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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
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Stereo Video Object Segmentation Using Stereoscopic Foreground Trajectories.

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    This study introduces an unsupervised stereo video segmentation method using stereoscopic trajectories. The novel approach enhances object detection and ensures consistent spatial-temporal segmentation for improved stereo experiences.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Multimedia Systems

    Background:

    • Stereo video segmentation is crucial for immersive 3D experiences.
    • Existing methods often struggle with long-term motion and inter-view consistency.
    • Unsupervised approaches are needed to reduce manual annotation efforts.

    Purpose of the Study:

    • To develop an unsupervised segmentation framework for stereo videos.
    • To leverage stereoscopic trajectories for improved motion modeling and object inference.
    • To ensure consistent spatial-temporal segmentation for enhanced stereo viewing.

    Main Methods:

    • Deriving stereo trajectories from left and right views using a graph structure.
    • Detecting object-like stereo trajectories for efficient object inference.
    • Employing an energy optimization function for final stereo segmentation using trajectory information.

    Main Results:

    • The proposed method achieves higher performance in stereo segmentation compared to existing approaches.
    • The framework effectively preserves stereo structures, contributing to better visual quality.
    • A new stereoscopic video benchmark with 50 challenging clips was created and released.

    Conclusions:

    • The unsupervised stereo segmentation framework using stereoscopic trajectories offers superior performance and structural preservation.
    • The method provides a robust solution for consistent spatial-temporal segmentation in stereo videos.
    • The released benchmark and code facilitate further research in stereo video analysis.