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Moving vehicle detection in dynamical scene is a significant but challenging problem in these days. A new and effective approach to extract moving vehicles is proposed in this paper. In our method, Harris corner and Lucas-Kanade (L-K) optical flow was adopted to generalize feature-point optical flow field between two consecutive frames which obtained from monocular moving camera, and then vector quantization...
This paper presents a method of insect recognition using computer vision technology. First, we extracted fourteen features from images of some species of insects. These features are rectangularity, elongation, roundness, eccentricity, sphericity, lobation, compactness and seven Hu moment invariants. Second, a machine learning algorithm named Random Trees was employed, to play a role of a classifier...
This paper presents a feature recognition method based on randomized trees. We aim to improve the performance of Lepetit's work, whose actual results are very sensitive to large changes of viewpoint due to its limited ability of samples synthesizing and learning. We propose an approach to alleviate its limitation, which simulates the image appearance changes under actual viewpoint changes by applying...
This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during...
Current mainstream vehicle recognition algorithms mainly depend on the synthesis of both appearance based and knowledge based features to identify the candidate objects. Whereas, because of the unpredictable complex noises in real world environments, the existences, quantification and the explanation for certain features are often ambiguous which makes current algorithm hard to fulfill the dilemmatic...
A monocular vision based detection algorithm is presented to detect rear vehicles. Our detection algorithm consist of two main steps: knowledge based hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, a shadow extraction method is proposed based on contrast sensitivity to extract regions of interest (ROI), it can effectively solve the problems caused...
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