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Related Concept Videos

Stereoisomerism02:52

Stereoisomerism

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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Stereoisomerism of Cyclic Compounds02:33

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In this lesson, we delve into the role of ring conformation and its stability, which determines the spatial arrangement and, consequently, the molecular symmetry and stereoisomerism of cyclic compounds. 1,2-Dimethylcyclohexane is used as a case study to evaluate the possible number of stereoisomers. Here, given the multiple (n = 2) chiral centers, there are 2n = 4 possible configurations that lack a plane of symmetry, as the ring skeleton exists in a non-planar chair conformation. In addition,...
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Related Experiment Video

Updated: Jun 8, 2026

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Dictionary learning for stereo image representation.

Ivana Tošić1, Pascal Frossard

  • 1Signal Processing Laboratory LTS4, Ecole Polytechnique Fédérale de Lausanne, Switzerland. ivana@berkeley.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for learning dictionaries optimized for stereo image representation. The new approach enhances multi-view geometry and improves feature matching for tasks like camera pose estimation.

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Last Updated: Jun 8, 2026

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Multi-view imaging presents challenges in defining representations that capture intrinsic geometric information.
  • Sparse image representations using overcomplete geometric dictionaries can reveal multi-view geometric structure.
  • Selecting appropriate dictionaries for joint stereo image representation is non-trivial.

Purpose of the Study:

  • To propose a novel method for learning overcomplete dictionaries tailored for the joint representation of stereo images.
  • To develop a sparse stereo image model incorporating multi-view correlation via local geometric transforms.
  • To enhance multi-view feature matching and distributed scene representation.

Main Methods:

  • Formulation of a sparse stereo image model describing multi-view correlation.
  • Development of a maximum-likelihood (ML) method for stereo dictionary learning, including multi-view geometry constraints.
  • Optimization of the ML objective function using the expectation-maximization algorithm.
  • Application to omnidirectional images, learning scales of atoms in a parametric dictionary.

Main Results:

  • Learned dictionaries demonstrate superior performance in the joint representation of stereo omnidirectional images.
  • Significant improvements observed in multi-view feature matching accuracy.
  • Demonstrated benefits for distributed scene representation and camera pose estimation.

Conclusions:

  • The proposed dictionary learning method effectively captures joint stereo image geometry.
  • Optimized dictionaries lead to enhanced performance in multi-view imaging tasks.
  • This approach offers a robust framework for advanced scene understanding and camera localization.