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Given a set of unordered images taken in a wide area, an effective solution is proposed for establishing robust feature correspondences among them. Two major improvements are made in our work as follows: firstly, a robust technique is proposed for the self-organization of a large number of images without spatial orderings; secondly, a novel wide-baseline matching approach is developed to obtain good...
Given a set of unsorted views captured in a wide area, an effective solution is proposed for image self-organization. The method starts with an initialization step where a small number of key frame pairs are selected to set up a global reference. Given a query image we automatically relate it to the existing key frames based on their pair-wise similarity evaluation. Four major enhancements are made...
This paper presents a computer vision based virtual learning environment for teaching communicative hand gestures used in sign language. A virtual learning environment was developed to demonstrate signs to the user. The system then gives real time feedback to the user on their performance of the demonstrated sign. Gesture features are extracted from a standard web-cam video stream and shape and trajectory...
This paper presents a computer vision based virtual learning environment for teaching communicative hand gestures used in Sign Language. A virtual learning environment was developed to demonstrate signs to the user. The system then gives real time feedback to the user on their performance of the demonstrated sign. Gesture features are extracted from a standard web-cam video stream and shape and trajectory...
The research discussed in this paper documents a comparative analysis of two nonlinear dimensionality reduction techniques for the classification of facial expressions at varying degrees of intensity. These nonlinear dimensionality reduction techniques are Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE). The approaches presented in this paper employ psychological tools,...
This paper presents work being carried out to estimate human pose using vision based methods. The data acquisition system uses an active marker technique synchronized with a three camera stereo vision system. The locations of the markers are then used to reconstruct a skeleton representation of the human pose. PCA and clustering techniques are used to classify the pose.
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