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Related Concept Videos

Stereoisomers02:32

Stereoisomers

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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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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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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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Stereoisomerism of Cyclic Compounds02:33

Stereoisomerism of Cyclic Compounds

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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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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Stereo Matching Using Tree Filtering.

Qingxiong Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel non-local cost aggregation method for stereo correspondence, improving depth edge preservation and temporal coherence in computer vision. The new approach enhances disparity accuracy compared to traditional local methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Traditional stereo correspondence methods use local cost aggregation, which is suboptimal and computationally dependent on region size.
    • Existing local methods struggle to preserve depth discontinuities and temporal coherence.

    Purpose of the Study:

    • To re-examine the cost aggregation problem and propose a novel non-local solution.
    • To improve depth edge preservation and temporal coherence in stereo vision.
    • To develop an advanced non-local weighted median filter for disparity refinement.

    Main Methods:

    • A non-local cost aggregation algorithm is proposed, utilizing a tree structure derived from stereo image pairs.
    • Pixel similarity is adaptively used to aggregate matching costs, preserving depth edges.
    • The method is extended to the time domain for temporal coherence and a non-local weighted median filter is introduced.

    Main Results:

    • The non-local approach aggregates matching costs adaptively based on pixel similarity on a tree structure.
    • Depth edges are preserved effectively due to the non-local aggregation and tree-based similarity.
    • The proposed non-local weighted median filter outperforms local filters in disparity/depth upsampling and refinement.

    Conclusions:

    • The proposed non-local cost aggregation method significantly enhances stereo correspondence by preserving depth edges.
    • Extending the method to the time domain ensures temporal coherence across video frames.
    • The non-local weighted median filter offers superior performance for disparity refinement tasks.