Entropy in Image Analysis III
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (230 p.) |
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| 245 | 1 | 0 | |a Entropy in Image Analysis III |
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| 520 | |a Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future. | ||
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| 653 | |a Jaccard similarity | ||
| 653 | |a multi-person counting | ||
| 653 | |a particles gradient motion (PGM) | ||
| 653 | |a speeded up robust features (SURF) | ||
| 653 | |a Retinex | ||
| 653 | |a image enhancement | ||
| 653 | |a gamma correction | ||
| 653 | |a low-light image | ||
| 653 | |a HSV color space | ||
| 653 | |a scan route | ||
| 653 | |a Hilbert curve | ||
| 653 | |a run-length-based entropy coding | ||
| 653 | |a image and video compression | ||
| 653 | |a secure communication | ||
| 653 | |a cellular neural network | ||
| 653 | |a power-divergence measure | ||
| 653 | |a computed tomography | ||
| 653 | |a iterative reconstruction | ||
| 653 | |a maximum-likelihood expectation-maximization method | ||
| 653 | |a continuous-time image reconstruction | ||
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