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We present a robot self-localization approach that is based on using a cascade of filters that increasingly refine a robot's guess regarding where it is in a hallway system. The location refinement carried out by each stage of the cascade compares a signature extracted from a stereo pair of camera images taken at the current location of the robot with a database of such signatures collected previously...
Our work provides a fast approach-path-independent framework for the problem of place recognition and robot localization in indoor environments. The approach-path independence is achieved by using highly viewpoint-invariant 3D junction features extracted from stereo pairs of images; these are based on stereo reconstructions of the JUDOCA junctions extracted from the individual images of a stereo pair...
In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start...
In this paper, we present a novel method for human action recognition from any arbitrary view image sequence that uses the Cartesian component of optical flow velocity and human body silhouette feature vector information. We use principal component analysis (PCA) to reduce the higher dimensional silhouette feature space into lower dimensional feature space. The action region in an image frame represents...
Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature...
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