Working Paper Article Version 1 This version is not peer-reviewed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

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.

Keywords

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

Subject

Computer Science and Mathematics, Information Systems

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.