Papers by Keyword: Scale-Invariant

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Authors: Yan Wei Wang, Si Qing Zhang, Bing Lin, Hong Liang, Yan Ming Pan
Abstract: Feature Point Extraction Method of X-ray Image Based on Scale Invariant is proposed in this paper for industrial X-ray image with low contrast and some artifacts. First of all, the scale transformation of original image is adopted by the Gaussian kernel to building the DOG multi-scale pyramid. Then, the location and scale of the key points is fixed by the three-dimensional quadratic function. Finally, the Simply SIFT descriptor illustrates the key points. Experimental results show that the algorithm has good stability in translation, rotation and affine transformation, especially with 10 percent normalized Gaussian noise, this algorithm can still be detected feature points accuracy.
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Authors: Yu Hong Du, Chen Wu, Di Zhao, Yun Chang, Xing Li, Shuo Yang
Abstract: A novel scale-invariant feature transform (SIFT) algorithm is proposed for soccer target recognition application in a robot soccer game. First, the method of generating scale space is given, extreme points are detected. This gives the precise positioning of the extraction step and the SIFT feature points. Based on the gradient and direction of the feature point neighboring pixels, a description of the key points of the vector is generated. Finally, the matching method based on feature vectors is extracted from SIFT feature points and implemented on the image of the football in a soccer game. By employing the proposed SIFT algorithm for football and stadium key feature points extraction and matching, significant increase can be achieved in the robot soccer ability to identify and locate the football.
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