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In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature...
We present a method to learn visual attributes (eg."red", "metal", "spotted") and object classes (eg. "car", "dress", "umbrella") together. We assume images are labeled with category, but not location, of an instance. We estimate models with an iterative procedure: the current model is used to produce a saliency score, which, together with...
This paper proposes an improvement of advanced driver assistance system based on saliency estimation of road signs. After a road sign detection stage, its saliency is estimated using a SVM learning. A model of visual saliency linking the size of an object and a size-independent saliency is proposed. An eye tracking experiment in context close to driving proves that this computational evaluation of...
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