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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Temporally Coherent Video Harmonization Using Adversarial Networks.

Hao-Zhi Huang, Sen-Zhe Xu, Jun-Xiong Cai

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 24, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a novel adversarial network for video harmonization, enhancing realism and temporal consistency in composite videos. The method trains effectively on synthetic data, outperforming previous techniques.

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    Area of Science:

    • Computer Vision
    • Image and Video Processing
    • Artificial Intelligence

    Background:

    • Compositing is crucial for image and video editing, with harmonization aiming to improve realism.
    • Existing harmonization methods primarily focus on static images, leaving a gap in video processing.
    • Achieving temporal consistency in harmonized videos remains a significant challenge.

    Purpose of the Study:

    • To develop an effective method for video harmonization, addressing limitations of image-centric approaches.
    • To enhance the realism and temporal coherence of composited video sequences.
    • To reduce reliance on precise foreground masks in video harmonization.

    Main Methods:

    • A convolutional neural network trained adversarially for video harmonization.
    • Utilizing a pixel-wise disharmony discriminator to improve realism.
    • Incorporating a temporal loss function to ensure consistency across frames.
    • Proposing a synthetic dataset generation method for supervised training.

    Main Results:

    • The proposed method achieves more realistic harmonized video results.
    • Temporal consistency is successfully integrated into the harmonization process.
    • Training on the synthetic dataset generalizes well to real-world data.
    • The pixel-wise discriminator alleviates the need for input foreground masks.

    Conclusions:

    • The developed adversarial network effectively addresses video harmonization challenges.
    • The method significantly improves visual realism and temporal consistency in videos.
    • Synthetic data generation provides a viable solution for training robust video harmonization models.