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Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In this paper we present that, besides the local features, context cues in the neighborhood provide important constraints that are not yet well utilized. We propose a framework to incorporate the context constraints for detection...
In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model...
Gestures represent an important channel of human communication, and they are “co-expressive” with speech. For this reason, in human-machine interaction automatic gesture classification can be a valuable help in a number of tasks, like for example as a disambiguation aid in automatic speech recognition. Based on the hand gesture categorization proposed by D. McNeill in his reference works on gesture...
In this paper, we adopt BP neural network as classification method and established the emotion model to simulate people's emotion based on image template. For achieving this purpose, firstly, we used 9 images which has different features of the famous psychologist Furnham's Shape & Color Test as templates and the basis of emotion classification. Then, we got a vector which has 27 dimensions by...
In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human...
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