| dc.contributor.advisor | Becerra-López, Fernando I. | |
| dc.contributor.author | Jayakumar, Abinaya | |
| dc.date.accessioned | 2021-08-26T20:32:26Z | |
| dc.date.accessioned | 2023-03-10T18:11:41Z | |
| dc.date.available | 2021-08-26T20:32:26Z | |
| dc.date.available | 2023-03-10T18:11:41Z | |
| dc.date.issued | 2021-05 | |
| dc.identifier.citation | Jayakumar, A. (2021). Deep Learning Technique for Image Classification by Segmentation. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO. | es_MX |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/71331 | |
| dc.description | Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods that would be impossible by analogue means. In particular, digital image processing is a concrete application of, and a practical technology based on classification, localization, feature extraction and segmentation. The main objective is to understand the following challenges and identify a solution. | es_MX |
| dc.language.iso | eng | es_MX |
| dc.publisher | ITESO | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf | es_MX |
| dc.subject | Deep Learning | es_MX |
| dc.subject | Image Classification | es_MX |
| dc.subject | Image Segmentation | es_MX |
| dc.title | Deep Learning Technique for Image Classification by Segmentation | es_MX |
| dc.type | info:eu-repo/semantics/masterThesis | es_MX |
