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In batch learning all the training examples have to be available at once to train the model, which often leads to slow performance and large memory requirements. Little work has been done in developing incremental object learners. In this paper, we present an incremental method that finds corresponding points of similar object instances, appearing in natural grayscale images with arbitrary location,...
Selecting automatically feature points of an object appearing in images is a difficult but vital task for learning the feature point based representation of the object model. In this work we present an incremental Bayesian model that learns the feature points of an object from natural un-annotated images by matching the corresponding points. The training set is recursively expanded and the model parameters...
Detecting and recognizing pedestrians in video footages are two essential and significant tasks in many automatic video understanding systems. In this paper, we propose an efficient approach to moving pedestrian detection and recognition in video. The testing process of this approach involves two main steps: moving edge detection and hypotheses generation. Moving edges are firstly extracted by comparing...
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