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In this paper, we propose a method for multimodal retinal image registration based on feature guided Gaussian mixture model (GMM) and edge map. We extract two sets of feature points from the edge maps of two images, and formulate image registration as the estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian...
Hand gesture is an effective and natural way for human-robot interaction (HRI). This paper presents a robust dynamic hand gesture recognition system with a RGB-D sensor. In order to automatically recognize hand gesture from color and depth sequences, where noise and occlusion are common problems, we extract steric Haar-like features to robustly represent the complicated spatial information of the...
In this paper, we propose a novel feature guided Gaussian mixture model (FG-GMM) for image matching, which typically requires matching two sets of feature points extracted from the given images. We formulate the problem as estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to...
A mobile Social Network (MSN) is a type of wireless networks formed by people moving around carrying mobile devices. In this paper, we specifically study the MSNs that are formed impromptu, e.g. when people gather together for a conference, event, or festival. We refer to them as Impromptu Mobile Social Networks (IMSNs), which allow people to communicate in a lightweight fashion based on contact opportunities...
In this paper, we propose an efficient part-based approach for action recognition. The main concept is to recognize human actions by less occluded parts without using a large set of part filters. Therefore, our approach is robust to occlusion and cost-effective. We extract spatiotemporal features from RGB-D videos, and assign a part-label to each feature. Then, for each part, a recognition score is...
In this paper, we propose a Bayesian conditional probability with latent-structure model for context-aware activities of daily living (ADL) recognition. The proposed ADL recognition system takes RGBD sensor (Microsoft Kinect) as the input device. In ADL recognition, the object interacted with human is a sort of important context as well as human action. To better understand the activity, we model...
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