dc.contributor.advisor | Iregui Guerrero, Marcela | spa |
dc.contributor.author | Torres Arboleda, Cristhian David | |
dc.coverage.spatial | Calle 100 | spa |
dc.date.accessioned | 2015-10-08T13:36:00Z | |
dc.date.accessioned | 2019-12-26T22:05:14Z | |
dc.date.available | 2015-10-08T13:36:00Z | |
dc.date.available | 2019-12-26T22:05:14Z | |
dc.date.issued | 2015-08-28 | |
dc.identifier.uri | http://hdl.handle.net/10654/6695 | |
dc.description.abstract | El reconocimiento de gestos se ha presentado como una alternativa para la implementación de sistemas de interacción eficaces. Particularmente las aplicaciones basadas en visión artificial poseen ventajas en potabilidad frente a otras alternativas. Sin embargo, los algoritmos suelen requerir entrenamiento computacional intensivo, siendo difíciles de implementar en dispositivos móviles. En este artículo se realiza un estudio preliminar para la detección de poses de la mano usando un algoritmo basado en Patrones Binarios locales, más conocido por su sigla en inglés LBP (Local Binary Patterns). Para lo anterior, se presenta un modelo heurístico de división de la mano en regiones ponderadas diferencialmente, que permite la clasificación directa de los gestos usando una medida de similitud. Los pesos y la distribución de
regiones se evaluaron de acuerdo a su precisión en la clasificación de cada pose de la mano. Estas pruebas se realizaron en un conjunto de imágenes, capturadas en condiciones controladas, correspondientes a cinco poses diferentes. El algoritmo propuesto con el esquema de regiones ponderadas muestra una buena capacidad de discriminación y presenta una alternativa válida para futuras aplicaciones. | spa |
dc.description.sponsorship | Universidad Militar Nueva Granada | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | spa | spa |
dc.title | Deteccion de poses de las manos usando descriptores LBP | spa |
dc.type | info:eu-repo/semantics/bachelorThesis | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.subject.lemb | SISTEMAS MULTIMEDIA | spa |
dc.subject.lemb | MULTIMEDIA POR COMPUTADOR | spa |
dc.publisher.department | Facultad de Ingeniería | spa |
dc.type.local | Trabajo de grado | spa |
dc.description.abstractenglish | The gesture recognition has been presented as an alternative to the implementation of effective systems of interaction. Particularly based in machine vision applications have advantages over other alternatives potability. However, computational algorithms often require intensive training, being difficult to implement on mobile devices. In this paper, a preliminary study to detect hand poses using local binary patterns based algorithm, better known by its acronym LBP (Local Binary Patterns) is performed. For this, a heuristic model of division hand in regions differentially weighted, which allows direct classification of gestures using a similarity measure is presented. The weights and distribution regions were evaluated according to their classification accuracy of each hand pose. These tests were performed on a set of images taken in controlled conditions corresponding to five different poses. The proposed scheme regions with weighted algorithm shows a good ability to discriminate and presents a viable alternative for future applications. | eng |
dc.title.translated | Detection poses of hands using LBP descriptors | spa |
dc.subject.keywords | Local binary Pattern | spa |
dc.subject.keywords | human computer interaction | spa |
dc.subject.keywords | computer vision | spa |
dc.subject.keywords | detect hand poses | spa |
dc.subject.keywords | HCI | spa |
dc.subject.keywords | LBP | spa |
dc.subject.keywords | pose | spa |
dc.publisher.program | Ingeniería Multimedia | spa |
dc.creator.degreename | Ingeniero Multimedia | spa |
dc.description.degreelevel | Pregrado | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.relation.references | Meenakshi Panwar and Pawan Singh Mehra, “Hand Gesture Recognition for Human Computer Interaction“. | spa |
dc.relation.references | Timo Ahonen, Abdenour Hadid and Matti Pietik¨ainen, “Face Description with Local Binary Patterns: Application to Face Recognition”. | spa |
dc.relation.references | Youdong Ding, Haibo Pang, Xuechun Wu and Jianliang Lan, “Recognition of hand-gestures using improved local binary pattern”. | spa |
dc.relation.references | Paulo Trigueiros, Fernando Ribeiro and Lu´ıs Reis, “A Comparative Study of Different Image Features for Hand Gesture Machine Learning” | spa |
dc.relation.references | Vladimir I. Pavlovic, Rajeev Sharma and Thomas S. Huang, “Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review” | spa |
dc.relation.references | Ankit Chaudhary, J. L. Raheja, Karen Das and Sonia Raheja, “Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey” | spa |
dc.relation.references | Bin Xiao, Xiang-min Xu and Qian-pei Mai, “Real-Time Hand Detection and Tracking Using LBP Features”. | spa |
dc.subject.proposal | Local binary Pattern | spa |
dc.subject.proposal | interacción humano computador | spa |
dc.subject.proposal | LBP | spa |
dc.subject.proposal | HCI | spa |
dc.subject.proposal | visión por computador | spa |
dc.subject.proposal | pose | spa |
dc.subject.proposal | reconocimiento de gestos de las manos | spa |
dc.publisher.grantor | Universidad Militar Nueva Granada | spa |