Special Issue "Trends in Machine Learning for Visual Computing"

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

Deadline for manuscript submissions: closed (7 April 2019).

Special Issue Editor

Prof. Dr. George A. Papakostas
E-Mail Website
Guest Editor
HUMAIN-Lab, Department of Computer and Informatics Engineering, Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece
Interests: computational intelligence; machine learning; computer vision; pattern recognition; image processing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

As we are converging towards the Industry 4.0 framework, the applications involving smart devices with vision capabilities are increasing rapidly. In addition to Industry 4.0, all devices requiring intelligent applications are also welcome. Those devices are making use of emerged machine learning algorithms, which analyze the visual content of real scenes in a variety of environments. In this context, modern vision devices, e.g., cameras, microscopes, etc., are able to capture high resolution images and to provide vast information that needs to be analyzed in real-time for commercial and scientific purposes. This Special Issue aims to record recent trends in the machine learning research field where the data source is any kind of vision device.

Prof. Dr. George A. Papakostas
Guest Editor

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 1000 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

  • object tracking, recognition
  • medical imaging analysis/understanding
  • intelligent vehicle vision systems
  • video surveillance
  • visual monitoring systems
  • robot vision
  • machine vision

Published Papers (1 paper)

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Research

Open AccessArticle
Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks
J. Imaging 2019, 5(4), 44; https://doi.org/10.3390/jimaging5040044 - 03 Apr 2019
Abstract
Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, [...] Read more.
Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera. Full article
(This article belongs to the Special Issue Trends in Machine Learning for Visual Computing)
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