0000000000341519
AUTHOR
Nicolas Malasne
<title>Architecture for real-time wood inspection</title>
This study has been realized to improve industrial machines that allow to analyze planks by detecting their width and too important defects thanks to a computer vision system. These machines are currently piloted by software with the help of PCs. The aim of our work is to realize a hardware card to increase the processing speed.
Face tracking and recognition: from algorithm to implementation
This paper describes a system capable of realizing a face detection and tracking in video sequences. In developing this system, we have used a RBF neural network to locate and categorize faces of different dimensions. The face tracker can be applied to a video communication system which allows the users to move freely in front of the camera while communicating. The system works at several stages. At first, we extract useful parameters by a low-pass filtering to compress data and we compose our codebook vectors. Then, the RBF neural network realizes a face detection and tracking on a specific board.
<title>Real-time face tracking and recognition for video conferencing</title>
This paper describes a system of vision in real time, allowing to detect automatically the faces presence, to localize and to follow them in video sequences. We verify also the faces identities. These processes are based by combining technique of image processing and methods of neural networks. The tracking is realized with a strategy of prediction-verification using the dynamic information of the detection. The system has been evaluated quantitatively on 8 video sequences. The robustness of the method has been tested on various lightings images. We present also the analysis of complexity of this algorithm in order to realize an implementation in real time on a FPGA based architecture.