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Related Concept Videos

Stereoisomerism02:52

Stereoisomerism

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Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula.
Transition metal complexes often exist as geometric isomers, in which the same atoms are connected through the same types of bonds but with differences in their orientation in space. Coordination complexes with two different ligands in the cis and trans positions from a ligand of interest form isomers. For example, the octahedral [Co(NH3)4Cl2]+ ion has two isomers (Figure 1) In the cis...
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On the basis of mirror symmetry, stereoisomers of an organic molecule can be further classified into diastereomers and enantiomers. Diastereomers are stereoisomers that are not mirror images of each other. Substituted alkenes, such as the cis and trans isomers of 2-butene, are diastereomers, as these molecules exhibit different spatial orientations of their constituent atoms, are not mirror images of each other, and do not interconvert. Here, the interconversion is suppressed due to...
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Stereo Image Matching Using Adaptive Morphological Correlation.

Victor H Diaz-Ramirez1, Martin Gonzalez-Ruiz1, Vitaly Kober2,3

  • 1Instituto Politécnico Nacional-CITEDI, Instituto Politécnico Nacional 1310, Tijuana 22310, BC, Mexico.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive morphological correlation method for accurate stereo matching. It effectively identifies point correspondences in challenging image areas and recovers occluded points, improving stereo vision accuracy.

Keywords:
disparity estimationlocally adaptive image processingmorphological correlationstereo vision

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Area of Science:

  • Computer Vision
  • Image Processing
  • Stereo Vision

Background:

  • Accurate stereo matching is crucial for 3D reconstruction and scene understanding.
  • Traditional methods struggle with homogeneous regions and object edges.

Purpose of the Study:

  • To develop a robust stereo matching method using adaptive morphological correlation.
  • To accurately determine point correspondences in challenging image areas.
  • To recover occluded and unmatched points.

Main Methods:

  • Utilizes locally adaptive image windows for matching.
  • Employs a novel morphological correlation optimized by a binary dissimilarity-to-matching ratio criterion.
  • Incorporates a simple post-processing step for occluded point recovery.

Main Results:

  • Achieves high accuracy in determining point correspondences, even in homogeneous regions and at object edges.
  • Successfully recovers unknown correspondences for occluded and unmatched points.
  • Demonstrates superior performance compared to two state-of-the-art methods through objective measures.

Conclusions:

  • The proposed adaptive morphological correlation method offers a significant advancement in stereo matching accuracy.
  • The technique is effective for both standard and challenging stereo image scenarios.
  • The method provides a robust solution for 3D computer vision applications.