Working Paper Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 2 October 2019 / Approved: 8 October 2019 / Online: 8 October 2019 (10:33:37 CEST)

A peer-reviewed article of this Preprint also exists.

Zhang, Q.; Shi, B. A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations. Information 2019, 10, 376. Zhang, Q.; Shi, B. A Global Extraction Method of High Repeatability on Discretized Scale-Space Representations. Information 2019, 10, 376.

Journal reference: Information 2019, 10, 376
DOI: 10.3390/info10120376

Abstract

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 obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template 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.

Subject Areas

local feature extraction; scale-space representation; laplacian of gaussian; convolution template

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