Connected Component Algorithm for Gestures Recognition

This paper presents head and hand gestures recognition system for Human Computer Interaction (HCI). Head and Hand gestures are an important modality for human computer interaction. Vision based recognition system can give computers the capability of understanding and responding to the hand and head gestures. The aim of this paper is the proposal of real time vision system for its application within a multimedia interaction environment. This recognition system consists of four modules, i.e. capturing the image, image extraction, pattern matching and command determination. If hand and head gestures are shown in front of the camera, hardware will perform respective action. Gestures are matched with the stored database of gestures using pattern matching. Corresponding to matched gesture, the hardware is moved in left, right, forward and backward directions. An algorithm for optimizing connected component in gesture recognition is proposed, which makes use of segmentation in two images. Connected component algorithm scans an image and group its pixels into component based on pixel connectivity i.e. all pixels in connected component share similar pixel intensity values and are in some way connected with each other. Once all groups have been determined, each pixel is labeled with a color according to component it was assigned to

3D hand posture recognition using multicam

This paper presents the hand posture recognition in 3D using the MultiCam, a monocular 2D/3D camera developed by Center of Sensorsystems (ZESS). The :VlultiCam is a camera which is capable to provide high resolution of color data acquired from CMOS sensors and low resolution of distance (or range) data calculated based on timeof- flight (ToF) technology using Photonic Mixer Device (PMD) sensors. The availability of the distance data allows the hand posture to be recognized in z-axis direction without complex computational algorithms which also enables the program to work in real-time processing as well as eliminates the background effectively. The hand posture recognition will employ a simple but robust algorithm by checking the number of fingers detected around virtually created circle centered at the Center of Mass (CoM) of the hand and therefore classifies the class associated with a particular hand posture. At the end of this paper, the technique that uses intersection between the circle and fingers as the method to classify the hand posture which entails the MultiCam capability is proposed. This technique is able to solve the problem of orientation, size and distance invariants by utilizing the distance data

Research on gesture recognition based on support vector machine

In response to the problems of low recognition accuracy and large computation of traditional neural network algorithms, a gesture recognition detection model is designed by using human skin color features and SVM model with gesture classification recognition as the target. The method adopts bilateral filtering and other graphical processing to smooth the edges of the palm, detect and binarize the skin color of the region, and filter the binarized image to smooth the edges. The skin color space is transferred from the RCB space to the YUV space under which the gesture region is separated from the background, and morphological processing techniques are introduced in terms of gesture integrity to effectively fill the black hole region and remove the white dot region in the gesture picture, and directly edge the gesture picture. After obtaining the standardized image, the feature values are obtained. The experimental results show that the method improves the accuracy of gesture recognition compared with traditional algorithms

Introdução à Análise de Movimento usando Visão Computacional

Pretende-se com este trabalho fazer uma introdução ao que tem vindo a ser realizado no domínio do seguimento e análise de movimento recorrendo a visão computacional.Assim no primeiro capítulo deste relatório faremos referência aos vários tipos de movimento e analisaremos as fases que compõem um sistema comum de captura e análise de movimento, descrevendo sucintamente alguns trabalhos realizados nesta área.Seguidamente, no segundo capítulo, faremos uma apresentação mais detalhada da área do seguimento e análise de movimento humano de corpo inteiro; nomeadamente, no reconhecimento da pose e do reconhecimento do andar e de gestos.Finalmente, no terceiro e último capítulo, daremos ênfase à análise de imagem médica e exemplificaremos, sumariamente, algumas das suas aplicações.With this work we intend to introduce what has been done in the domain of tracking and motion analysis by using computational vision.Therefore in the first chapter of this report we will refer the various types of motion, and analyse the steps that compose a general system of movement capture and analysis, by succinctly describing some works done in this field.Then, in the second chapter we will do a more detailed study about the area of human entire body tracking and motion analysis; namely, in pose recognition and in the recognition of gait and gestures.Finally, in the third and last chapter, emphasis will be given to the medical images analysis and we will summarily exemplify some of its applications

Информационная безопасность

В сборнике опубликованы материалы докладов, представленных на 59-й научной конференции аспирантов, магистрантов и студентов БГУИР. Материалы одобрены оргкомитетом и публикуются в авторской редакции. Для научных и инженерно-технических работников, преподавателей, аспирантов, магистрантов и студентов вузов