined criteria may include, without
limitation, a VIP player, a known card cheat, a known card counter
or a player who has been barred from play has logged in as playing
at the specific game table in a specific seat, or a player who the
system has determined, during the play of the game, to be a highly
skilled card counter, is varying his/her bets according to the
decks running or true count, winning an unusual amount of money or
hands and so forth.
[0039] The computer 110 includes a memory 112 that stores
information including control procedures 112a, communication
procedures 112b and data 112c. The computer 100 is also coupled to
a casino computer 150, which collects information regarding the
bets and keeps track of the money in the casino. Operation of the
computer is described below with reference to the card game system
100.
[0040] FIG. 2 is a flowchart 200 depicting a method according to an
embodiment of the invention. In step 202, the game begins and the
invention performs wager tracking. The invention provides a means
for signaling the beginning of a game. In one aspect, the dealer
presses a button on the card shoe 106, chip tray 120 or other
location to signal the start of the game to the computer. Camera
140 acquires a first image of the bet (initial wager) in the area
104 and the computer stores the image. In one aspect, the computer
also processes the image as described below. In step 204, the game
is played. In step 206, the game is finished and the camera
acquires a second image of the bet (final wager). In step 208, the
first image and second image are compared to one another. If the
player wins and the bets match, then the player is paid in step
212. If the house wins and the bets match, then the house keeps the
wager in step 214. In either event, if step 208 determines that the
final wager does not match the initial wager, then step 216
generates an alarm signal to alert the dealer and/or additional
casino personnel to review the game for possible fraud. In aspects
of the invention, the alarm can be an audible or visual alert in
proximity to the game table and/or an alert on a remote monitor in
a remote location.
[0041] In one aspect, the first image is taken at the beginning of
a game and the second image is taken at the end of the game. In
other aspects, images may be taken at specific times during the
course of play or at periodic time intervals during the course of
play. One benefit to acquiring multiple images is that there may be
times during the game that a change in the bet is allowed, for
example in blackjack, including doubling down or splitting. In
these circumstances, the bet may change during the game and
additional images acquired during the game may be processed to
ensure the lawful play of the game. In one aspect, FIG. 2 includes
the additional step 207 to depict intermediate image acquisitions
of the player's station during course of the game, and step 207A to
depict intermediate image acquisition of the dealer's chip tray
during course of the game.
[0042] In one aspect, a first image and second image are compared
to one another to ensure that no illegal change was made to the bet
during the course of the game. One technique for comparison is to
permit a certain number of pixel differences between the images
that define an allowed match, where a number of different pixels
above that threshold number is defined as a mismatch.
[0043] In yet another aspect, steps 202A and 220 are implemented to
acquire an image of the chip tray and to identify the contents of
the chip tray. This can be done in terms of contents or value of
all the chips in the tray. Once the game is complete and the
invention knows the value of the wagers won and lost by the
players, the invention can calculate an expected value of the chips
that should be in the chip tray. Step 220 compares the actual
amount to the expected amount. If there's a mismatch between these
amounts, the invention can alert the dealer or supervisor to
investigate.
[0044] B. Automatic Bet Recognition Image Processing
[0045] An exemplary method of performing an automatic bet
recognition technique is described below with reference to the
figures. Headings 1 through 8 describe exemplary aspects of the
technique for the sake of describing the invention in an organized
manner and are not intended to be limiting.
[0046] 1. Obtain Suitable Digital Image
[0047] FIG. 3 depicts chips placed in a betting area similar to
area 104. A preferred input to the automatic bet recognition system
(ABRS) image processing and pattern recognition (IPPR) system is a
medium or high resolution color digital image of the betting area
104. Color resolution should include at least 4 bits of resolution,
and preferably at least 8 bits of resolution, in each color
channel. The spatial resolution can be comparable to that achieved
by commercially available CCD or CMOS imaging devices commonly used
in digital cameras (e.g. 1 mega-pixel image or higher). The imaging
device should be aimed and focused such that all stacks of chips
are viewed from the side and in focus. A stack of chips will
therefore appear in the image as a rectangle with vertical and
horizontal sides, whose width is constant and determined by the
standard diameter of a gaming chip (and the distance between the
stack and the imaging device), and whose height varies depending on
the number of chips in the stack.
[0048] In some embodiments of the invention, the value of chips in
a chip tray is to be imaged and evaluated. In these embodiments,
the stacks of chips will be horizontal rather than vertical, and
will be confined to a constrained area (i.e. the chip tray). In the
following discussion, references to "vertical" and "horizontal"
dimensions of chip stacks should be understood to be reversed in
such embodiments. Also, certain functions, e.g., function 2
describing location of stacks of chips in the image, may be
obviated in such embodiments.
[0049] All stacks of chips to be evaluated are preferably
completely contained within the image, and any extraneous objects
(e.g. cards, water bottles, etc.) in the image should be spatially
disjoint from the stacks of chips. If more than one stack of chips
is present in the betting area, each such stack should be disjoint
from and not occlude any other such stacks in the image. The
lighting should be as uniform as possible, without distinct shadows
superimposed on the stack. The background should contrast as much
as possible with the colors of the chips. Such contrast is
facilitated if the imaging device is focused at the distance of the
betting area, so that distant backgrounds, e.g. players' clothing,
is somewhat out of focus.
[0050] The exemplary card gaming system is shown with reference to
the exemplary embodiment shown in FIG. 1. Details of the exemplary
physical embodiment of the imaging hardware used to obtain the
required images, its spatial orientation on the game table, and the
external signals and/or internal timers used to trigger the
acquisition of an image, are components of the ABRS system which
employs the method taught in this invention, and may be modified
with respect to the ABRS-IPPR. Whenever such an image is obtained,
the ABRS-IPPR will proceed to process and evaluate the stacks of
chips (if any) within the betting area, using the method taught in
this invention. The ABR-IPPR may be used with other configurations
than shown in the exemplary embodiment of FIG. 1.
[0051] 2. Locate Stacks of Chips in the Image
[0052] Once a suitable digital image has been obtained and
presented to the computer 110 executing the ABRS-IPPR software
program, the program first locates the stacks of chips (if
any).
[0053] Initially, it is beneficial to perform some pre-processing
with various digital filters, such as a median filter in order to
normalize signal intensity, improve contrast, remove image features
much smaller than gaming chips, and filter out other noise.
[0054] The invention employs a method of digital image processing.
One such method is called "edge detection" and searches for
vertical and horizontal lines bordering regions of different color.
Another method called "line continuity search" extends horizontal
and vertical lines to delimit regions. A complementary method
called "regional continuity search" seeks to find regions of
consistent color and/or texture within the image, which are
candidates for the edge surfaces of individual chips. An
alternative method called "template matching" seeks to locate areas
of the image that correspond to a preset template, which in this
case is a rectangle of known width and variable height and color.
The latter method may be performed on the original image, or,
preferentially, on a two-dimensional Fourier transform of the
image. While these techniques are described with reference to
color, they also work well on a grayscale copy of the original
color image.
[0055] FIG. 4A depicts a high-level method employed by the
invention to identify a player's bet, locate the candidate chips
constituting the bet, match signatures of the candidate chips to
those stored in memory and then validate a match, if possible.
Although many algorithmic methods and combinations of algorithmic
methods could be employed to locate stacks of chips in an image,
the preferred embodiment comprises eleven sequential steps shown in
FIG. 4B.
[0056] Step 452--Vertical edge detection: Each pixel in the image
is replaced by a grayscale value computed from the average
difference between the color values of a number of pixels to its
left and the color values of a number of pixels to its right. The
appropriate number of pixels to use for edge detection depends on
the optical and digital resolution of the image; it ranges between
one pixel and about five pixels. For the example in FIG. 3, three
pixels were used for vertical edge detection.
[0057] Step 454--Vertical median filters: Each pixel in the
vertical edge image after step 452 is replaced by the median of a
vertical column of N pixels, of which it is the center. The effect
of this filter is to eliminate vertical lines shorter than N/2, and
to fill in gaps in longer vertical lines. Appropriate values of N
depend on the image resolution and on the size of vertical
boundaries to be detected--values of N from 3 to 1/2 of the chip
height may be optimal. For the example in FIG. 3, in which the
average height of a single chip image is 30 pixels, 9 pixels were
used for the vertical median filter.
[0058] Step 456--Vertical line detection: Within a sliding
rectangular window, the brightest pixel in each row is displaced
into the column containing the majority of brightest pixels, and
each other pixel in the row is set to zero. The effect of this
filter is to enhance vertical lines, and to convert the grayscale
image into a binary image of candidate vertical edges. The
resolution and contrast of real vertical edges improves, although
some random noise begins to look like vertical lines. Various
rectangle sizes can be used in this step. For the example in FIG.
3, a rectangular window of height 30 pixels (the average height of
a chip, hence the minimum length of real vertical edges) and of
width 5 pixels (the average error of the vertical edge detector)
was used. In a higher resolution or more tightly focused image,
larger rectangles would be used. In a lower resolution or more
distant image, smaller rectangles would be used. The size of the
optimal line detection rectangle is fixed by the properties of the
imaging hardware, and remains substantially constant for a
particular embodiment of the invention.
[0059] Step 458--Vertical path discovery: Locate continuous
vertical paths that are at least as long as the height of a chip
(30 pixels, for the example in FIG. 3), and which may connect with
other vertical paths within a horizontal distance determined by the
expected maximum misalignment of chips in a stack (30 pixels, for
the example in FIG. 3). Pixels which are part of such vertical
paths are set to binary 1's, and pixels which are not are set to
binary 0's.
[0060] Step 460--Horizontal edge detection: Each pixel in the
original color image is replaced by a grayscale value computed from
the average difference between the color values of a number of
pixels above it and the color values of a number of pixels below
it. For the example in FIG. 3, three pixels were used for
horizontal edge detection.
[0061] Step 462--Horizontal median filters: Each pixel in the
horizontal edge image after step (e) is replaced by the median of a
horizontal row of N pixels, of which it is the center. The effect
of this filter is to eliminate horizontal lines shorter than N/2,
and to fill in gaps in longer horizontal lines. Appropriate values
of N depend on the image resolution and on the size of horizontal
boundaries to be detected--values of N from 3 to 1/2 of the chip
width may be optimal. For the example in FIG. 3, 11 pixels were
used for the horizontal median filter. This is the horizontal
distance in the image over which there is no appreciable curvature
of the chip edge.
[0062] Step 464--Horizontal line detection: Within a sliding
rectangular window, the brightest pixel in each column is displaced
into the row containing the majority of brightest pixels, and each
other pixel in the column is set to zero. The effect of this filter
is to enhance horizontal lines, and to convert the grayscale image
into a binary image of candidate horizontal edges. The resolution
and contrast of real horizontal edges improves, although some
random noise begins to look like horizontal lines. Various
rectangle sizes can be used in this step. For the example in FIG.
3, a rectangular window of height 5 pixels (the average error of
the horizontal edge detector) and of width 340 pixels (the average
width of a chip, hence the maximum length of real horizontal edges)
was used. In a higher resolution or more tightly focused image,
larger rectangles would be used. In a lower resolution or more
distant image, smaller rectangles would be used. The size of the
optimal line detection rectangle is fixed by the properties of the
imaging hardware, and remains substantially constant for a
particular embodiment of the invention.
[0063] Step 466--Horizontal path discovery: Locate continuous
horizontal paths that are at least as long as the uncurved width of
a chip (100 pixels, for the example in FIG. 3), and which may
connect with other horizontal paths within a vertical distance
determined by the expected maximum curvature of chip edges (I
pixel/100 pixels, for the example in FIG. 3). Pixels which are part
of such horizontal paths are set to binary 1's, and pixels which
are not are set to binary 0's.
[0064] Step 468--Boundary detection: The vertical boundary lines
from step 468 are combined with the horizontal boundary lines from
step 466, to search for candidates for fully bounded rectangular
regions. Vertical lines extending beyond their intersections with
horizontal lines are truncated. Likewise, horizontal lines are
truncated at corners. The result of processing the image of FIG. 3,
after boundary detection, is shown in FIG. 5.
[0065] Step 470--Flood fill regions: Enclosed regions after step
468 are flood filled. Filled regions with dimensions less than 1/2
of a nominal chip dimension (height 30 pixels, width 320 pixels, in
the image of FIG. 1) are eliminated.
[0066] Step 472--Clip stack candidates: The bounding rectangle of
each filled region from step 470 is a candidate for a stack of
chips. The five candidate stacks identified in the FIG. 3 image are
shown in FIG. 6. The two smaller regions are rejected as too small
to be chip stacks, leaving three candidate stacks located, as shown
in FIG. 7.
[0067] After stack location, the original image has been replaced
by zero or more smaller images, each including only a subset of the
original image that depicts a stack of chips, as shown in FIG. 7.
If no stacks of chips are found, the method terminates and reports
that it was unable to detect any chips in the betting area. This
might happen because of a failure of the imaging hardware, a
failure of the stack location method, or improper placement of a
stack by the player, or it might be a normal expected condition at
the current stage of the game. In any case, the ABRS system in
which the ABRS-IPPR software program is embedded takes appropriate
action to either alarm the dealer or casino personnel, or perhaps
to identify that player position as uninhabited.
[0068] It will be readily understood by those skilled in the art
that there are many other combinations of digital filters, method
steps, and pattern-matching steps known to the art which could be
employed to achieve essentially the same stack location result as
the exemplary sequence of digital filters and method steps
described above and depicted in FIG. 4A-B. It is understood that
the present invention includes any and all such combinations of
filters known to the art, and is not limited to the exemplary
sequence of filters described above.
[0069] If the stack location method detects one or more stacks of
chips, subsequent steps in the method are typically performed
separately and sequentially on each such stack. The values of all
the chips in each stack are combined together for the final bet
tabulation of step 410.
[0070] 3. Locate Individual Chips in a Stack of Chips
[0071] The input to this step of the exemplary method is an image
of a single stack of candidate chips, or one of the plurality of
stacks, as shown in FIG. 7. The next task is to divide the image
into separate images, each one an image of the edge of a single
chip in the stack.
[0072] Although many algorithmic methods and combinations of
algorithmic methods could be employed to locate stacks of chips in
an image, the preferred embodiment comprises six sequential steps
as shown in the FIG. 8 flowchart 800.
[0073] Step 802--As for the stack location method, a horizontal
line search for parallel horizontal boundaries spaced close to the
known chip thickness, combined with a regional continuity search,
can find many chip boundaries.
[0074] Step 804--As for the stack location method, a horizontal
median filter accentuates horizontal lines in the image. The
initial separated image of an exemplary stack of chips is shown in
FIG. 9A, and the result of the horizontal edge filter and edge
enhancement is shown in FIG. 9B.
[0075] Step 806--The average edge intensity in each row is
determined, and a peak-finding method is used to locate the average
vertical position of horizontal edges. Frequently, there will be no
clear boundary between two adjacent chips of the same denomination.
In this case, the method uses its knowledge of the chip thickness
(or aspect ratio) to deduce that two chips are stacked rather than
just one. In this case, an edge between two peaks of approximately
double (or triple, etc) the expected chip height is inferred. It is
unnecessary that the software identify a precise boundary between
adjacent identical chips--it is sufficient to know how many there
are. In the example shown in FIG. 9A, the expected chip height is
30 pixels, so detected "peaks" less than 25 pixels apart, caused by
shadows in the image (faint diagonal lines crossing chips in FIG.
9B), are ignored.
[0076] Step 808--Starting at the average vertical position of each
inferred chip boundary, a dynamic programming algorithm, known as a
Viterbi algorithm, is employed to determine the most likely path of
the boundary between adjacent chips. The chip boundaries determined
by this method for the chip stack image of FIG. 9B are shown in
FIG. 9C.
[0077] Step 810--The chip boundaries detected in step 808 are
curved, especially for chips near the top and bottom of the stack,
because of parallax of images acquired at close range. In order to
avoid including portions of neighboring chips in the individual
chip images, the chips discovered in step 808 are clipped
vertically, at positions such that about 80% of the horizontal
boundary of the chip is excluded from the chip image.
[0078] Step 812--The boundaries of individual chips in the image,
isolated in steps 802-810, are recorded for input to the chip
identification method.
[0079] It is usually unnecessary for the chip location method to
use any knowledge about the precise colors and/or patterns of the
valid chip denominations. However, if the ambient lighting is
especially uneven, or the chip patterns of different denominations
of chips are insufficiently distinctive, the use of expected chip
colors and patterns may increase the robustness of chip
location.
[0080] The chip location method may be unable to subdivide the
stack image into individual chip images, either because the stack
was misidentified (e.g. it is really a similar-looking foreign
object), or the stack is poorly aligned or contains foreign
objects, or the ambient lighting or shadow obscures the chip
boundaries. In some such cases, the "stack" can be rejected as a
foreign object, and processing can proceed on other detected
stacks. In the exceptional case in which a stack can neither be
properly segmented nor rejected, the ABRS-IPPR notifies the parent
ABRS system that it is unable to evaluate the bet, so the dealer
can make a manual evaluation of the bet.
[0081] The final result of applying the method of flowchart 800 to
the stack of FIG. 9A, is the set of individual chip images shown in
FIG. 10.
[0082] It will be readily understood by those skilled in the art
that there are many other combinations of digital filters,
algorithmic steps, and pattern-matching steps known to the art
which could be employed to achieve essentially the same chip
location result as the exemplary sequence of digital filters and
algorithmic steps described above and depicted in FIG. 8. It is
understood that the present invention includes any and all such
combinations of filters known to the art, and is not limited to the
exemplary sequence of filters and algorithmic steps described
above.
[0083] If the chip location method detects one or more chips in the
stack, subsequent steps in the method can be performed separately
and sequentially on each such chip. The values of all the chips in
the stack are combined together, and with the counts and values
obtained from other stacks, for the final bet report.
[0084] 4. Tabulate Vertical Bands of a Single Chip
[0085] The input to this step of the method is an image of the edge
of a single gaming chip, as shown in FIG. 12A. The next task is to
characterize the colors, widths, and patterns of the several
distinct regions of the chip edge. In general, each denomination of
chip that can be legitimately wagered on a particular table has a
unique combination of at least two distinct colors, arranged
periodically around the periphery of the chip such that, regardless
of the orientation of the chip, at least one complete instance of
the alternating pattern of colors is visible in the image (which
captures 1/2 of the periphery of the chip). In addition to
alternating bands of different colors, the edge of the chip may
display geometric patterns such as dots, bars, or other figures of
one color, surrounded by regions of another color.
[0086] The band tabulation method characterizes each band of the
chip visible in the image. A band consists of a roughly rectangular
region of the image, spanning the full thickness of the chip, and
demarcated on the left and/or right by regions of contrasting
color. In some chip patterns, the boundary between bands is not
vertical but V-shaped, slanted, or irregular. The method treats
such band boundaries as if they were vertical. A flowchart of a
preferred embodiment of the band tabulation method is shown in FIG.
11.
[0087] A preferred embodiment of the vertical band tabulation step
comprises the following steps, as depicted in FIG. 11 flowchart
1100:
[0088] Step 1102--Vertical edge detection: As for the stack
location method, a vertical line search finds candidate band
boundaries.
[0089] Step 1102--Peak-picking: The average edge intensity in each
column is determined, and a peak-finding method is used to locate
the average horizontal position of vertical edges. The output of
such an method is shown in FIG. 12B. Some filtering is applied to
eliminate very small regions between "peaks." The surviving peaks
delimit regions of distinct color, which are candidates for the
distinctive color bands defining the denomination of the chip.
[0090] Step 1102--Find the average color of each vertical band: For
each region, compute the mean color, in each RGB channel, of the
pixels within the region, excluding boundary pixels.
[0091] Step 1102--In some gaming chip designs, the color bands
include dots, crosses, or other symbols of contrasting color. The
ABRS-IPPR system can learn which chips have such enclosed symbols
during training, as described below. However, unexpected enclosed
symbols may be encountered, if unauthorized chips are present. The
preferred method can detect such enclosed symbols using a boundary
detection and flood fill method similar to that described in step
470 above. Enclosed symbols are counted and recorded for use in
subsequent chip identification steps. Any such enclosed symbols are
treated as separate regions in subsequent steps--that is, the
average color and relative image area of each enclosed region is
calculated. However, pixels in enclosed regions are excluded from
the average color determination of step 1102.
[0092] Step 1102--Merge adjacent regions of similar color: If two
adjacent regions have similar colors, they are presumed to differ
by the intervention of a shadow, and should be merged into a single
region. Bands surviving this step are shown in FIG. 12C.
[0093] Step 1102--Normalize brightness: Because the incident light
falling on each region is variable, the relative intensity in the
three colors is more distinctive than the absolute intensity of any
color. This is accounted for by normalizing the total luminance of
each pixel. This is done by normalizing the red (R) and green (G)
values of each band such that the total luminance is 1.0. This
normalization converts both black and white bands to grey; it is
equivalent to removing the intensity dimension of an
intensity/hue/saturation (ihs) color representation.
[0094] Step 1102--Find edges of the chip: The input images to
flowchart 1100 are bounded by areas of background, which have
nothing to do with the chip identity. Regions at the extreme left
and right of the chip image are likely to consist of such
irrelevant background. Color regions at the extreme left and right
of the chip image are eliminated, unless they are large enough to
extend beyond the possible boundary region.
[0095] Step 1102--Compute region size: Of the surviving regions,
the true size (measured as degrees of arc) of each region is
computed, based on the viewing angle. Thus, regions near the edge
of the chip span fewer pixels in the image than do similar-sized
regions near the center of the image. A simple approximation
assuming an infinite viewing distance is used to normalize the
region size.
[0096] Step 1102--The first and last regions identified by the
above steps, unless they exceed a parameterized value, are excluded
from the following analysis as unreliable data. The final
normalized bands in the example chip of FIG. 12A are shown in FIG.
12D.
[0097] It will be readily understood by those skilled in the art
that there are many other combinations of digital and heuristic
filters, and algorithmic steps known to the art, which could be
employed to achieve essentially the same band identification result
as the exemplary sequence of digital filters and algorithmic steps
described above and depicted in FIG. 11. It is understood that the
present invention includes any and all such combinations of filters
and algorithmic steps known to the art, and is not limited to the
exemplary sequence of filters and algorithmic steps described
above.
[0098] The output of flowchart 1100 is a list of all bands in the
chip image, each characterized by (a) its true radial width, (b)
its average normalized color, (c) the number, color, and relative
size of any enclosed regions.
[0099] 5. Tabulate Unique Bands
[0100] The input to this step of the method is a list of all bands
detected in the chip image, their normalized colors, and the size,
number, and color of enclosed symbols, if any. In this aspect of
the method, the list of observed bands is reduced to a minimal
ordered list of bands. The method searches for repeating patterns
of sequences of bands of approximately the same color, width, and
included regions. This minimal ordered list of bands constitutes
the raw signature pattern of the chip.
[0101] If there are multiple examples of bands which are similar in
color and size, which are consolidated into a single band in the
above step, then these bands are retained in memory as potentially
distinct bands. These potential additional bands may be used in
subsequent steps of the method to allow matches with signatures
containing additional bands.
[0102] If there are included symbols within the regions, then the
number, colors, and relative size of such symbols is included in
the signature pattern.
[0103] 6. Standardize Colors
[0104] The input to this step of the method is an ordered list of
all unique bands detected in the chip image. Before attempting to
match this signature pattern to one of the expected authorized chip
signatures, the colors are transformed into a maximally distinctive
color space. The parameters of the color transformation are
determined when the signature patterns of all authorized
denominations of chips used at the gaming table are specified
during training, as described below. After color transformation,
the ordered list of unique band widths, transformed colors, and the
number, color, and size of included regions, constitutes the
standard signature pattern of the chip.
[0105] The standard signature pattern of the chip imaged in FIG.
10a is shown as a color image and in the table in FIG. 13.
[0106] It should be noted that the chip depicted in FIG. 12 in fact
has three distinct color bands. The first band detected ("Orange2",
[0.74, 0.1, 0.14]) is actually the same as the fourth band
(("Orange2", [0.61, 0.18, 0.10]). The standard signature patterns
of these bands, actually identical, are measured as different
because the first is lying in the shadow of other chips in the
stack. Such variations of measured signatures under different
conditions of light and shadow is to be expected. The exemplary
method can nonetheless correctly identify gaming chips despite such
variations, provided that the variation in lighting is not too
severe, and the color patterns of authorized chips are sufficiently
distinctive.
[0107] 7. Match Chip Signature to Best Authorized Chip
Signature
[0108] Once the standard signature pattern of each gaming chip has
been determined, it is then classified as either one of the
authorized chip patterns, or as an unauthorized pattern. Any of a
several classification methods may be employed to achieve this
classification, including linear classifiers, Bayesian classifiers,
hierarchical classifiers, neural network classifiers, and others.
While any of these or other classification methods could be used, a
preferred method is a radial basis function neural network
classifier. The advantage of a radial basis function neural network
classifier over many other classifiers is that it is able to
determine when a chip is not a member of the authorized set,
without having to be explicitly trained to recognize unauthorized
chips.
[0109] The standard signature pattern of the chip is supplied as
input to a neural network, which computes the probability that the
chip matches an authorized chip on which the network has been
trained, as explained below. An example of a neural network
suitable for such a determination is provided in FIG. 14. It should
be readily apparent to one skilled in the art that there are many
other neural network architectures and other classification methods
that could be used to obtain substantially equivalent results to
those obtained from the neural network of FIG. 14.
[0110] The neural network depicted in FIG. 14 comprises three
layers: an input layer, a "hidden" layer, and an output layer. Each
node in each layer is connected with each other node in the layer
below it. In general, the links between a layer and the next layer
are associated with a "weight," which is a variable parameter
multiplying the value of the node in the first layer. Each node
computes a "transfer function," a single valued function of the
weighted inputs to the node, plus an additional added "bias"
associated with the node. The value of the transfer function, for
specified inputs, is then propagated on to the next layer of the
network.
[0111] A "radial basis function" neural network is one in which the
nodes of the second, "hidden", layer, are peaked functions (for
example, Gaussian functions) centered at a position in the input
domain specific to that node (in this example, in the
three-dimensional space of red, green, and chip width). With each
node is associated a center and a width, and the value of the node
is the sum of the values of the peaked transfer function when
applied to the inputs. A preferred embodiment of the method uses
radial basis functions which are three-dimensional Gaussian
functions of the red and green values, and the radian dimensions,
of each of the color bands represented in the input layer. With
each node of the RBF layer there are associated six parameters: the
red, green, and width coordinates of the center of the Gaussian
function, and the red, green, and width standard deviations of the
function. In this example, the standard deviations of the RBF
functions in the input dimensions were constrained to be equal, so
that there are four independent trainable parameters associated
with each node in the hidden layer. The red and green dimensions of
the radial basis functions in the network of FIG. 14, after
training on the input denominations represented in the stack of
gaming chips shown in FIG. 3, are shown in FIG. 15. Each peak in
the figure is centered at the (red, green) standardized coordinates
of one of the colors of one of the bands of the authorized chips.
The value of the corresponding node of the network, when presented
as input with the standardized chip signature of a chip, is the
height of the RBF at the red and green coordinates observed for any
color band of that chip.
[0112] The values of the output nodes (the third layer of the
network depicted in FIG. 14) are computed as a weighted sum of the
values of the nodes of the RBF layer, transformed by the transfer
function of the third layer. In the preferred embodiment, the
weights of the links between layers 2 and 3 are trained, and the
layer 3 transfer function is a sigmoid transfer function (e.g.
tanho), which then reports membership probabilities. Various other
transfer functions and weight vectors would also be suitable for
use in this method.
[0113] The output of the neural network depicted in FIG. 14, or of
an alternative neural network or equivalent classifier, is the
probability that the gaming chip is of a particular denomination of
authorized gaming chips included in the set of chips on which the
network was trained, as described below.
[0114] FIG. 16 shows the output produced by the neural network of
FIG. 14, when presented as input with the signature patterns of
seven of the chips from the example stack of FIG. 9. FIG. 16
includes three chips of denomination $5, two chips of denomination
$1000, and two other chips that weren't included in the set of
authorized chips used to train the network.
[0115] If the signature pattern of the chip matches exactly one of
the authorized signatures within a specified tolerance, the
software program records a chip of the corresponding denomination,
and adds it to the amount of the bet.
[0116] If the signature pattern of the chip matches more than one
of the authorized signatures within a specified tolerance, the
ABRS-IPPR notifies the parent ABRS system that it is unable to
determine the value of a chip in the stack. This can occur if
irregularity of lighting or shadow causes the system to incorrectly
estimate the color of a band in the chip signature. The ABRS can
then signal the dealer to manually record the value of the bet. If
this problem recurs, it may be necessary to reduce the number or
increase the distinctness of the authorized chips at the table.
[0117] If the signature pattern of the chip fails to match any of
the authorized signatures within a specified tolerance, the
ABRS-IPPR notifies the parent ABRS system that an unauthorized chip
is present in the stack. The ABRS can then signal the dealer to
manually verify the irregularity, and take appropriate action.
Whereas the game deal may choose to manually enter the amount of
the wager into the system by means of a keypad as he/she would do
in the event the players wager was cash or a call bet.
[0118] It will be readily understood by those skilled in the art
that there are other arrangements and architectures of neural
networks, and similar pattern-matching methods, which can similarly
classify signature patterns into trained categories. It is
understood that the present invention includes any and all such
combinations of classification methods known to the art, and is not
limited to the exemplary neural network architectures described
above.
[0119] 8. Report the Total Amount of the Wager
[0120] The total value of the player's bet is determined by summing
the value of each chip and denomination thereof in each wagered
stack. The ABRS-IPPR reports the total value of the chips in the
player's betting area to the parent ABRS system.
[0121] C. Training to Determining the Authorized Chip Patterns
[0122] The invention also teaches a method of determining the
signature patterns of chips that are authorized for a particular
table in a casino, and determining the matching tolerances for
identifying players' chips as one of the authorized denominations.
The ABRS can distinguish among at least 12 different values (e.g.
$1, $5, $25, $100, $500, $1,000, $5,000, $10,000, $25,000, $50,000
& $100,000) of authorized chip signatures. The reliability of
the ABRS-IPPR system is dependent on the number and distinctness of
the chip signatures. When the number of authorized signatures is
about one dozen, and the colors of bands in the signatures are
sufficiently distinct over the range of illumination in the casino,
the method of the invention is quite reliable. As the number of
authorized chip signatures increases, the method becomes less
robust.
[0123] Prior to use of the system in any casino, the ABRS-IPPR is
trained to recognize the chip signatures authorized for use at each
gaming table. Such training is effected by capturing multiple
images of stacks of chips, including all authorized denominations,
over a range of illumination conditions representative of the
conditions expected when the system is used. For each training
stack imaged, the correct denomination of each chip in the stack is
supplied to the training software program.
[0124] During training, the stack of chips of known denominations
is imaged in the same orientation and configuration as would be
used for determination of the values of a stack of actual gaming
chips in the exercise of the methods taught in this invention. The
true value of the chips in the stack, and, optionally, the true
colors of the bands of said chips, are supplied to the training
method. The training method uses methods well known to the art,
such as error back-propagation, to iteratively adjust the
parameters of the neural network so as to optimize the rate of
correct classification of the chips in the training set.
[0125] During training, the relative size and the colors of
enclosed symbols imprinted within color bands of the chips are
recorded and included in the training set.
[0126] As a part of the training, the optimal color transformation
of color bands of the authorized chips, to be used in the flowchart
800, is determined. The optimal transformation is such as to
maximize the separation, in normalized color coordinates, of the
chips in the authorized set. Techniques for choosing
transformations which maximize the discrimination of exemplars in
the training set are known to the art.
[0127] The number of training examples required to fully train the
recognition system depends on the number and distinctness of the
authorized chip denominations, and the variability of the
illumination. In most cases, the number of training instances
required will be between 100 and 10000. A sufficient number of
training instances can be obtained quite readily--for example, if
two stacks, each containing 10 chips of a chip denomination to be
trained, were stacked in the betting area of each of five player
positions at a gaming table, then a single image captured from each
player position would provide 100 training instances.
[0128] During training, the ABRS-IPPR learns the average signature
of each authorized denomination of chip, the optimal color
transformation to be employed in flowchart 800 of the recognition
method, and the optimal matching tolerance to be used when matching
the candidate chip signature in the recognition method.
[0129] Increasing the number of training examples improves the
ability of the system to correctly identify unauthorized chips. The
training is typically non-linear, where the initial training has
great impact and training in high volumes has an increasingly
smaller impact, but which may nonetheless be worthwhile depending
on the granularity of desired detection.
[0130] If any new chip pattern is authorized for play, or if the
pattern of any authorized chip changes, the system should be
retrained. It may be desirable to periodically retrain the system,
even if there are few or no changes in authorized chip
denominations or patterns, to protect against changes in the colors
of different manufacturing lots of nominally identical chips, and
against changes in illumination at the casino gaming tables.
[0131] D. Additional Applications
[0132] The invention is also applicable to other areas of a casino
where it would be useful to identify chips. For example, the
invention can be implemented in a cashier's booth to ensure that
the correct amount of money is given to patrons in exchange for
their chips. A screen can be provided visible to the cashier to
inform the cashier of the correct total vale of the chips to be
cashed in by the patron.
[0133] The invention can also be used in more than just card games,
for example, craps and other such games where the player has an
area to place a bet, the invention can identify the bet and assist
the dealer in assessing the proper amount to pay out in the event
of a player's win. In fact, since such games can include
odds-related payouts requiring a calculation, the computer may be
able to perform the calculation faster and more accurately than a
dealer.
[0134] E. Player's True Worth Computation
[0135] The invention can be used to automatically determine a
player's true worth to the casino. This computation is performed by
the casino computer 150 using information regarding a player's wins
and losses. Since the invention includes a player tracking card
reader, the invention communicates the identity of the player to
the casino computer along with the player's wins and losses. The
casino then computes the value of the player to the casino and can
provide the player with complementary goods and services based on
the player's true worth.
[0136] As mentioned above, the invention can be used in combination
with other peripherals such as a card shoe of the type described in
U.S. Pat. Nos. 5,362,053; 5,374,061; 5,722,893; 6,039,650;
6,299,536 and 6,582,301 all incorporated herein by reference in
their entirety. A card shoe of this type provides one type of means
for signaling the beginning of a game. In one aspect, the dealer
presses a button on the card shoe 106, chip tray 120 or other
location to signal the start of the game to the computer. In
another aspect, the card shoe 106 can automatically signal the
computer 110 that a new game is beginning and can automatically
signal the computer when the game is completed. This can be
performed because the shoe tracks the cards dealt to the players
and the dealer, and the shoe knows when a player wins and loses
each hand and when the game is over.
[0137] The invention can be employed in combination with any
intelligent shoe to track the player's winnings and losses for the
casino. This can assist the casino in determining a player's true
worth to the casino. Moreover, since the invention includes a
player tracking card reader, the invention can transmit data to the
casino computer regarding the player's bet and wins and losses.
This way, the casino can track the player's wins and losses at many
games in the casino, and can accurately determine the player's true
worth.
[0138] F. Conclusion
[0139] The invention provides numerous aspects and advantages to a
card game with automatic bet recognition. Advantages of the
invention include the ability to identify chips wagered by a player
to automatically determine the player's bet.
[0140] Having disclosed exemplary embodiments and the best mode, it
will be understood by those skilled in the art that changes in form
and detail may be made therein without departing from the spirit
and scope of the invention.
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