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This paper proposes a new method for image registration by combining SURF and FREAK. SURF can extract robust feature points, and the topology of FREAK descriptor has strong ability of regional description. First, feature points of images are extracted by SURF, and described by FREAK descriptor. Then descriptors are roughly matched through the ratio of the closest neighbor and second closest one. Second,...
Hand posture recognition is an extremely active research topic in Computer Vision and Robotics, with many applications ranging from automatic sign language recognition to human-system interaction. Recently, a new descriptor for object representation based on the kernel method (KDES) has been proposed. While this descriptor has been shown to be efficient for hand posture representation, across-the-board...
Automatic classification of tropical wood species is becoming more important especially for timber exporting countries due to the considerable economic challenge as a result of fraudulent labelling of timber species at the custom checkpoints. Hence, a reliable automated wood species recognition system is needed to inspect the wood species labelling at the checkpoints. A tropical wood species classification...
Aiming at reaching an interactive and simplified usage of high-resolution 3D acquisition systems, this paper presents a fast and automated technique for pre-alignment of dense range images. Starting from a multi-scale feature point extraction and description, a processing chain composed by feature matching and correspondence searching, ranking grouping and skimming is performed to select the most...
This paper presents a method to construct efficient and distinctive descriptors for local image features based on Scale Invariant Features Transform (SIFT), namely, Kernel Independent Component Analysis Scale Invariant Features Transform (KICA-SIFT). KICA-SIFT is a improved version of the conventional SIFT for the two reasons: first, the improved SIFT descriptors are relative invariant to affine transformation,...
This paper presents an effective dimensionality reduction method based on support vector machine. By utilizing mapping vectors from support vector machine for dimensionality reduction purpose, we obtain features which are computationally efficient, providing high classification accuracy and robustness especially in noisy environment. These characteristics are acquired from the generalization capability...
In this paper, we use a Hidden Markov Models (HMM) to classify bags of SURF keypoints descriptors of a given class. The performance of this technique is compared to that of others, by testing it on various multi-class datasets. We also describe a prospective of expanding our application to include the detection and classification of moving objects in a video stream using optical flow and Self Organizing...
Visual target classification is one of the most important issues addressed in wireless multimedia sensor network (WMSN). This paper proposes a hybrid Gaussian process based classification method to implement binary visual classification (human/nonhuman) in WMSN. Because the computation ability of sensor node in WMSN is strictly limited, target classification is achieved by Gaussian process classifier...
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