Sandra Nope

Modelo bio-inspirado para el reconocimiento de gestos usando primitivas de movimiento en visión

This article addresses the issue of gesture recognition using movement primitives to obtain a bio-model that, in a close future, can be used in the robot programming through the imitation learning paradigm. Those movement primitives are extracted from consecutive images caught by a standard web cam. For robot programming by imitation, gesture recognition was identified as first phase, which requires three main aspects to be taken into consideration. These are the instantaneous movement representation, the temporal integration of related information, and the classification strategy. These three aspects are going to be developed in this article and in contrast to other works in this field; the movement extraction and its codification are inspired in the macaco´s brain motion processing. The obtained model was applied then to the recognition of four different hand gestures performed by different people. The success percentage using different standard classification strategies varied between 91.42% and 97.14%.

Identifying facial gestures to emulate a mouse: navigation application on Facebook.

A system is presented for emulate a mouse from the movement of the head and eyelids. The position of the head is used for controlling the horizontal and vertical displacement of the cursor, and the closing of the eyelids to activate the click of the right and left buttons. The system includes zoom, navigation shortcuts, vertical scrollbars and menus activation according to the cursor position; that enhance the functionality and facilitate handling applications. The proposed solution eliminates the restriction of direct contact with the mouse, and empowers people with motor disabilities in the upper extremities to interact with the computer. The mouse emulator can also be used by people without limitations to expand command instructions. The system was tested in navigation on social network Facebook, where an average speed of 382 pixels / s was obtained, with an average accuracy of 22 pixels for the X axis and 17 pixels for the Y axis. However, after user interaction with the interface, improvements of 23% and 37% were observed, in execution time and location accuracy, respectively. Click activation by temporary location over a menu option had a performance of 100%; while the right and left clicks by eyelid closing had a performance of 93% and 92%, respectively. Finally, the surveys showed high satisfaction about the proposed interface during user interaction with Facebook.

Identifying facial gestures to emulate a mouse: Control application in a web browser

Computer interfaces continue using mouse and keyboard, although the form of human interaction is based primarily on exchange of audiovisual messages generated by the voice and body gestures in general. Because of the importance of gestures in human communication and the possibility for people with limited mobility of the upper limbs, this research focuses on the development of an interface to emulate the mouse and keyboard through facial gestures and head movement. The system was developed to interact with a web browser by processing and analysis of images provided by a webcam. The interface allows typing for text searching, move the mouse, click by closing one eye, open and navigate between tabs, vertical scroll, zoom, back and leave. Tests show a moving speed of cursor 411 px/s and a write speed of 3.5 characters/s, correct activation of right and left clicks 94% and 92% respectively. Finally, the survey showed a high user satisfaction while browsing on Internet.

Evaluación de técnicas diferenciales de primer orden para el cálculo de flujo óptico

Estimar el movimiento ha sido un asunto de gran interes en vision artificial. Quizas, el flujo optico es la tecnica mas usada en la estimacion del movimiento y ha sido usada con exito en multiples aplicaciones. Se presenta un estudio comparativo de algunas tecnicas diferenciales para el calculo de flujo optico. Se evalua la exactitud de dichas tecnicas usando imagenes sinteticas y reales. A traves de experimentaciones, se identifican heuristicamente algunas causas que afectan la estimacion del movimiento correcta. Este estudio fue motivado por la necesidad de estimar la informacion de movimiento para el reconocimiento e imitacion de gestos en robots articulados.