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


Introduction

09/222014 AFC Workshop Series @ MESALAB @ UCMerced

Slide-2/1024

What is depth ordering?

(a) Imput image (b) Edge image (b) Depth ordering

MESA LABMESA LABMESA LABMESA LABIntroduction

(a) Imput image (b) Edge image

(c) Contour completion

(d) Image layer


Applications (why to do this work?)Image segmentation

Object recognitionTarget trackingScene understanding

09/22/2014 AFC Workshop Series @ MESALAB @ UCMerced

Slide-4/1024

Introduction

MESA LABMESA LABMESA LABMESA LABDepth ordering algorithm

based on T-junctions and occlusion reasoning


Slide-5/1024

1. Motivation

2. Method

3. Experiments

4. Conclusion


1. Motivation

T-junction points

convexity




Slide-7/1024

1. Motivation

Problems:Existed methods have limitations to order

objects completely, especially in multiple backgrounds .


• 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


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


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.


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


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;


Slide-12/1024

2. Method


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.


Slide-13/1024

2. Method


2. Method

• Detection T-junction points



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.


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.


Slide-16/1024

2. Mehtod

A B

C D


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.


Slide-17/1024

2. Mehtod


the method is as follows:


Slide-18/1024

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

MESA LABMESA LABMESA LABMESA LAB3. Experiments

Experimental results


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


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


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]




Slide-24/1024


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


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



Thanks

AFC Workshop Series @ MESALAB @ UCMerced09/22/2014

MESA LAB Depth ordering Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation)...

Documents

Transcript of MESA LAB Depth ordering Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation)...