Abstract-This article presents a validator stage for a pedestrian detection system based on the use of probabilistic models for the infrared domain. Four different models are employed in order to recognize the pose of the pedestrians; open, almost open, almost closed and fully closed legs are detected. In an attempt to overcome the drawbacks of template-matching in far infrared images, two different approaches are proposed. The algorithm has been tested on an experimental vehicle in different situations and a Receiver Operating Characteristic has been computed.
In this paper we propose a new set of bio-inspired descriptors for image classification based on low-level processing performed by the retina. Taking as a starting point a descriptor called FREAK (Fast Retina Keypoint), we further extend it mimicking the center-surround organization of ganglion receptive fields.To test our approach we compared the performance of the original FREAK and our proposal on the 15 scene categories database. The results show that our approach outperforms the original FREAK for the scene classification task.
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