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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 restricted...

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Related Experiment Video

Updated: May 29, 2026

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

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

Published on: August 12, 2021

Computational experiments with a feature based stereo algorithm.

¹Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study refines the Marr-Poggio-Grimson computational model for stereo vision. The updated algorithm improves stereo matching performance on natural images, offering insights into artificial and biological vision systems.

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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

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Last Updated: May 29, 2026

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

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

Published on: August 12, 2021

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

Area of Science:

Computer Vision
Computational Neuroscience
Artificial Intelligence

Background:

The Marr-Poggio computational model (1977) established a framework for stereo vision by matching feature points across different image resolutions.
Subsequent psychophysical experiments and computational studies highlighted limitations and suggested modifications to the original algorithm.

Purpose of the Study:

To present an enhanced version of the Marr-Poggio-Grimson algorithm incorporating recent modifications.
To evaluate the performance of this refined algorithm on a diverse set of natural images, including aerial photographs.

Main Methods:

The study implements a modified Marr-Poggio-Grimson algorithm.
The algorithm utilizes difference-of-Gaussian filtering and hierarchical matching strategies.
Performance is tested on various natural image datasets.

Main Results:

The modified algorithm demonstrates improved stereo matching capabilities on natural images.
The hierarchical approach effectively restricts search spaces for finer resolution matching.
The refined model shows enhanced performance compared to earlier versions.

Conclusions:

The updated Marr-Poggio-Grimson algorithm offers a more robust approach to stereo vision processing.
This computational model provides valuable insights into the constraints of stereo systems.
The findings have implications for both biological and artificial vision research.