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Computers
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Vision, Image and Signal Processing (ICVISP)
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Vision, Image and Signal Processing (ICVISP)

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 13581

Special Issue Editor

Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: human-centered systems; machine learning; data science; distributed computing; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have seen dramatic progress being made in the areas of computer vision, image and signal processing. Traditionally, computer vision is fundamental to robotics research and development. With the availability of inexpensive, consumer-grade programmable depth cameras such as Microsoft Kinect, we are seeing many interesting computer vision-based applications for rehabilitation, worker safety, medical operation, retail, gaming, education, etc. Image processing is playing an ever-important role in security (such as facial and fingerprint recognition) and medical research and practice (such as the diagnosis of various diseases). Signal processing is the foundation for making sense of various information obtained from wearable devices and Internet of Things. Indeed, it is an exciting era to see our society being transformed by technology.

Selected papers that presented at the 2nd International Conference on Vision, Image and Signal Processing (ICVISP 2018) are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles with at least 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in Open Access format in Computers and collected together on this Special Issue website.

Prof. Dr. Wenbing Zhao
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 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. Computers 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

  • Gesture and activity recognition
  • Facial recognition
  • Medical image processing
  • Machine learning in vision, image and signal processing
  • Pattern recognition
  • Sensor fusion
  • Internet of Things

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Published Papers (2 papers)

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17 pages, 7441 KiB  
Article
Active Eye-in-Hand Data Management to Improve the Robotic Object Detection Performance
by Pourya Hoseini, Janelle Blankenburg, Mircea Nicolescu, Monica Nicolescu and David Feil-Seifer
Computers 2019, 8(4), 71; https://doi.org/10.3390/computers8040071 - 23 Sep 2019
Cited by 4 | Viewed by 5348
Abstract
Adding to the number of sources of sensory information can be efficacious in enhancing the object detection capability of robots. In the realm of vision-based object detection, in addition to improving the general detection performance, observing objects of interest from different points of [...] Read more.
Adding to the number of sources of sensory information can be efficacious in enhancing the object detection capability of robots. In the realm of vision-based object detection, in addition to improving the general detection performance, observing objects of interest from different points of view can be central to handling occlusions. In this paper, a robotic vision system is proposed that constantly uses a 3D camera, while actively switching to make use of a second RGB camera in cases where it is necessary. The proposed system detects objects in the view seen by the 3D camera, which is mounted on a humanoid robot’s head, and computes a confidence measure for its recognitions. In the event of low confidence regarding the correctness of the detection, the secondary camera, which is installed on the robot’s arm, is moved toward the object to obtain another perspective of the object. With the objects detected in the scene viewed by the hand camera, they are matched to the detections of the head camera, and subsequently, their recognition decisions are fused together. The decision fusion method is a novel approach based on the Dempster–Shafer evidence theory. Significant improvements in object detection performance are observed after employing the proposed active vision system. Full article
(This article belongs to the Special Issue Vision, Image and Signal Processing (ICVISP))
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21 pages, 8921 KiB  
Article
A Comparison of Compression Codecs for Maritime and Sonar Images in Bandwidth Constrained Applications
by Chiman Kwan, Jude Larkin, Bence Budavari, Bryan Chou, Eric Shang and Trac D. Tran
Computers 2019, 8(2), 32; https://doi.org/10.3390/computers8020032 - 28 Apr 2019
Cited by 12 | Viewed by 7202
Abstract
Since lossless compression can only achieve two to four times data compression, it may not be efficient to deploy lossless compression in bandwidth constrained applications. Instead, it would be more economical to adopt perceptually lossless compression, which can attain ten times or more [...] Read more.
Since lossless compression can only achieve two to four times data compression, it may not be efficient to deploy lossless compression in bandwidth constrained applications. Instead, it would be more economical to adopt perceptually lossless compression, which can attain ten times or more compression without loss of important information. Consequently, one can transmit more images over bandwidth limited channels. In this research, we first aimed to compare and select the best compression algorithm in the literature to achieve a compression ratio of 0.1 and 40 dBs or more in terms of a performance metric known as human visual system model (HVSm) for maritime and sonar images. Our second objective was to demonstrate error concealment algorithms that can handle corrupted pixels due to transmission errors in interference-prone communication channels. Using four state-of-the-art codecs, we demonstrated that perceptually lossless compression can be achieved for realistic maritime and sonar images. At the same time, we also selected the best codec for this purpose using four performance metrics. Finally, error concealment was demonstrated to be useful in recovering lost pixels due to transmission errors. Full article
(This article belongs to the Special Issue Vision, Image and Signal Processing (ICVISP))
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