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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...
Event extraction is a major task of Automatic Content Extraction (ACE) program. This paper focuses on the sub-task of event extraction, event argument identification, and proposes a novel method for Chinese event argument identification. The method involves two steps: (1) weighting features by the ReliefF algorithm for considering the particular contributions of different features on clustering analysis,...
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...
In this paper, a new greedy feature selection algorithm is proposed to detect more precisely informative features. It overcomes the limitation of many existing MI-based gready feature selection algorithms. It is capable of detecting the relation of relevant feature combinations in some degree.In addition, the requirements of the memory storage and computation cost are low. Experimental results for...
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