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Fractional stereo matching using expectation-maximization.

Wei Xiong¹, Hin Shun Chung, Jiaya Jia

¹The Chinese University of Hong Kong, Hong Kong. wayne.xiong@hotmail.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 17, 2009

Summary

This summary is machine-generated.

This study introduces a new probabilistic framework for fractional stereo matching, addressing challenges with blended foreground objects and varying transparencies. The method effectively computes alpha values for image matting, improving stereo matching accuracy.

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Area of Science:

Computer Vision
Image Processing
Computational Photography

Background:

Traditional stereo matching assumes color constancy, which fails with fractional boundaries and unknown transparencies.
Spatially varying disparities in layered images cause foreground pixels to blend with different background pixels.

Purpose of the Study:

To develop a probabilistic framework for fractional stereo matching that accounts for unknown transparencies and spatially varying disparities.
To propose an automatic optimization method for solving the Maximizing a Posterior (MAP) problem using Expectation-Maximization (EM).

Main Methods:

Introduced a probabilistic framework constraining pixel colors, disparities, and alpha values across different layers.
Proposed an automatic Expectation-Maximization (EM) optimization method for MAP estimation.
Encoded background occlusion effects via layer blending without explicit detection.

Main Results:

Successfully handled fractional boundaries and unknown transparencies in stereo matching.
Alpha computation served as a novel approach to natural image matting, especially when foreground and background colors are similar.
Demonstrated efficacy on challenging stereo images compared to state-of-the-art methods.

Conclusions:

The proposed probabilistic framework and EM optimization effectively solve fractional stereo matching problems.
The method provides a robust approach to image matting and handles background occlusion naturally.
Achieved state-of-the-art performance on challenging stereo image datasets.