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Special Issue "Document-Image Related Visual Sensors and Machine Learning Techniques"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Guest Editor
Prof. Dr. Kyandoghere Kyamakya

Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
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Phone: +43 463 2700 3540
Interests: intelligent transportation systems; machine vision; machine learning and pattern recognition; neurocomputing and applications; systems science and nonlinear dynamics; telecommunications systems; robotics and autonomous systems
Guest Editor
Dr. Fadi Al-Machot

Alpen-Adria-Universität Klagenfurt, Department of Applied Informatics, Klagenfurt, Austria
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Interests: machine learning; pattern recognition; image processing; data mining; video understanding; cognitive modeling and recognition
Guest Editor
Dr. Ahmad Haj Mosa

Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria
Website | E-Mail
Interests: machine learning; cognitive neuroscience; applied mathematics; machine vision
Guest Editor
Dr. Jean Chamberlain Chedjou

Alpen-Adria-Universität Klagenfurt, Institute of Smart System Technologies, Klagenfurt, Austria
Website | E-Mail
Interests: dynamic systems in engineering; neurocomputing and applications; optimization and inverse problems; intelligent transportation systems

Special Issue Information

Dear Colleagues,

Digitizing paper-based documents to cut down costs and reduce the well-known negative environmental impacts of using and wasting too much paper in offices has led to an increased focus on the systematic electronic scanning of documents through, scanners, mobile phone cameras, etc.

Generally, the quality of the captured document-images is far from good due to a series of challenges related to the performance of the visual sensors and, for camera-based captures, difficult external environmental conditions encountered during the sensing (image capturing) process. Such document-images are mostly hard to read, have low contrast and are corrupted by various artifacts such as noise, blur, shadows, spot lights, etc., just to name a few.

To ensure an acceptable quality of the final document-images that can be perfectly digitalized and involved in various high-level applications based on digital documents, the sensing process must be made much more robust than the raw capture result generated by a purely physical visual sensor. Thus, the physical sensors must be virtually augmented by a series of additional pre- and/or post-processing functional blocks, which mostly involve, amongst others, advanced machine learning techniques.

Paper submissions with innovative and robust approaches are invited for submission as they are needed to solve a series of core issues of relevance for this Special Issue:

  • Visual sensors related issues w.r.t. document capture or digitization:
    • Modeling and calibration of visual sensors w.r.t. various distortions
    • Camera calibration concepts to robustly defocus images
    • Identification, classification and characterization of visual sensor-related sources of document-image deterioration and distortion
    • Sharpness quality prediction for mobile-captured document images
    • Variational models for document-image binarization
    • Fuzzy models for blur estimation on document images
    • Adaptive binarization of degraded and/or distorted document images
    • Rectification and mosaicking of camera-captured document images
    • Sensor systems for low light document capture and binarization with multiple flash images
  • Quality assessment of the performance of visual sensors for document capture:
    • Document-image analysis
    • Subjective and objective assessment of the quality of document-images w.r.t. distortions such as blur, noise, contrast, shadow, spot light, etc.
    • Neurocomputing applications in image quality detection
  • Post-processing of document-images (captured either by scanners or by mobile phone cameras):
    • Image quality analysis and enhancement:
      • Document-image degradation models
      • Restoration of deteriorated document-images
      • Quality enhancement of distorted (w.r.t. blur, noise, contrast, shadow, spot light, etc.) document images
      • Datasets creation for the quality assessment of camera-captured images
      • Perspective rectification of camera-captured document images
    • Document image classification and character recognition:
      • Automated and robust classification of document-images under difficult realistic conditions (i.e. deteriorated or distorted images)
      • Deep learning applications in robust automated image classification
      • Impact of image distortion on the readability of QR codes
      • Robust optical character recognition for distorted and/or deteriorated document-images

Prof. Dr. Kyandoghere Kyamakya
Dr. Fadi Al-Machot
Dr. Ahmad Haj Mosa
Dr. Jean Chamberlain Chedjou
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. Sensors is an international peer-reviewed open access semimonthly 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.

Published Papers (3 papers)

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Research

Open AccessArticle
A New Filtering System for Using a Consumer Depth Camera at Close Range
Sensors 2019, 19(16), 3460; https://doi.org/10.3390/s19163460
Received: 16 June 2019 / Revised: 31 July 2019 / Accepted: 1 August 2019 / Published: 8 August 2019
PDF Full-text (5268 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which [...] Read more.
Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which is an obstacle for real-time applications that do not rely on point cloud post-processing. In order to fill this gap, by analyzing the noise region based on position and shape, we proposed a composite filtering system for using consumer depth cameras at close range. The system consists of three main modules that are used to eliminate different types of noise areas. Taking the human hand depth image as an example, the proposed filtering system can eliminate most of the noise areas. All algorithms in the system are not based on window smoothing and are accelerated by the GPU. By using Kinect v2 and SR300, a large number of contrast experiments show that the system can get good results and has extremely high real-time performance, which can be used as a pre-step for real-time human-computer interaction, real-time 3D reconstruction, and further filtering. Full article
(This article belongs to the Special Issue Document-Image Related Visual Sensors and Machine Learning Techniques)
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Open AccessCommunication
Converting a Common Low-Cost Document Scanner into a Multispectral Scanner
Sensors 2019, 19(14), 3199; https://doi.org/10.3390/s19143199
Received: 20 May 2019 / Revised: 18 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
PDF Full-text (27313 KB) | HTML Full-text | XML Full-text
Abstract
Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images [...] Read more.
Forged documents and counterfeit currency can be better detected with multispectral imaging in multiple color channels instead of the usual red, green and blue. However, multispectral cameras/scanners are expensive. We propose the construction of a low cost scanner designed to capture multispectral images of documents. A standard sheet-feed scanner was modified by disconnecting its internal light source and connecting an external multispectral light source comprising of narrow band light emitting diodes (LED). A document was scanned by illuminating the scanner light guide successively with different LEDs and capturing a scan of the document. The system costs less than a hundred dollars and is portable. It can potentially be used for applications in verification of questioned documents, checks, receipts and bank notes. Full article
(This article belongs to the Special Issue Document-Image Related Visual Sensors and Machine Learning Techniques)
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Open AccessArticle
The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data
Sensors 2019, 19(4), 809; https://doi.org/10.3390/s19040809
Received: 29 January 2019 / Revised: 12 February 2019 / Accepted: 13 February 2019 / Published: 16 February 2019
PDF Full-text (996 KB) | HTML Full-text | XML Full-text
Abstract
Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to [...] Read more.
Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results. Full article
(This article belongs to the Special Issue Document-Image Related Visual Sensors and Machine Learning Techniques)
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