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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Multistage branch-and-bound merging for planar surface segmentation in disparity space.

Ninad Thakoor1, Jean Gao, Venkat Devarajan

  • 1Electrical Engineering Department, University of Texas at Arlington, Arlington, TX 76010, USA. ninad.thakoor@uta.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 16, 2008
PubMed
Summary

This study introduces an iterative split-and-merge framework for segmenting planar surfaces in disparity images. The method models scenes with planar approximations, efficiently merging regions to accurately identify surfaces.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Stereo vision systems generate disparity images, which represent depth information.
  • Accurate segmentation of planar surfaces in disparity images is crucial for 3D scene reconstruction and analysis.
  • Existing methods may struggle with complex scenes or computational efficiency.

Purpose of the Study:

  • To develop an iterative split-and-merge framework for segmenting planar surfaces in disparity space.
  • To model scenes by approximating surfaces as planar and optimizing plane parameters.
  • To efficiently estimate the number and labeling of planar surfaces in stereo images.

Main Methods:

  • An iterative split-and-merge framework is proposed for disparity image segmentation.
  • The framework models scene disparity by approximating surfaces as planar.
  • A multistage branch-and-bound algorithm is employed for efficient optimization during merging.

Main Results:

  • The framework iteratively refines planar surface segmentation by splitting and merging regions.
  • The method minimizes residuals between actual and modeled disparity, optimizing plane parameters.
  • Experimental results demonstrate the framework's effectiveness on various stereo image datasets.

Conclusions:

  • The presented iterative split-and-merge framework provides an efficient method for planar surface segmentation in disparity images.
  • The approach effectively models scenes with planar approximations and optimizes surface identification.
  • The multistage branch-and-bound algorithm contributes to the computational efficiency of the segmentation process.