Sharif Digital Repository

Human detection in still images is one of the hardest problems in object detection area. There are several challenges like articulation, pose variation, variant clothing, none uniform illumination, cluttered background and occlusion which make this problem more sophisticated than any other object detection problem. The general solution for this kind of problems is based on supervised learning that contains two main parts: 1-extracting proper features, 2- using proper classifier. The main focus of this thesis is on the first part, extracting proper features, which should be robust to mentioned challenges. Based on the level of extraction we can divide the features to four groups: 1-low-level,... 

A survey on the methods of detecting a human body inside surrounding captured images of a machine, optimizing one of these methods and finding an injured person in a captured picture is our target. Using neural networks for better detection and making RBFN network, make it possible to use high altitude captured pictures with lower resolution and light changes. Modifying RBFN to MRBFN network for an optimized processing with effect of living signs seems to be an applicable approach. These methods can extended from detecting an injured person in a captured picture, to analyze the medical images like detecting a special pattern in MRI picture. Of course it’s a start for many future...