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The goal of a fall detection system is to automatically detect cases where a human falls and may have been injured. We propose a statistical method based on Kinect depth cameras, that makes a decision based on information about how the human moved during the last few frames. Our method proposes novel features to be used for fall detection, and combines(More)
Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific(More)
A key issue in case-based reasoning is how to maintain the domain knowledge in the face of a changing environment. During the case retrieval process in case-based reasoning, feature-value pairs are used to compute the ranking scores of the cases in a case base, and different feature-value pairs may have different importance measures, represented as weight(More)
A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled(More)
In this paper, some image registration algorithms are investigated for the purpose of image fusion in a digital camera application. A hybrid scheme which uses both feature-based and intensity-based methods is proposed. In particular, an edge-based image registration approach is developed to guide the intensity-based registration which uses optical ow(More)
Recently more and more researchers have been supporting the view that learning is a goal-driven process. One of the key properties of a goal-driven learner is introspectiveness-the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning(More)