R. Mocan, L. Diosan, “Multiclass classification based on clustering approaches for obstacle recognition in traffic scenes”, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), 2016, pp. 257 – 261
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 some…
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 image…
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 approach,…
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 diagnosis.…
I.G. Mircea, G. Czibula, M.R. Petrușel, Sex Identification in Archaeological Remains Using Decision Tree Learning, Studia Universitatis Babes-Bolyai, Informatica, 2015, pages 91-103
R. Mocan, L. Diosan, “Obstacle recognition in traffic by adapting the hog descriptor and learning in layers,” Studia Univ. Babes–Bolyai, Informatica, vol. 55, Nr 2, 2015