Object Recognition Through Artificial Intelligence Techniques
This paper describes a methodology for object recognition categorized as polyhedron and non-polyhedron. This recognition is achieved through digital image processing combined with artificial intelligence algorithms, such as Hopfield networks. The procedure consists of processing images in search of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad Pedagógica y Tecnológica de Colombia
2020-04-01
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| Series: | Revista Facultad de Ingeniería |
| Subjects: | |
| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10734 |
| _version_ | 1819086585504727040 |
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| author | José Luis Ramírez-Arias, Ph. D. Astrid Rubiano-Fonseca, Ph. D. Robinson Jiménez-Moreno, Ph. D. |
| author_facet | José Luis Ramírez-Arias, Ph. D. Astrid Rubiano-Fonseca, Ph. D. Robinson Jiménez-Moreno, Ph. D. |
| author_sort | José Luis Ramírez-Arias, Ph. D. |
| collection | DOAJ |
| description | This paper describes a methodology for object recognition categorized as polyhedron and non-polyhedron. This recognition is achieved through digital image processing combined with artificial intelligence algorithms, such as Hopfield networks. The procedure consists of processing images in search of patterns to train the system. The process is carried out through three stages: i) Segmentation, ii) Smart recognition, and iii) Feature extraction; as a result, images of objects are obtained and trained in the designed neuronal network. Finally, Hopfield's network is used to establish the object type as soon as it receives one. The proposed methodology was evaluated in a real environment with a considerable number of detected images; the noisy images recognition uncertainty was 2.6%, an acceptable result considering variable light, shape and color. The results obtained from this experiment show a high recognition level, which represents 97.4%. Out of this procedure, we can assume that it is possible to train new patterns, and it is expected that the model will be able to recognize them. Potentially, the proposed methodology could be used in a vast range of applications, such as object identification in industrial environments, grasping objects using manipulators or robotic arms, tools for blind patients, among other applications. |
| first_indexed | 2024-12-21T21:22:35Z |
| format | Article |
| id | doaj.art-896e687aa24a41a8a2cd8054f2d36ace |
| institution | Directory Open Access Journal |
| issn | 0121-1129 2357-5328 |
| language | English |
| last_indexed | 2024-12-21T21:22:35Z |
| publishDate | 2020-04-01 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | Article |
| series | Revista Facultad de Ingeniería |
| spelling | doaj.art-896e687aa24a41a8a2cd8054f2d36ace2022-12-21T18:49:50ZengUniversidad Pedagógica y Tecnológica de ColombiaRevista Facultad de Ingeniería0121-11292357-53282020-04-012954e10734e1073410.19053/01211129.v29.n54.2020.1073410734Object Recognition Through Artificial Intelligence TechniquesJosé Luis Ramírez-Arias, Ph. D.0Astrid Rubiano-Fonseca, Ph. D.1Robinson Jiménez-Moreno, Ph. D.2Universidad Militar Nueva GranadaUniversidad Militar Nueva GranadaUniversidad Militar Nueva GranadaThis paper describes a methodology for object recognition categorized as polyhedron and non-polyhedron. This recognition is achieved through digital image processing combined with artificial intelligence algorithms, such as Hopfield networks. The procedure consists of processing images in search of patterns to train the system. The process is carried out through three stages: i) Segmentation, ii) Smart recognition, and iii) Feature extraction; as a result, images of objects are obtained and trained in the designed neuronal network. Finally, Hopfield's network is used to establish the object type as soon as it receives one. The proposed methodology was evaluated in a real environment with a considerable number of detected images; the noisy images recognition uncertainty was 2.6%, an acceptable result considering variable light, shape and color. The results obtained from this experiment show a high recognition level, which represents 97.4%. Out of this procedure, we can assume that it is possible to train new patterns, and it is expected that the model will be able to recognize them. Potentially, the proposed methodology could be used in a vast range of applications, such as object identification in industrial environments, grasping objects using manipulators or robotic arms, tools for blind patients, among other applications.https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10734objects recognition as of 2d imagesmorphologic operationsneuronal networkshopfield network |
| spellingShingle | José Luis Ramírez-Arias, Ph. D. Astrid Rubiano-Fonseca, Ph. D. Robinson Jiménez-Moreno, Ph. D. Object Recognition Through Artificial Intelligence Techniques Revista Facultad de Ingeniería objects recognition as of 2d images morphologic operations neuronal networks hopfield network |
| title | Object Recognition Through Artificial Intelligence Techniques |
| title_full | Object Recognition Through Artificial Intelligence Techniques |
| title_fullStr | Object Recognition Through Artificial Intelligence Techniques |
| title_full_unstemmed | Object Recognition Through Artificial Intelligence Techniques |
| title_short | Object Recognition Through Artificial Intelligence Techniques |
| title_sort | object recognition through artificial intelligence techniques |
| topic | objects recognition as of 2d images morphologic operations neuronal networks hopfield network |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10734 |
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