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Related Concept Videos

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Dense Stereo Matching Method Based on Local Affine Model.

Jie Li1, Wenxuan Shi, Dexiang Deng

  • 1School of Electronic Information, Wuhan University, Wuhan, China.

Journal of Computers
|October 29, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stereo matching method using a local affine model for accurate disparity map generation. The approach enhances cost aggregation and achieves subpixel precision, outperforming existing techniques.

Keywords:
Local affine modelZNCCedge-preservingocclusion handling

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Stereo matching is crucial for 3D scene reconstruction.
  • Traditional methods struggle with disparity discontinuities and efficiency.
  • Existing cost aggregation techniques have limitations in accuracy and detail preservation.

Purpose of the Study:

  • To propose a novel stereo matching method for accurate disparity space image construction.
  • To develop an efficient cost aggregation technique using a local affine model.
  • To achieve subpixel precision disparity maps while preserving discontinuities.

Main Methods:

  • A new self-adapting dissimilarity measurement for calculating matching costs.
  • A local affine model for efficient cost aggregation, preserving disparity discontinuities.
  • Extension of the local affine model to the color space for enhanced performance.

Main Results:

  • The proposed method constructs accurate disparity space images.
  • Efficient cost aggregation is achieved using the local affine model.
  • Experimental results show subpixel precision disparity maps compared to state-of-the-art methods.

Conclusions:

  • The local affine model offers an effective approach for stereo matching cost aggregation.
  • The method successfully preserves disparity discontinuities and achieves high accuracy.
  • This technique advances the state-of-the-art in generating precise subpixel disparity maps.