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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Submodular Attribute Selection for Visual Recognition.

Jingjing Zheng, Zhuolin Jiang, Rama Chellappa

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an attribute-based representation for visual recognition, enhancing image and video analysis. By selecting discriminative attributes, it improves recognition accuracy and outperforms existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Low-level features are insufficient for complex visual recognition tasks.
    • High-level semantic understanding is crucial for image and video analysis.

    Purpose of the Study:

    • To develop a discriminative and compact attribute-based representation for visual recognition.
    • To improve the performance of object and action recognition in images and videos.

    Main Methods:

    • Encoding objects/actions using human-generated and data-driven attributes.
    • Attribute selection using submodular optimization and a greedy algorithm.
    • Developing a (1-1/e)-approximation algorithm for attribute selection.

    Main Results:

    • The proposed attribute-based representation significantly enhances visual recognition performance.
    • The method outperforms several recently proposed recognition approaches.
    • Experimental validation on four public datasets confirms effectiveness.

    Conclusions:

    • Attribute-based representations offer a powerful approach to visual recognition.
    • Discriminative attribute selection is key to improving representation compactness and accuracy.
    • The proposed method provides a robust and efficient solution for real-world visual recognition challenges.