Classifying via hierarchical temporal memory
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| Autori: | , |
|---|---|
| Natura: | artículo |
| Data di pubblicazione: | 2019 |
| Descrizione: | With recently advances in technology (hardware and software) there is an interest of humanity in having machines that behave like humans do. One aspect that researchers have to overcome is how to imitate the cognitive processes of the brain; cognitive processes like visual pattern recognition, speech recognition, space comprehension and so on. This task needs an algorithm that receives raw information from the environment, thus a signal processing method is needed to convert the raw input into useful information. Computer Vision is an interesting field of research, because the process of capturing images is simple and the hardware to process these images is available with current technology. This research focuses on the field of classifying images using hierarchical temporal memory (HTM), a machine learning technique that imitates the neocortex and emulates cognitive processes. |
| Stato: | Kérwá |
| Istituzione: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lingua: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/104033 |
| Accesso online: | https://revistas.tec.ac.cr/index.php/memorias/article/view/4549 https://hdl.handle.net/10669/104033 |
| Keyword: | ALGORITHM PATTERN RECOGNITION COMPUTER VISION |