Abstract It was proved that the fusion of information from multi-modality images increases the accuracy of pedestrian recognition systems. One of the best approach so far is to concatenate the features from multi-modality images into a large feature vector, but it requires strong camera calibration settings and non-discriminative modalities could lead to missclassification of…
Abstract Despite many years of research, pedestrian recognition is still a difficult, but very important task. It was proved that concatenating information from multi-modality images improves the recognition accuracy, but with a high computational cost. We present a modality selection approach, which is able to dynamically select the most discriminative modality for a given…
Abstract Despite many years of research, obstacle recognition is still a difficult, but very important task. We present a multi-class approach, that extracts from images the Histogram of Oriented Gradients (HOG) based on aspect ratio of Region of Interest (ROI) and use them in a multiclass classification problem. For the learning phase we propose…
Abstract The detection of tumors in digital images originated from mammograms can be a challenging task. In this paper we investigate a Computer Aided Diagnosis System based on a Genetic Programming classifier. The performance of the considered classifier is evaluated for five of the image descriptors used in literature and we propose a new…
Abstract Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a…