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Open AccessArticle

A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations

by Qingming Zhang 1,2,* and Buhai Shi 1,*
1
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
2
School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China
*
Authors to whom correspondence should be addressed.
Information 2019, 10(12), 376; https://doi.org/10.3390/info10120376
Received: 2 October 2019 / Revised: 21 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid interpolating scales on few data points and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution templates, are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change, and even viewpoint change. View Full-Text
Keywords: local feature extraction; scale-space representation; Laplacian of Gaussian; convolution template local feature extraction; scale-space representation; Laplacian of Gaussian; convolution template
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Zhang, Q.; Shi, B. A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations. Information 2019, 10, 376.

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