Titlebook: Computer Vision Metrics; Textbook Edition Scott Krig Textbook 20161st edition Springer International Publishing Switzerland 2016 Computer v

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https://doi.org/10.1007/978-3-030-02038-5 of ground truth data design and use, including manual and automated methods. We then propose a method and corresponding ground truth dataset for measuring interest point detector response as compared to human visual system response and human expectations. Also included here are example applications

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NoC-Aware Computational Sprintingion and imaging to simulate the biology and theories of the human visual system. The state of the art in computer vision is rapidly moving towards synthetic brains and synthetic vision systems, similar to other biological sciences where we see synthetic biology such as prosthetics, robotics, and gen

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https://doi.org/10.1007/978-3-319-33762-3Computer vision; Deep learning; Feature learning; Feature descriptors; Image processing; Computational im

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Joseph Baines,David Ravensbergen surface reconstruction. A high-level overview of selected topics is provided, with references for the interested reader to dig deeper. Readers with a strong background in the area of 2D and 3D imaging may benefit from a light reading of this chapter.

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