Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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TITLE: APPARATUS AND METHOD FOR AUTOMATIC REAL-TIME BI-LAYER
SEGMENTATION USING COLOR AND INFRARED IMAGES
INVENTOR:
Pierre Benoit Boulanger
TECHNICAL FIELD:
The present disclosure is related to the field of bi-layer video segmentation
of
foreground and background images using the fusion of self-registered color and
infrared
("IR") images, in particular, a sensor fusion based on a real-time
implementation of a
relaxation labelling algorithm computed on a graphic processing unit ("GPU")
in real-
time.
BACKGROUND:
Many tasks in computer vision involve bi-layer video segmentation. One
important application is in teleconferencing, where there is a need to
substitute the
original background with a new one. A large number of papers have been
published on
bi-layer video segmentation. For example, background subtraction techniques
try to
solve this problem by using adaptive thresholding with a background model [1].
One of the most well known techniques is chroma keying which uses blue or
green backgrounds to separate the foreground objects. Because of its low cost,
it is
heavily used in photography and cinema studios around the world. On the other
hand,
these techniques are difficult to implement in real office environment or
outdoors as the
segmentation results depend heavily on constant lighting and the access to a
blue or
green background. To remediate this problem, some techniques use learned
backgrounds using frames where the foreground object is not present. Again,
those
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techniques are plagued by ambient lighting fluctuations as well as by shadows.
Other
techniques perform segmentation based on stereo disparity map computed from
two or
more cameras [2, 3]. These methods have several limitations as they are not
robust to
illumination changes and scene features making dense stereo map difficult to
get in
most cases. They also have low computational efficiency and segmentation
accuracy.
Recently, several researchers have used active depth-cameras in combination
with a
regular camera to acquire depth data to assist in foreground segmentation [4,
5]. The
way they combine the two cameras, however, involves scaling, re-sampling and
dealing
with synchronization problems. There are some special video cameras available
today
that produce both depth and red-green-blue ("RGB") signals using time-of-
flight, e.g.
ZCam [6], but this is a very complex technology that requires the development
of new
miniaturise streak cameras which are hard to produce at low cost.
It is, therefore, desirable to provide a system and method for the bi-layer
video
segmentation of foreground and background images that overcomes the
shortcomings
in the prior art.
SUMMARY:
A new solution to the problem of bi-layer video segmentation is provided in
terms
of both hardware design and in the algorithmic solution. At the data
acquisition stage,
infrared video can be used, which is robust to illumination changes and
provides an
automatic initialization of a bitmap for foreground-background segmentation. A
contrast-
preserving relaxation labeling algorithm can then be used to finalize the
segmentation
process using the color information. The parallel characteristics of this new
relaxation-
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labelling algorithm can be implemented on a Graphic Processing Unit ("GPU")
hardware
to achieve real-time (30 Hz) performance on commodity hardware.
Broadly stated, an apparatus is provided for automatic real-time bi-layer
segmentation of foreground and background images, comprising: an infrared
("IR") light
source configured to illuminate an object with IR light, the object located in
a foreground
portion of an image, the image further comprising a background portion; a
color camera
configured to produce a color video signal; an IR camera configured to produce
an
infrared video signal; a beam splitter operatively disposed between the color
camera
and the IR camera whereby a portion of light reflecting off of the object
passes through
the beam splitter to the color camera and another portion of light reflecting
off of the
object reflects off of the beam splitter and passes through an interference
filter to the IR
camera, the interference filter configured to allow IR light to pass through;
and a video
processor operatively coupled to the color camera and the IR camera and
configured to
receive the color video signal and the IR video signal, the video processor
further
comprising algorithm means configured to enable the video processor to process
the
color and IR video signals to separate the foreground portion from the
background
portion of the image and produce an output video signal that contains only the
foreground portion of the image.
Broadly stated, a method is provided for a method for the automatic real-time
bi-
layer segmentation of foreground and background images, the method comprising
the
steps of: illuminating an object in an image with infrared and visible light,
the image
further comprising a foreground portion and a background portion; producing a
color
video signal of the image; producing an infrared video signal of the image;
processing
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the color video signal to produce a color image array comprising a plurality
of color
vectors, each color vector representing a color pixel in a color image of the
image;
processing the infrared video signal to produce an infrared image array
comprising a
plurality of infrared components, each infrared component representing a gray
scale
value of an infrared pixel in an infrared image of the image; comparing the
infrared
image array against a predetermined threshold to produce an infrared mask to
predict
the foreground and background portions of the image; using the color image
array,
producing a pentamap to partition the color image into a plurality of regions;
applying a
Gaussian Mixture Model algorithm to the infrared mask and the pentamap to
determine
an initial segmentation of the foreground and background portions of the
image;
applying an iterative relaxation labelling algorithm to the infrared mask and
the
pentamap until a compatibility coefficient between adjacent pixels in the
image are
equal to or less than a predetermined threshold thereby finalizing the
definition of the
foreground and background portions of the image; and smoothing the transition
between the foreground and background portions of the image.
Broadly stated, an apparatus is provided for automatic real-time bi-layer
segmentation of foreground and background images, comprising: means for
illuminating
an object in an image with infrared and visible light, the image further
comprising a
foreground portion and a background portion; means for producing a color video
signal
of the image; means for producing an infrared video signal of the image; means
for
processing the color video signal to produce a color image array comprising a
plurality
of color vectors, each color vector representing a color pixel in a color
image of the
image; means for processing the infrared video signal to produce an infrared
image
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array comprising a plurality of infrared components, each infrared component
representing a gray scale value of an infrared pixel in an infrared image of
the image;
means for comparing the infrared image array against a predetermined threshold
to
produce an infrared mask to predict the foreground and background portions of
the
image; means for producing a pentamap using the color image array to partition
the
color image into a plurality of regions; means for applying a Gaussian Mixture
Model
algorithm to the infrared mask and the pentamap to determine an initial
segmentation of
the foreground and background portions of the image; means for applying an
iterative
relaxation labelling algorithm to the infrared mask and the pentamap until a
compatibility
coefficient between adjacent pixels in the image are equal to or less than a
predetermined threshold thereby finalizing the definition of the foreground
and
background portions of the image; and means for smoothing the transition
between the
foreground and background portions of the image.
BRIEF DESCRIPTION OF THE DRAWINGS:
Figure 1 is a block diagram depicting an apparatus for the bi-layer video
segmentation of foreground and background images.
Figure 2 is a pair of images depicting synchronized and registered infrared
and
color images.
Figure 3 is a pair of images depicting images as viewed through an infrared
mask
and a pentamap.
Figure 4 is a set of twelve images depicting an image converging to a final
image
after successive iterations of a relaxation algorithm applied to the image.
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Figure 5 is a pair of images depicting the effect of Gaussian border blending
applied to the image.
Figure 6 is a block diagram depicting a system for processing the bi-layer
video
segmentation of an image.
Figure 7 is a flowchart depicting a method for bi-layer video segmentation of
foreground and background images.
DETAILED DESCRIPTION OF EMBODIMENTS
Referring to Figure 1, a block diagram of an embodiment of a data acquisition
apparatus for a system for the bi-layer video segmentation of foreground and
background images is shown. In this embodiment, the foreground can be
illuminated by
an invisible infrared ("IR") light source having a wavelength ranging between
850 nm to
1500 nm that can be captured by an infrared camera tuned to the wavelength
selected,
using a narrow-band ( 25 nm) optical filter to reject all light except the
one produced by
the IR light source. In a representative embodiment, an 850 nm IR light source
can be
used but other embodiments can use other IR wavelengths as well known to those
skilled in the art, depending on the application requirements. The IR camera
and color
camera can produce a mirrored video pair that is synchronized both in time and
space,
using a genlock mechanism for temporal synchronization and an optical beam
splitter
for spatial registration. With this system, there is no need to align the
images using
complex calibration algorithms since they are guaranteed to be coplanar and
coaxial.
An example of a video frame captured by the apparatus of Figure 1 is shown in
Figure 2. As one can see, the IR image captured using the apparatus of Figure
1 is a
mirror version of the color image captured by this apparatus. This is due to
the
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reflection imparted on the IR image by reflecting off of the beam splitter.
The mirrored
IR image can be easily corrected using image transposition as well known to
those
skilled in the art.
In one embodiment, the apparatus can automatically produce synchronized IR
and color video pairs, which can reduce or eliminate problems arising from
synchronizing the IR and color images. In another embodiment, the IR
information
captured by the apparatus can be independent of illumination changes; hence, a
bitmap
of the foreground/background can be made to produce an initial image. In a
further
embodiment, the IR illuminator can add flexibility to the foreground
definition by moving
the IR illuminator around to any object to be segmented from the rest of the
image. In
so doing, the foreground can be defined by the object within certain distance
from the IR
illuminator rather than from the camera.
One advantage of the IR image is that it can be used to predict foreground and
background areas in the image. The IR image is a gray scale image, in which
brighter
parts indicate the foreground (as illuminated by IR source). Missing
foreground parts
must be within a certain distance from the illuminated parts.
For the foreground/background segmentation, in one embodiment, one can
determine a binary label vector:
f=(f1,f2...f1Pi),
where fn E {0,1 }, with 0 being the background label and 1 being the
foreground
label, and IPI being the number of pixels.
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After registration in the image plane of both the IR and color images, each IR
image can be represented as an array:
1=(11,I2...11PI),
and each color image can be represented as an array:
Z=(Z1,Z2...ZIP ),
where In is a gray scale value, and Zr, is an RGB color vector.
In one embodiment, an estimate of the foreground area can be found by
comparing the IR image against a predetermined threshold to produce an IR-MASK
defined as:
IR-MASK= {pixels) that the IR value Ip>T}
where T can be determined automatically using the Otsu algorithm [11].
Using this binary image, one can further define a pentamap P that can
partition
the color image into five regions. These regions can be:
1. certain foreground ("CF");
2. certain background ("CB");
3. local foreground ("LF");
4. local background ("LB"); and
5. uncertain region ("U").
In one embodiment, the pentamap can be formed from the initial segmentation
produced by the thresholding of the IR image, where the bitmap IR-MASK can be
processed by various morphological operators [10] such as erosion and
dilation.
In another embodiment, a pentamap can be composed of five binary maps:
P -{TCF, TCB, TLF, TLB}; with
1. TCF ={pixels I pixel that are member of IR-MASK.erosion(s)}
2. TLF ={pixels I pixel that are members of IR-MASK.xor. TCF}
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3. TCB ={pixels pixel that are members of not. (IR-MASK.dilation(r+s))}
4. TSB ={pixels I pixel that are members of IR-MASK.dilation(r+s).xor.IR-
MASK.dilation(T)}
5. Tu ={pixels I pixel that are members of P.xor. TCF xor. TCB xor. TLF xor.
TSB}
where s is the size of the structuring element for the foreground and r the
size of the
structuring element for the background.
Examples of the effect of the IR-MASK and the pentamap on an image are
shown at Figure 3. Sample foreground/background cues can be drawn from
TLF/TLB,
which is an interior/exterior narrow strip of width s of the foreground. In
one
embodiment, it can be assumed that the gray level pixels in the unknown area
Tu are
consistent with the neighbourhood regions. In this embodiment, the size of the
structuring element s (in the definition of TCB and TLB above) does not change
the
segmentation result much as long as se- [15, 25].
In this algorithm, a Gaussian Mixture Model ("GMM") can be used to represent
the foreground/background color model. Each GMM can be represented by a full-
covariance Gaussian mixture with M components (M=10). In one embodiment, the
GMM for the foreground can be represented as:
KF={KF1, KF2 . . . KFM};
and similarly for the background as:
KB={KB1 KB2... KBM}.
Following this initial segmentation, a relaxation labeling [7] can be used to
reduce
ambiguities and noise based on the parallel use of local constraints between
labels. In
this embodiment of implementation, each pixel can be first assigned a
probability vector
and a label based on the color information. The probability vector can be
updated
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iteratively based on the local constraints between labels [8]. The relaxation
labelling
process can be composed of four main steps:
Step 1: Initialization: For each pixel, compute a probability vector
Pro (P) = [Pr10 (P) , Pro' (P)],
where Pr1 is the probability of pixel p being the foreground (fp=1), and Pro
is the
probability (fp=0) being the background.
Based on the pentamap, the probability vector for pixels in the unknown area
Tu
can be defined according to the following scheme:
1 bPETCF,TLF
max Pr(Zp I KF)
0 i=1.. M
Pr1 (p) = max Pr(Zp I KFi) + max Pr(Zp I KB) )' P E TU (1)
i=1..M i=1..M
0 VPETCB,TLB
Pro0 (p) =1- Prl (p) (2)
Step 2: Iteration: At the nth iteration, the probability vector Pr(p) for
pixel p can be
updated based on the previous vector Pr"-'(p) and neighbourhood probability
vector:
Pr"-'(q), q c N(p)
where N(p) is the 8-connected neighbourhood about pixel p, and q is a
neighbouring
pixel to pixel p.
Prn (p) = Prin-1(p)(1 + Q,n-1(p))
1 1
LPrj n-1 (p)(1 + Q;n-1(p)) (3)
i=0
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where
Q,n-1(p) = 1 c(p, q) * Pr;"-' (q) (4)
card(N(p)) gEN(p)
with i={0,11.
C(p,q) is the compatibility coefficient between pixels p and its neighbour q
and can be
defined by:
1 iZZII2 < e
Qp,q) p q (5)
-1 otherwise
where Zp and Zq are the pixel color of neighbouring pixels p and q and 8 is
the
threshold on the difference.
For the purposes of this specification, the embodiments presented herein are
presumed to comprise a rectangular arrangement of pixels aligned both
vertically and
horizontally, and that the pixels comprise a rectangular or square
configuration. It is
obvious to those skilled in the art that other physical arrangements of pixels
that are not
aligned vertically or horizontally can be used with the apparatuses and
methods
described herein, as can pixels whose configuration is not rectangular or
square, and
that such arrangements and configurations are to be included with the
embodiments
discussed herein.
Step 3: Convergence and final labelling: After running for of iterations, each
pixel can
be assigned a label with a largest probability. As shown in Figure 4, one can
see the
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convergence rate of the pentamap as a function of the number of iteration. In
a
representative embodiment, we found of=10 to be sufficient in most cases.
Step 4: Border Blending: In one embodiment, border blending can begin with the
final
segmentation produced by the relaxation labeling algorithm. A Gaussian filter
or
matting can then be applied to the boundary contour, so that there is a smooth
transition
between foreground and background, eliminating obvious artifacts at the
boundaries
between the foreground and background portions of the image. Figure 5
illustrates the
segmentation results after border blending.
Referring to Figure 7, one embodiment of the method described herein can
include the following steps:
1. taking color and infrared images of an object in an image.
2. comparing the infrared image to a predetermined threshold to produce an
IR mask.
3. creating a tri-map of the color image to define the color image into three
distinct partitions or regions.
4. creating a pentamap from the tri-map to define the color image into five
distinct partitions or regions.
5. perform an iterative relaxation labeling algorithm to the pentamap until
there is convergence in defining the foreground and background portions
of the image, meaning, that a compatibility coefficient between adjacent
pixels in the image is equal to or less than a predetermined threshold.
6. perform a Gaussian filter or matting process to the converged image to
smooth the transition between the foreground and background portions of
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the image whereby a video image of the foreground portion can be
produced without the background portion thereby enabling the ability to
superimpose the foreground portion onto any desired background imagery
using video processing techniques well known to those skilled in the art.
Hardware Implementation
In order to provide real-time (30 Hz) performance on commodity computing, the
algorithm can be implemented using GPU processing. Figure 6 illustrates one
embodiment of an apparatus that can carry out the algorithm. The two cameras
(color
and IR) can be synchronized or "genlocked' together using the color camera as
the
source of a master clock. The video signals from the IR and color cameras can
then be
combined together using a side by side multiplexer to insure perfect
synchronization of
the frames of the two video signals. A high-speed digitizer can then convert
the video
signals from the multiplexer into digital form where each pixel of the
multiplexed video
signals can be converted into 24 bits integer corresponding to RGB. In the
case of the
IR signal, the integer can be set to be R=G=B. The digitizer can then directly
transfer
each digitized pixel into the main memory of a host computer using Direct
Memory
Access (DMA) transfer to obtain a frame transfer rate of at least 30Hz. The
CPU can
process each frame by separating the IR images from the color images and then
correct
the mirror transformation of the IR images through a transposition of the
image. The
GPU can then transfer the color and IR images through the PCI-E bus towards
the GPU
texture memory where the IR and color pixels can be processed using the
algorithm
described above. Since the GPU can be configured to operate much faster than
the
CPU, this processing can be performed at 120 Hz. The resulting segmented color
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image can then be transferred back to the system memory through the PCI-E bus.
The
CPU can then change the state of the device driver indicating to the
application program
that a new image is ready.
In one embodiment, the method described herein can be Microsoft DirectX
compatible, which can make the image transfer and processing directly
accessible to
various programs as a virtual camera. The concept of virtual camera can useful
as any
applications such as Skype, H323 video conferencing system or simply video
recording
utilities can connect to the camera as if it was a standard webcam.
Although a few embodiments have been shown and described, it will be
appreciated by those skilled in the art that various changes and modifications
might be
made without departing from the scope of the invention. The terms and
expressions
used in the preceding specification have been used herein as terms of
description and
not of limitation, and there is no intention in the use of such terms and
expressions of
excluding equivalents of the features shown and described or portions thereof,
it being
recognized that the scope of the invention is defined and limited only by the
claims that
follow.
References:
[1] N. Friedman, S. Russell, "Image Segmentation in Video Sequences: a
Probabilistic
Approach", Proc. 13th Conf, on Uncertainty in Artificial Intelligence, Aug
1997, pp. 175-
181.
[2] C. Eveland, K. Konolige, and R.C. Bolles, "Background modeling for
segmentation of
video-rate stereo sequences", Proc. IEEE Computer Vision and Pattern
Recognition
(CVPR), Santa Barbara, CA, USA, Jun 1998, pp. 266-271.
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[3] V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, "Bi-layer
Segmentation of Binocular video", Proc. CVPR, San Diego, CA, US, 2005, pp. 407
-
414.
[4] N. Santrac, G. Friedland, R. Rojas, "High resolution segmentation with a
time-of-
flight 3D-camera using the example of a lecture scene", Fachbereich mathematik
and
informatik, Sep 2006.
[5] O. Wang, J. Finger, Q. Yang, J. Davis, and R. Yang, "Automatic Natural
Video
Matting with Depth", Pacific Conference on Computer Graphics and Applications
(Pacific Graphics), 2007.
[6] G. Iddan and G. Yahav, "3D Imaging in the studio (and elsewhere)", Proc.
SPIE,
2001, pp. 48-55.
[7] R.A. Hummel and S.W. Zucker, "On the Foundations of Relaxation Labeling
Processes", IEEE Trans. Pattern Analysis and Machines Intelligence, May 1983,
pp.
267-287.
[8] M. W. Hansen and W. E. Higgins, "Relaxation Methods for Supervised Image
Segmentation", IEEE Trans. Pattern Analysis and Machine Intelligence, Sep
1997, pp.
949-962.
[9] Y. Boykov, and M.-P. Jolly, "Interactive graph cuts for optimal boundary
and region
segmentation of objects in N-D images", Proc. IEEE Int. Conf. on computer
vision,
2001, CD-ROM.
[10] http://en.wikipedia.org/wiki/Morphological image processing
[11] http://en.wikipedia.org/wiki/Otsu's method
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