Local stereo matching using adaptive local segmentation
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Local Stereo Matching Using Adaptive Local Segmentation. Sanja Damjanovi´c, Ferdinand van der Heijden, and Luuk J. Spreeuwers. International Scholarly Research Network (ISRN), May 2012. Outline. Introduction ( Related Work ) Proposed Algorithm Experimental Results Conclusion.

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Local stereo matching using adaptive local segmentation

Local Stereo Matching Using Adaptive Local Segmentation

Sanja Damjanovi´c, Ferdinand van der Heijden,

and Luuk J. Spreeuwers

International Scholarly Research Network (ISRN), May 2012


Outline
Outline

  • Introduction (Related Work)

  • Proposed Algorithm

  • Experimental Results

  • Conclusion



Introduction1
Introduction

  • Window-based matching produces an incorrect disparity map:

    • e.g. the discontinuities are smoothed

  • Related Works…

[21]

[21]K.-J. Yoon and I.-S. Kweon. Adaptive support-weight approach for correspondence search. PAMI, 2006.


Objective
Objective

  • Propose a local stereo matching framework:

    • Based on an adaptive local segmentation

    • robust against local radiometricaldifferences

    • successfully recovers disparities:

      • around the objects edges

      • of thin objects

      • of the occluded region


Proposed algorithm
ProposedAlgorithm



Preprocessing
Preprocessing

  • Goal: make the input image more suitable for adaptive local segmentation

  • Problems:

    • Noise : low-textured region (uniform region)

    • Sampling errors : high-textured region

  • Apply a nonlinear intensity transformation


Preprocessing1
Preprocessing

  • Transformation: based on the interpolated sub-pixel samples by bicubic transform in the 4 neighborhoods


Preprocessing2
Preprocessing

Before

Before - Detail

After - Detail


Adaptive local segmentation
Adaptive Local Segmentation

  • Goal: prevent that the matching region contains the pixels with

    significantly different disparities

  • Ideas:

    • Uniform areas : low threshold

    • Textured areas : high threshold

  • Using local intensity variation measure

    • determine the level of local texture


Adaptive local segmentation1
Adaptive Local Segmentation

  • local intensity variation :

    • Horizontal central difference:

    • Vertical central difference:

    • Intensity variation measure:

  • I(x, y − 1/2) and I(x, y + 1/2) : vertical half-pixel shifts of image I


Adaptive local segmentation2
Adaptive Local Segmentation

(low) red→ orange→ green→ blue (high)

Local intensity variation levels


Adaptive local segmentation3
Adaptive Local Segmentation

  • Dynamic threshold(Td) for each range by a look-up table:

    ‧ T : constant

  • If | center pixel(x,y) – neighbor pixel | < Td(x,y)

    • same segment (support region)


Adaptive local segmentation4

W

Adaptive Local Segmentation

W x W reference window

W

: adjacent pixel(gray value)

: central pixel(gray value)

: threshold

B (binary map)


Stereo correspondence cost aggregation
Stereo Correspondence (Cost/Aggregation)

  • (1) BlBr→ B

    • zl / zr: pixels from the left/right matching window (within B)

  • (2) Subtract the central pixel values cl and cr from vectors zl and zr


Stereo correspondence cost aggregation1
Stereo Correspondence (Cost/Aggregation)

  • (3) Eliminate the outliers

  • Sum of squared differences(SSD):

Np: the length of vectors zland zrfor disparity d.

Support region vector → zland zr


Hybrid winner take all
Hybrid Winner-take-all

  • Goal: consider only disparities supported by a sufficient number

  • Result of hybrid WTA:

number of pixels

disparity range

cost

threshold

: a set containing the number of pixels participating in the cost aggregation step

: threshold(, )


Postprocessing
Postprocessing

  • Goal:detect the disparity errors and correct them

  • Outliers:

    • Errors caused by low-textured areas larger than the initial window

    • Occlusion

  • Method:

    • Median filter

    • Histogram voting

    • Consistency check


Postprocessing1
Postprocessing

  • Histogram voting:

    • propagates disparities to the regions with close intensities

Threshold:


Postprocessing2
Postprocessing

repeated iteratively until there are no more updates to disparities in the map

  • Histogram voting

    • Counting the disparities along 8 radial directions:

    • Normalization:

    • New value:


Pre processing post processing
Pre-processing &Post-processing

none

post-processing

pre-processing

post-processing

+ pre-processing


Experimental results
ExperimentalResults


Experimental results1
Experimental Results

  • Parameters:

Rank:49


Experimental results2
Experimental Results

Left Image

Proposed

Error Map

Ground Truth



Conclusion1
Conclusion

  • Introduce a approach for stereo matching:

    • Based on the adaptive local segmentation

    • Pre-processing :

      • smootheslow-textured areas

      • Sharpens texture edges

    • Post-processing :

      • Detect and recovers occluded and unreliable disparities


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