Structural Pattern Recognition meets Computer Vision
Donatello Conte - Université de Tours
Computer Vision Problems, such as object detection, object tracking, action recognition and so on, have been usually addressed through Statistical Pattern Recognition techniques. SVM, Regression or Neural Networks, are some examples of classical statistical techniques that have been used, quite effectively, in many application contexts of computer vision. Nevertheless, some attempts have been proposed using more complex data structures (notably graphs) for solving Computer Vision Tasks. First part of this talk will present some of these proposals, in the context of background subtraction problem, object tracking, people re-identification and action recognition. Recently, graphs have gained a lot of attention in the Computer Vision community thanks to the use of this kind of data within deep learning techniques. Graph Neural Networks have demonstrated their effectiveness in solving Computer Vision problems, and in some cases recent proposals have bridged the gap between statistical and structural pattern recognition. Second part of the talk will be devoted to illustrate some these examples.