[robotics-worldwide] [journals] International Journal of Computer Vision and Image Processing (IJCVIP) 7(2)

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[robotics-worldwide] [journals] International Journal of Computer Vision and Image Processing (IJCVIP) 7(2)

Jose Garcia
Dear colleagues,

 

We are pleased to provide you with the official announcement for the latest
issue of the International Journal of Computer Vision and Image Processing
7(2).


Abstract Announcement for International Journal of Computer Vision and Image
Processing (IJCVIP) 7(2)


The contents of the latest issue of:
International Journal of Computer Vision and Image Processing (IJCVIP)
Volume 7, Issue 2, April - June 2017
Indexed by: INSPEC
For a complete list of indexing and abstracting services that include this
journal, please reference the bottom of this announcement.
Published: Quarterly in Print and Electronically
ISSN: 2155-6997; EISSN: 2155-6989;
Published by IGI Global Publishing, Hershey, USA
 
<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_journal_international-2Djournal-2Dcomputer-2Dvision-2Dim&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=OjLG_px92g_yXTjGCe-nS8XWPUk9WSgvp1c1Dl8oR44&e=
age/1181> www.igi-global.com/ijcvip

Editor-in-Chief: Jose Garcia-Rodriguez (University of Alicante, Spain)

Note: The International Journal of Computer Vision and Image Processing
(IJCVIP) has an Open Access option, which allows individuals and
institutions unrestricted access to its published content. Unlike
traditional subscription-based publishing models, open access content is
available without having to purchase or subscribe to the journal in which
the content is published. All IGI Global manuscripts are accepted based on a
double-blind peer review editorial process.

GUEST EDITORIAL PREFACE

Special Issue on International Conference on Recent Trends in Image
Processing and Pattern Recognition (rtip2r), 2016

K.C. Santosh (Department of Computer Science, The University of South
Dakota, Vermillion, SD, USA)

To obtain a copy of the Guest Editorial Preface, click on the link below.
 
<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_pdf.aspx-3Ftid-3D183656-26ptid-3D158630-26ctid-3D15-26t-3Dspecia&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=SP6WLUaEUkcjuzG0_j__st8nlAhQJURJ2bvwLt8xqU0&e=
l%20issue%20on%20international%20conference%20on%20recent%20trends%20in%20im
age%20processing%20and%20pattern%20recognition%20%28rtip2r%29,%202016>
www.igi-global.com/pdf.aspx?tid=183656&ptid=158630&ctid=15&t=Special Issue
on International Conference on Recent Trends in Image Processing and Pattern
Recognition (rtip2r), 2016

ARTICLE 1

A Deep Learning Approach for Hepatocellular Carcinoma Grading

Vitoantonio Bevilacqua (Department of Electrical and Information Engineering
(DEI), Polytechnic University of Bari, Bari, Italy), Antonio Brunetti
(Department of Electrical and Information Engineering (DEI), Polytechnic
University of Bari, Bari, Italy), Gianpaolo Francesco Trotta (Department of
Mechanics, Mathematics and Management (DMMM), Polytechnic University of
Bari, Bari, Italy), Leonarda Carnimeo (Department of Electrical and
Information Engineering (DEI), Polytechnic University of Bari, Bari, Italy &
Apulia Intelligent Systems Ltd, Bari, Italy), Francescomaria Marino
(Department of Electrical and Information Engineering (DEI), Polytechnic
University of Bari, Bari, Italy), Vito Alberotanza (Interdisciplinary
Department of Medicine - Section of Diagnostic Imaging, University of Bari,
Bari, Italy), Arnaldo Scardapane (Interdisciplinary Department of Medicine -
Section of Diagnostic Imaging, University of Bari, Bari, Italy)

Introduction and objective: Computer Aided Decision (CAD) systems based on
Medical Imaging could support radiologists in grading Hepatocellular
carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding
medical invasive procedures such as biopsies. The identification and
characterization of Regions of Interest (ROIs) containing lesions is an
important phase allowing an easier classification in two classes of HCCs.
Two steps are needed for the detection of lesioned ROIs: a liver isolation
in each CT slice and a lesion segmentation. Materials and methods: Materials
consist in abdominal CT hepatic lesion from 18 patients subjected to liver
transplant, partial hepatectomy, or US-guided needle biopsy. Several
approaches are implemented to segment the region of liver and, then, detect
the lesion ROI. Results: A Deep Learning approach using Convolutional Neural
Network is followed for HCC grading. The obtained good results confirm the
robustness of the segmentation algorithms leading to a more accurate
classification.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-deep-learning-approach-for-hepatocellular-carci
noma-grading/183657

To read a PDF sample of this article, click on the link below.
 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_viewtitlesample.aspx-3Fid-3D183657&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=fyxbKrlcknafWc21E68DbneIEDfMqU7Znvur5QCUk6k&e=>
www.igi-global.com/viewtitlesample.aspx?id=183657

ARTICLE 2

Face Match for Family Reunification: Real-World Face Image Retrieval

Eugene Borovikov (Communications Engineering Branch, U.S. National Library
of Medicine, Bethesda, MD, USA), Szilard Vajda (Central Washington
University, Ellensburg, WA, USA), Michael Gill (U.S. National Library of
Medicine, Bethesda, MD, USA)

Despite the many advances in face recognition technology, practical face
detection and matching for unconstrained images remain challenging. A
real-world Face Image Retrieval (FIR) system is described in this paper. It
is based on optimally weighted image descriptor ensemble utilized in
single-image-per-person (SIPP) approach that works with large unconstrained
digital photo collections. The described visual search can be deployed in
many applications, e.g. person location in post-disaster scenarios, helping
families reunite quicker. It provides efficient means for face detection,
matching and annotation, working with images of variable quality, requiring
no time-consuming training, yet showing commercial performance levels.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/face-match-for-family-reunification/183658

To read a PDF sample of this article, click on the link below.
 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_viewtitlesample.aspx-3Fid-3D183658&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=yy5G1YCaE21Z1ozARFsPsh-di0KrlkX2R0YKLn25C5o&e=>
www.igi-global.com/viewtitlesample.aspx?id=183658

ARTICLE 3

Foreign Circular Element Detection in Chest X-Rays for Effective Automated
Pulmonary Abnormality Screening

Fatema Tuz Zohora (Department of Computer Science, University of South
Dakota, Vermillion, SD, USA), K.C. Santosh (Department of Computer Science,
University of South Dakota, Vermillion, SD, USA)

In automated chest X-ray screening (to detect pulmonary abnormality:
Tuberculosis (TB), for instance), the presence of foreign element such as
buttons and medical devices hinders its performance. In this paper, using
digital chest radiographs, the authors present a new technique to detect
circular foreign element, within the lung regions. They first compute edge
map by using several different edge detection algorithms, which is followed
by morphological operations for potential candidate selection. These
candidates are then confirmed by using circular Hough transform (CHT). In
their test, the authors have achieved precision, recall, and F1 score of
96%, 90%, and 92%, respectively with lung segmentation. Compared to
state-of-the-art work, their technique excels performance in terms of both
detection accuracy and computational time.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/foreign-circular-element-detection-in-chest-x-ray
s-for-effective-automated-pulmonary-abnormality-screening/183659

To read a PDF sample of this article, click on the link below.
 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_viewtitlesample.aspx-3Fid-3D183659&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=kCHYt6grkJtA2gZ44aAbChP-PEmkoYpK_eYKPFy8pqo&e=>
www.igi-global.com/viewtitlesample.aspx?id=183659

ARTICLE 4

Performance Analysis of Anisotropic Diffusion Based Colour Texture
Descriptors in Industrial Applications

Prakash S. Hiremath (Department of Computer Science (MCA), KLE Technological
University, BVBCET Campus, Hubli, India), Rohini A. Bhusnurmath (Department
of P.G. Studies and Research in Computer Science, Gulbarga University,
Kalaburagi, India)

A novel method of colour texture analysis based on anisotropic diffusion for
industrial applications is proposed and the performance analysis of colour
texture descriptors is examined. The objective of the study is to explore
different colour spaces for their suitability in automatic classification of
certain textures in industrial applications, namely, granite tiles and wood
textures, using computer vision. The directional subbands of digital image
of material samples obtained using wavelet transform are subjected to
anisotropic diffusion to obtain the texture components. Further, statistical
features are extracted from the texture components. The linear discriminant
analysis is employed to achieve class separability. The texture descriptors
are evaluated on RGB, HSV, YCbCr, Lab colour spaces and compared with gray
scale texture descriptors. The k-NN classifier is used for texture
classification. For the experimentation, benchmark databases, namely,
MondialMarmi and Parquet are considered. The experimental results are
encouraging as compared to the state-of-the-art-methods.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/performance-analysis-of-anisotropic-diffusion-bas
ed-colour-texture-descriptors-in-industrial-applications/183660

To read a PDF sample of this article, click on the link below.
 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_viewtitlesample.aspx-3Fid-3D183660&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=TAT60TwpZaeuSZKSwOKf18H8PlAXV_lAPCsbve0rGs0&e=>
www.igi-global.com/viewtitlesample.aspx?id=183660

ARTICLE 5

Feature Selection of Interval Valued Data Through Interval K-Means
Clustering

D. S. Guru (Department of Studies in Computer Science, University of Mysore,
Mysore, India), N. Vinay Kumar (Department of Studies in Computer Science,
University of Mysore, Mysore, India), Mahamad Suhil (Department of Studies
in Computer Science, University of Mysore, Mysore, India)

This paper introduces a novel feature selection model for supervised
interval valued data based on interval K-Means clustering. The proposed
model explores two kinds of feature selection through feature clustering
viz., class independent feature selection and class dependent feature
selection. The former one clusters the features spread across all the
samples belonging to all the classes, whereas the latter one clusters the
features spread across only the samples belonging to the respective classes.
Both feature selection models are demonstrated to explore the generosity of
clustering in selecting the interval valued features. For clustering, the
kernel of the K-means clustering has been altered to operate on interval
valued data. For experimentation purpose four standard benchmarking datasets
and three symbolic classifiers have been used. To corroborate the
effectiveness of the proposed model, a comparative analysis against the
state-of-the-art models is given and results show the superiority of the
proposed model.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/feature-selection-of-interval-valued-data-through
-interval-k-means-clustering/183661

To read a PDF sample of this article, click on the link below.
 <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_viewtitlesample.aspx-3Fid-3D183661&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=-LGHLtzJ2idxvg1O1XmeLdC3KnVjTvLIZxTh5d9FVv0&e=>
www.igi-global.com/viewtitlesample.aspx?id=183661

ARTICLE 6

Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on
Script Identification

S. K. Obaidullah (Department of Computer Science and Engineering, Kolkata,
India), K. C. Santosh (Department of Computer Science, The University of
South Dakota, Vermillion, SD, USA), Chayan Halder (West Bengal State
University, Kolkata, India), Nibaran Das (Jadavpur University, Kolkata,
India), Kaushik Roy (West Bengal State University, Kolkata, India)

Document analysis research starves from the availability of public datasets.
Without publicly available dataset, one cannot make fair comparison with the
state-of-the-art methods. To bridge this gap, in this paper, the authors
propose a word-level document image dataset of 13 different Indic languages
from 11 official scripts. It is composed of 39K words that are equally
distributed i.e., 3K words per language. For a baseline results, five
different classifiers: multilayer perceptron (MLP), fuzzy unordered rule
induction algorithm (FURIA), simple logistic (SL), library for linear
classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used
with three state-of-the-art features: spatial energy (SE), wavelet energy
(WE) and the Radon transform (RT), including their possible combinations.
The authors observed that MLP provides better results when all features are
used, and achieved the bi-script accuracy of 99.24% (keeping Roman common),
98.38% (keeping Devanagari common) and tri-script accuracy of 98.19%
(keeping both Devanagari and Roman common).

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/word-level-multi-script-indic-document-image-data
set-and-baseline-results-on-script-identification/183662

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=183662

  _____  

For full copies of the above articles, check for this issue of the
International Journal of Computer Vision and Image Processing (IJCVIP) in
your institution's library. This journal is also included in the IGI Global
aggregated "InfoSci-Journals" database:
<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.igi-2Dglobal.com_e-2Dresources_infosci-2Ddatabases_infosci-2Djournals_&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=vMKzbRtitLZJTfTtL-T2wwErdG3Xql6Wx0uXy3X6J9A&e=>
www.igi-global.com/isj.

  _____  

CALL FOR PAPERS

Mission of IJCVIP:

The mission of the International Journal of Computer Vision and Image
Processing (IJCVIP)  is to provide the worldwide community with a platform
for the latest research and advances in computer vision technologies,
multimedia applications, and data image processes. IJCVIP focuses on
interdisciplinary methods and state-of-the-art research among various
disciplines related to the science and technology of machines. This journal
solicits reviews on benchmarks, evaluations of systems, tools, and
significant research activities around the globe.

Indices of IJCVIP:

* ACM Digital Library
* Bacon's Media Directory
* Cabell's Directories
* DBLP
* Google Scholar
* INSPEC
* JournalTOCs
* MediaFinder
* ProQuest Advanced Technologies & Aerospace Journals
* ProQuest Computer Science Journals
* ProQuest Illustrata: Technology
* ProQuest SciTech Journals
* ProQuest Technology Journals
* The Standard Periodical Directory
* Ulrich's Periodicals Directory

Coverage of IJCVIP:

Topics to be discussed in this journal include (but are not limited to) the
followings:

* Algorithms
* Applications
* Computational models
* Data structures and databases
* Identification
* Machine intelligence
* Matching
* Motion
* Range
* Recognition
* Shape
* Systems and tools

Interested authors should consult the journal's manuscript submission
guidelines
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.igi-2Dglobal.com_calls-2Dfor-2Dpapers_international-2Djournal-2Dcomputer-2Dv&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=fhcM2hc2dUbW-Z7XuFvUP6TyFwqHT8NQy_YoKxZp_FI&e=
ision-image/1181>
www.igi-global.com/calls-for-papers/international-journal-computer-vision-im
age/1181

 

 

Jose Garcia-Rodriguez, Phd

Associate Professor

Editor in Chief International Journal of Computer vision and Image
Processing

Vice-Dean International Relations Polytechnic University College

Director Phd Program in Computer Science

Director of NVIDIA GPU Research Center  & GPU Education Center

Dpt. Computer Technology

University of Alicante

PO Box. 99. 03080  Alicante (Spain)

tel: +34 965903400 ext. 2616 mobile: +34 610488989

fax: +34 965909643

email:  <mailto:[hidden email]> [hidden email]

skype: jose.garcia.alicante

 <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.dtic.ua.es_-7Ejgarcia_&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=-MV3jrFDTbUSXWOo0Vg78OoPv1qgPrkrjP8o5fSleJM&e=> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.dtic.ua.es_-7Ejgarcia_&d=DwICAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=SxSlDMc8D8Wx96htmUqMHUZEDDg9lY8KzDnwyhXyMGM&s=-MV3jrFDTbUSXWOo0Vg78OoPv1qgPrkrjP8o5fSleJM&e=

 

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