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A study on discrimination of SIFT feature applied to binary images
(CERIST, 2015-10-04) Setitra, Insaf; Larabi, Slimane
Scale Invariant Feature Transform (SIFT) since its first apparition in 2004 has been (and still is) extensively used in computer vision to classify and match objects in RGB and grey level images and videos. However, since the descriptor used in SIFT approach is based on gradient magnitude and orientation, it has always been considered as texture feature and received less interest when treating binary images. In this work we investigate the power of discrimination of SIFT applied to binary images. A theoretical and experimental studies show that SIFT can still describe shapes and can be used to distinguish objects of several classes.
Object classification in videos An overview
(CERIST, 2012) Setitra, Insaf
In this article, we will discuss the classification of moving objects in videos. An overview of classical steps in video classification will be given and a particular attention will be given to classification in video surveillance since classification in this kind of systems is very important and plays a primary role in several functions such as event classification, speed control, classification of intrusions and so forth.