MESA LAB Depth ordering Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation)...
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Transcript of MESA LAB Depth ordering Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation)...
MESA LABMESA LABMESA LABMESA LAB
Depth ordering Depth ordering
Guimei ZhangMESA MESA (Mechatronics, Embedded Systems and Automation)LABLAB
School of Engineering,University of California, Merced
E: [email protected] Phone:209-658-4838Lab: CAS Eng 820 (T: 228-4398)
Sep 22, 2014. Monday 4:00-6:00 PMApplied Fractional Calculus Workshop Series @ MESA Lab @ UCMerced
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Introduction
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What is depth ordering?
(a) Imput image (b) Edge image (b) Depth ordering
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(a) Imput image (b) Edge image
(c) Contour completion
(d) Image layer
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Applications (why to do this work?)Image segmentation
Object recognitionTarget trackingScene understanding
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Introduction
MESA LABMESA LABMESA LABMESA LABDepth ordering algorithm
based on T-junctions and occlusion reasoning
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1. Motivation
2. Method
3. Experiments
4. Conclusion
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1. Motivation
T-junction points
convexity
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1. Motivation
Problems:Existed methods have limitations to order
objects completely, especially in multiple backgrounds .
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• conventional methods always detect T-junctions before segmentation, which will result in detecting false T-junctions or missing real T-junctions in clutter images
1. Motivation
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2. Method
• overcomes the first problem by introducing high level occlusion reasoning theory when some regions include no T-junction, no convexity or inconsistent T-junction point
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2. Method
• We combine low level depth cue (T-junctions) and
high level occlusion reasoning, therefore make
progress to order the objects completely, even in
multiple backgrounds.
• In addition, conventional methods always detect T-
junctions before segmentation, which will result in
detecting false T-junction or missing real T-junctions in
clutter images.
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2. Method
Character 1: T-junction is composed by three boundaries and
only two boundaries are collinear, in other words, the angle
between them is 180 degree. Two collinear ones are named
as occlusion boundaries, and the other is called occluded
boundary.
Character 2: The region contained occlusion boundaries is in
front of the one included occluded boundary.
2.1 T-junction analysis: A B
C
O
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In previous work, T-junctions are detected before segmentation. The shortcomings of this kind of methods are as follows :
it is easy to detect false T-junctions due to the complexity of the real images and texture of some objects;
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2. Method
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preserve T-junctions before image segmentation and remove the false T-junctions, in other words the post-processing is time consuming
detection T-junction method based on image is more complex than one based on contour. So we first segment real image and get the contour of image, then detect T-junctions on the contour image.
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2. Method
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2. Method
• Detection T-junction points
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2. Mehtod2.2 occlusion reasoning
visual psychology principle:
The figure (foreground) has definition shape, but the
background has not, if the background is perceived as having
certain shape, that is due to the other gestalt.
The background seems continuous stretch without being
interrupted behind the figure.
The figure always appears in the front and the background is
in the back.
The figure can give human more deep impression, and easier
to remember.
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Reasoning laws( inspired by human cognition):
Law 1: If the background has not definition shape, the region
which has definition shape is in front of the one which has not.
Law 2: When the background has definition shape, we first
remove part objects formed the boundary of background, and can
get the region which has definition shape is in front of the one
which has not.
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2. Mehtod
A B
C D
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Law 3: The lower the background region in the image is more likely to be closer to viewpoint when there are multiple background regions in the scene.
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2. Mehtod
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the method is as follows:
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2. Method
MESA LABMESA LABMESA LABMESA LAB3. Experiments
First: input image
Sec: T-junction detection
Last: The depth map
( rendered as a gray level image, and high values
indicate regions closer to the viewpoint)
Experiment result
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3. ExperimentsComparison with the state of the art :
(a) input image (b) T-junction detection (c) The depth-map obtained by the
method in Ref [7] (d) The depth-map obtained by our method
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3. Experiments
(a) input image (b) segmentation (c) The depth-map obtained by the
method in Ref [8] (d) The depth-map obtained by our method
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3. Experiments
(a) input image (b) T-junction detection of our method (c) The depth-map obtained by our method (d) T-junction detection of Ref [6] (e) The depth-map obtained by the method in Ref [6]
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4. Conclusion
A new T-junctions detection method based on contour is
proposed in this paper, which can accurately detect the T-
junctions on an already segmented image.
And Monocular depth ordering algorithm based on low
level depth cue (T-junctions) and high level occlusion
reasoning is proposed in this paper.
AFC Workshop Series @ MESALAB @ UCMerced09/22/2014
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The initial depth image ordering is first obtained based
on T-junction; and then more detail depth ordering can
be achieved by using of high level occlusion reasoning.
Results are compared with the method using depth cue
(T-junction and convexity) and the method optimization
algorithm based frameworks, our method can get the
perfect depth ordering, and can establish global and
consistent depth interpretation.
4. Conclusion
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