GRAPH CUTS THEORETIC CONCEPTS WITH DIGITAL SIGNAL PROCESSING: A REVIEW
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| Publicado en: | NeuroQuantology vol. 20, no. 10 (2022), p. 4269 |
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NeuroQuantology
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 1303-5150 | ||
| 024 | 7 | |a 10.14704/nq.2022.20.10.NQ55414 |2 doi | |
| 035 | |a 2901928669 | ||
| 045 | 2 | |b d20220101 |b d20221231 | |
| 084 | |a 253831 |2 nlm | ||
| 100 | 1 | |a Dayanand | |
| 245 | 1 | |a GRAPH CUTS THEORETIC CONCEPTS WITH DIGITAL SIGNAL PROCESSING: A REVIEW | |
| 260 | |b NeuroQuantology |c 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Vision and graphics researchers continue to explore graph cuts as a topic of study in their respective fields. Some appealing links among graph cuts, those certain combinatorial algorithms (dynamic programming, direct paths) and Markov random fields have been discovered or rediscoveries in recent years. Another relationship is between sub modular functions and electric circuit theory, Bayesian networks and belief propagation, and essential geometry and anisotropic diffusion. | |
| 653 | |a Algorithms | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Circuits | ||
| 653 | |a Fields (mathematics) | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Digital signal processing | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Signal processing | ||
| 700 | 1 | |a Jain, Arihant | |
| 773 | 0 | |t NeuroQuantology |g vol. 20, no. 10 (2022), p. 4269 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2901928669/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2901928669/fulltextPDF/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |