Special Issue "Edge Detection Evaluation"

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 1 September 2021.

Special Issue Editors

Dr. Philippe Montesinos
E-Mail Website
Guest Editor
Laboratory of Computer and Production Engineering, IMT Mines Alès, 30100 Alès, France
Interests: image filtering and segmentation; image matching; object detection and recognition
Dr. Baptiste Magnier
E-Mail Website
Guest Editor
Laboratory of Computer and Production Engineering, IMT Mines Alès, 30100 Alès, France
Interests: image processing; edge detection; edge detection evaluation

Special Issue Information

Dear Colleagues,

From the 1980s onward, edge detection has been an important research field in digital image processing, and also one of the fundamental steps in computer vision techniques. The points in digital images where brightness/color change contain essential information for image analysis and computer vision. For this reason, edge detection remains a crucial stage in numerous image processing applications. Since edges are considered a set of curved lines formed by the points of sharp brightness/color change, they can be detected through mathematical methods often involving numerical derivation. Then, to ensure that an edge detection technique is reliable, especially for a specific application, it needs to be rigorously assessed before being used in a continual/frequent computer vision tool.

Thus, the measurement process can be classified as either an unsupervised or a supervised evaluation criterion. The first class of methods exploits only the input contour image and gives a coherence score that qualifies the result given by the algorithm as continuation, connectivity or thinness of edges. For the second class of methods, a supervised evaluation criterion computes a dissimilarity measure between a segmentation result and a ground truth, generally obtained from synthetic data or expert judgement (i.e., manual segmentation).

This Special Issue aims to gather innovative research on edge detection and especially on edge detection evaluation in image segmentation techniques. We welcome submissions including but not limited to the following topics: approaches for edge detection; threshold determination; edge detection operators; image filtering for edge detection; shape similarity measures; gradient orientation evaluation; edge model; etc.

Dr. Philippe Montesinos
Dr. Baptiste Magnier
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • edge detection
  • edge detection evaluation
  • edge detection operators
  • image filtering for edge detection
  • shape similarity measures
  • gradient orientation evaluation
  • edge model

Published Papers (1 paper)

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Research

Communication
UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video
J. Imaging 2021, 7(5), 77; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7050077 - 23 Apr 2021
Viewed by 277
Abstract
Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection [...] Read more.
Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods. Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
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