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This paper proposes a novel inherently rotation invariant local descriptor which combined intensity information and gradient information of key feature. The CS-LBP shows a better performance than SIFT and do not need large computation. To further enhance its performance and robustness, we calculated the gradient of key feature and computed a combined histogram included intensity and gradient information...
Feature refers to some relevant information which is present on images or faces. Feature extraction used to extract those features from the face. Among that bulk of keypoints, only robust features are detected by using feature descriptors. This paper analyzes 2 robust feature detector and descriptors are: Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). These two robust...
Space-time feature extraction is a recent and popular method used for action recognition. This paper presents a new algorithm to improve the robustness of spatio-temporal feature extraction techniques against the illumination and scale variations. Most of the interest point detectors are sensitive to illumination variations that may cause serious problems in action recognition by finding wrong keypoints...
In this paper a modified sports genre categorization framework is presented. The view type of close-up is detected as domain knowledge before categorization on large scale database. Close-up views occupy more than 1/3 of the duration of a sport match depending on its genre, and appears almost the same in various genres, which largely affected the performance of sports genre categorization. The presented...
In the state-of-the-art visual object recognition, there are a number of descriptors that have been proposed for various visual recognition tasks. But it is still difficult to decide which descriptors have more significant impact on this task. The descriptors should be distinctive and at the same time robust to changes in viewing conditions. This paper evaluates the performance of two distinctive...
With the recent development on image affine region descriptors, we can extract more salient and useful local information from images. That information can be used to help us to better solve a fundamental problem in computer vision stereo vision. In this paper we propose a framework for stereo matching problems in order to give a rich-information based, high-precision and fast solution. Affine regions...
In this paper, a technique to design a robust feature extractor and descriptor for visual map building is proposed. The extracted features are required to be computationally attractive and invariant to image rotation, scale change and illumination. We adapted the scale invariant features transform (SIFT) algorithm for map building applications. Our main contributions are: firstly, we introduce of...
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