Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
1999-01-13
2003-01-07
Werner, Brian (Department: 2621)
Image analysis
Applications
Target tracking or detecting
C382S118000, C382S164000, C382S299000
Reexamination Certificate
active
06504942
ABSTRACT:
The present invention relates to a method of and an apparatus for detecting a face-like region of a colour image. Such a method may be used in association with other methods for detecting a face in an image and for capturing a target image, for instance during the initialisation stage of an image tracking system which may be associated with an observer tracking autostereoscopic display. Such methods and apparatuses have a wide range of applications, for instance in skin colour detection, face detection and recognition, security surveillance, video and image compression, video conferencing, multimedia database searching and computer games.
The present invention also relates to an observer tracking display, for instance of the autostereoscopic type.
Autostereoscopic displays enable a viewer to see two separate images forming a stereoscopic pair by viewing such displays with the eyes in two viewing windows. Examples of such displays are disclosed in EP 0 602 934, EP 0 656 555, EP 0 708 351, EP 0 726 482 and EP 0 829 743. An example of a known type of observer tracking autostereoscopic display is illustrated in
FIG. 1
of the accompanying drawings.
The display comprises a display system
1
co-operating with a tracking system
2
. The tracking system
2
comprises a tracking system
3
which supplies a sensor signal to a tracking processor
4
. The tracking processor
4
derives from the sensor signal an observer position data signal which is supplied to a display control processor
5
of the display system
1
. The processor
5
converts the position data signal into a window steering signal and supplies this to a steering mechanism
6
of a tracked 3D display
7
. The viewing windows for the eyes of the observer are thus steered so as to follow movement of the head of the observer and, within the working range, to maintain the eyes of the observer in the appropriate viewing windows. EP 0 877 274 and GB 2 324 428 disclose an observer video tracking system which has a short latency time, a high update frequency and adequate measurement accuracy for observer tracking autostereoscopic displays.
FIG. 2
of the accompanying drawings illustrates an example of the system, which differs from that shown in
FIG. 1
of the accompanying drawings in that the tracking system
3
comprises a Sony XC999 NTSC video camera operating at a 60 Hz field rate and the tracking processor
4
is provided with a mouse
8
and comprises a Silicon Graphics entry level machine of the Indy series equipped with an R4400 processor operating at 150 Mhz and a video digitiser and frame store having a resolution of 640×240 picture elements (pixels) for each field captured by the camera
3
. The camera
3
is disposed on top of the display
7
and points towards the observer who sits in front of the display. The normal distance between the observer and the camera
3
is about 0.85 meters, at which distance the observer has a freedom of movement in the lateral or X direction of about 450 mm. The distance between two pixels in the image formed by the camera corresponds to about 0.67 and 1.21 mm in the X and Y directions, respectively. The Y resolution is halved because each interlaced field is used individually.
FIG. 3
of the accompanying drawings illustrates in general terms the tracking method performed by the processor
4
. The method comprises an initialisation stage
9
followed by a tracking stage
10
. During the initialisation stage
9
, a target image or “template” is captured by storing a portion of an image from the camera
3
. The target image generally contains the observer eye region as illustrated at
11
in
FIG. 4
of the accompanying drawings. Once the target image or template
11
has been successfully captured, observer tracking is performed in the tracking stage
10
.
A global target or template search is performed at
12
so as to detect the position of the target image within the whole image produced by the camera
3
. Once the target image has been located, motion detection is performed at
13
after which a local target or template search is performed at
14
. The template matching
12
and
14
are performed by cross-correlating the target image in the template with each sub-section overlaid by the template. The best correlation value is compared with a predetermined threshold to check whether tracking has been lost in step
15
. If so, control returns to the global template matching step
12
. Otherwise, control returns to the step
13
.
The motion detection
13
and the local template matching
14
form a tracking loop which is performed for as long as tracking is maintained. The motion detection step supplies position data by a differential method which determines the movement of the target image between consecutive fields and adds this to the position found by local template matching in the preceding step for the earlier field.
The initialisation stage
9
obtains a target image or a template of the observer before tracking starts. The initialisation stage disclosed in EP 0 877 274 and GB 2 324 428 uses an interactive method in which the display
7
displays the incoming video images and an image generator, for example embodied in the processor
4
, generates a border image or graphical guide
16
on the display as illustrated in
FIG. 5
of the accompanying drawings. A user-operable control, for instance forming part of the mouse
8
, allows manual actuation of capturing of the image region within the border image.
The observer views his own image on the display
7
together with the border image which is of the required template size. The observer aligns the midpoint between his eyes with the middle line of the graphical guide
16
and then activates the system to capture the template, for instance by pressing a mouse button or a keyboard key. Alternatively, this alignment may be achieved by dragging the graphical guide
16
to the desired place using the mouse
8
.
An advantage of such an interactive template capturing technique is that the observer is able to select the template with acceptable alignment accuracy. This involves the recognition of the human face and the selection of the interesting image regions, such as the eye regions. Whereas human vision renders this process trivial, such template capture would be difficult for a computer, given all possible types of people with different age, sex, eye shape and skin colour under various lighting conditions.
Suwa et al, “A Video Quality Improvement Technique for Video Phone and Video Conference Terminal”, IEEE Workshop on Visual Signal Processing and Communications, Sep. 21-22 1993, Melbourne, Australia disclose a technique for detecting a facial region based on a statistical model of skin colour. This technique assumes that the colour and brightness in the facial region lie within a defined domain and the face will occupy a predetermined amount of space in a video frame. By searching for a colour region which consists of image pixels whose colours are within the domain and whose size is within a known size, a face region may be located. However, the colour space domain for the skin colour changes with changes in lighting source, direction and intensity. The colour space also varies for different skin colours. Accordingly, this technique requires calibration of the skin colour space for each particular application and system and is thus of limited applicability.
Swain et al, “Color Indexing”, International Journal of Computer Vision, 7:1, pages 11 to 32, 1991 disclose the use of colour histograms of multicoloured objects to provide colour indexing in a large database of models. A technique known as “histogram back projection” is then use to locate the position of a known object such as a facial region, for instance as disclosed by Sako et al, “Real-Time Facial-Feature Tracking based on Matching Techniques and its Applications”, proceedings of 12 IAPR International Conference on Patent Recognition, Jerusalem, Oct. 6-13 1994, vol II, pages 320 to 324. However, this technique requires knowledge of the desired target, such as
Ezra David
Holliman Nicolas Steven
Hong Qi He
Renner Otto Boisselle & Sklar
Sharp Kabushiki Kaisha
Werner Brian
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