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J. Imaging
Special Issues
Edge Detection Evaluation
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Edge Detection Evaluation

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

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 16161

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
EuroMov Digital Health in Motion, Université de Montpellier, IMT Mines Ales, 30100 Ales, France
Interests: image processing; multimedia security; digital images and videos; edge detection; computer vision
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 1800 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 (5 papers)

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Research

22 pages, 5894 KiB  
Article
Human Tracking in Top-View Fisheye Images: Analysis of Familiar Similarity Measures via HOG and against Various Color Spaces
by Hicham Talaoubrid, Marina Vert, Khizar Hayat and Baptiste Magnier
J. Imaging 2022, 8(4), 115; https://doi.org/10.3390/jimaging8040115 - 16 Apr 2022
Cited by 4 | Viewed by 2089
Abstract
The purpose of this paper is to find the best way to track human subjects in fisheye images by considering the most common similarity measures in the function of various color spaces as well as the HOG. To this end, we have relied [...] Read more.
The purpose of this paper is to find the best way to track human subjects in fisheye images by considering the most common similarity measures in the function of various color spaces as well as the HOG. To this end, we have relied on videos taken by a fisheye camera wherein multiple human subjects were recorded walking simultaneously, in random directions. Using an existing deep-learning method for the detection of persons in fisheye images, bounding boxes are extracted each containing information related to a single person. Consequently, each bounding box can be described by color features, usually color histograms; with the HOG relying on object shapes and contours. These descriptors do not inform the same features and they need to be evaluated in the context of tracking in top-view fisheye images. With this in perspective, a distance is computed to compare similarities between the detected bounding boxes of two consecutive frames. To do so, we are proposing a rate function (S) in order to compare and evaluate together the six different color spaces and six distances, and with the HOG. This function links inter-distance (i.e., the distance between the images of the same person throughout the frames of the video) with intra-distance (i.e., the distance between images of different people throughout the frames). It enables ascertaining a given feature descriptor (color or HOG) mapped to a corresponding similarity function and hence deciding the most reliable one to compute the similarity or the difference between two segmented persons. All these comparisons lead to some interesting results, as explained in the later part of the article. Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
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15 pages, 1386 KiB  
Article
Seamless Copy–Move Replication in Digital Images
by Tanzeela Qazi, Mushtaq Ali, Khizar Hayat and Baptiste Magnier
J. Imaging 2022, 8(3), 69; https://doi.org/10.3390/jimaging8030069 - 10 Mar 2022
Cited by 3 | Viewed by 2119
Abstract
The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this [...] Read more.
The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery. Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
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19 pages, 22144 KiB  
Article
A Low Redundancy Wavelet Entropy Edge Detection Algorithm
by Yiting Tao, Thomas Scully, Asanka G. Perera, Andrew Lambert and Javaan Chahl
J. Imaging 2021, 7(9), 188; https://doi.org/10.3390/jimaging7090188 - 17 Sep 2021
Cited by 4 | Viewed by 2303
Abstract
Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In [...] Read more.
Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications. Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
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18 pages, 53028 KiB  
Article
Comparative Study of Data Matrix Codes Localization and Recognition Methods
by Ladislav Karrach and Elena Pivarčiová
J. Imaging 2021, 7(9), 163; https://doi.org/10.3390/jimaging7090163 - 27 Aug 2021
Cited by 5 | Viewed by 6210
Abstract
We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image [...] Read more.
We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection. Full article
(This article belongs to the Special Issue Edge Detection Evaluation)
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10 pages, 32497 KiB  
Communication
UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video
by Wesley T. Honeycutt and Eli S. Bridge
J. Imaging 2021, 7(5), 77; https://doi.org/10.3390/jimaging7050077 - 23 Apr 2021
Cited by 2 | Viewed by 2317
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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