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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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A Quad Edge-Based Grid Encoding Model for Content-Aware Image Retargeting.

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    This summary is machine-generated.

    This study introduces a novel quad edge-based grid encoding model for content-aware image retargeting. This new method optimizes image grids for better visual results, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computer Graphics

    Background:

    • Traditional image retargeting methods often struggle with preserving image content and structure.
    • Existing grid encoding models (vertex-based, axis-aligned) have limitations in flexibility and structural preservation.

    Purpose of the Study:

    • To present a novel quad edge-based grid encoding model for content-aware image retargeting.
    • To improve the visual quality and structural integrity of retargeted images.

    Main Methods:

    • Developed a difference-based encoding scheme where distances between vertices are optimization variables.
    • Formulated the grid optimization as a convex quadratic program.
    • Proposed three quadratic objective functions guiding grid deformation for visually comfortable results.

    Main Results:

    • The quad edge-based model ensures global convexity and allows locally adaptive spatial regularizations.
    • The proposed method effectively preserves salient regions and structural components.
    • Comparative evaluations show superior qualitative and quantitative performance against ten state-of-the-art methods.

    Conclusions:

    • The novel quad edge-based grid encoding model offers significant advantages for image retargeting.
    • This approach provides a robust and efficient solution for content-aware image resizing.
    • The method demonstrates state-of-the-art performance in preserving image quality and structure.