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Related Concept Videos

Scaling01:26

Scaling

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Shape and Texture of Coarse Aggregate01:25

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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SARLBP: Scale Adaptive Robust Local Binary Patterns for Texture Representation.

Parth C Upadhyay, John A Lory, Guilherme N DeSouza

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
    Summary

    A new Scale Adaptive Robust Local Binary Pattern (SARLBP) descriptor improves texture classification by dynamically adapting scales to capture both micro and macro texture details, outperforming existing methods in noise and scale variations.

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    Area of Science:

    • Computer Vision
    • Pattern Recognition
    • Image Processing

    Background:

    • Local Binary Pattern (LBP) is effective for texture classification but sensitive to noise and scale variations.
    • Traditional LBP struggles to capture macro-structure information, limiting its robustness.

    Purpose of the Study:

    • To introduce a novel texture descriptor, Scale Adaptive Robust Local Binary Pattern (SARLBP), for enhanced texture classification.
    • To overcome the limitations of traditional LBP methods regarding noise, scale variations, and macro-structure information capture.

    Main Methods:

    • SARLBP dynamically determines an optimal scale for each radial direction based on local image characteristics.
    • It extracts four distinct patterns using regional image medians, optimized neighbors, fixed scale pixels, and radial differences.
    • The descriptor integrates micro and macro texture information through scale adaptation.

    Main Results:

    • SARLBP demonstrated superior performance in texture classification across multiple public databases (ALOT, CUReT, UMD, Kylberg).
    • The method showed significant robustness against Gaussian and Salt-and-Pepper noise.
    • SARLBP achieved better results than state-of-the-art LBP variants with a smaller feature dimension.

    Conclusions:

    • SARLBP provides a comprehensive and robust texture representation by effectively capturing information at multiple scales.
    • The proposed method offers improved accuracy and resilience to noise and scale variations compared to existing LBP techniques.
    • SARLBP presents a promising advancement for texture classification applications in computer vision.