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Related Concept Videos

Stereoisomers02:32

Stereoisomers

19.5K
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...
19.5K
Stereoisomerism02:52

Stereoisomerism

14.6K
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...
14.6K

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Related Experiment Video

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Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Photometric invariant stereo matching method.

Feifei Gu, Hong Zhao, Xiang Zhou

    Optics Express
    |December 25, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a robust stereo matching method to accurately estimate disparity maps, even with noisy images and varying lighting. The technique effectively filters noise and normalizes color for improved stereo vision performance.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Photogrammetry

    Background:

    • Stereo matching is crucial for 3D reconstruction.
    • Noise and photometric variations significantly degrade stereo matching accuracy.
    • Existing methods often struggle with complex real-world image conditions.

    Purpose of the Study:

    • To develop a robust stereo matching algorithm.
    • To improve disparity map estimation under noise and photometric variations.
    • To enhance the accuracy and reliability of stereo vision systems.

    Main Methods:

    • A comprehensive mathematical model of color formation.
    • Band-pass filtering with a DoP kernel for noise reduction.
    • Log-chromaticity normalization to handle illumination changes.
    • A specialized matching cost for disparity estimation.

    Main Results:

    • The proposed method demonstrates superior robustness against image noise.
    • Accurate disparity maps are generated despite significant photometric variations.
    • Performance is validated against state-of-the-art stereo matching algorithms.

    Conclusions:

    • The developed stereo matching method offers high accuracy and robustness.
    • It effectively addresses challenges posed by noise and illumination in stereo images.
    • The approach advances the capabilities of stereo vision applications.