[robotics-worldwide] [journals] International Journal of Computer Vision and Image Processing (IJCVIP) 7(3)

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[robotics-worldwide] [journals] International Journal of Computer Vision and Image Processing (IJCVIP) 7(3)

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(3).


Abstract Announcement for International Journal of Computer Vision and Image
Processing (IJCVIP) 7(3)
The contents of the latest issue of:
International Journal of Computer Vision and Image Processing (IJCVIP)
Volume 7, Issue 3, July - September 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
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.

ARTICLE 1

A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments

Miguel A. Molina-Cabello (Department of Computer Languages and Computer
Science. University of Málaga, Málaga. Spain), Rafael Marcos Luque-Baena
(Department of Computer Languages and Computer Science. University of
Málaga, Málaga. Spain), Ezequiel López-Rubio (Department of Computer
Languages and Computer Science. University of Málaga, Málaga. Spain), Juan
Miguel Ortiz-de-Lazcano-Lobato (Department of Computer Languages and
Computer Science. University of Málaga, Málaga. Spain), Enrique Domínguez
(Department of Computer Languages and Computer Science. University of
Málaga, Málaga. Spain)

Automated video surveillance presents a great amount of applications and one
of them is traffic monitoring. Vehicle type detection can provide
information about the characteristics of the traffic flow to human traffic
controllers in order to facilitate their decision-making process. A video
surveillance system is proposed in this work to execute such classification.
First of all, a foreground detection and tracking object process has been
carried out. Once the vehicles are detected, a feature extraction method
obtains the most significant features of this detected vehicles. When the
extraction process is done, the vehicle types are determined by employing a
set of Growing Neural Gas neural networks. The performance of the proposal
has been analyzed from a qualitative and quantitative point of view by using
a set of benchmark traffic video sequences, with acceptable results.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-growing-neural-gas-approach-to-classify-vehicle
s-in-traffic-environments/188757

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188757

ARTICLE 2

Growing Neural Forest-Based Color Quantization Applied to RGB Images

Jesús Benito-Picazo (Department of Computer Languages and Computer Science,
University of Málaga, Málaga, Spain), Ezequiel López-Rubio (Department of
Computer Languages and Computer Science, University of Málaga, Málaga,
Spain), Enrique Domínguez (Department of Computer Languages and Computer
Science, University of Málaga, Málaga, Spain)

Although last improvements in both physical storage technologies and image
handling techniques have eased image managing processes, the large amount of
information handled nowadays constantly demands more efficient ways to store
and transmit image data streams. Among other alternatives for such purpose,
the authors find color quantization, which consists of color indexing for
minimal perceptual distortion image compression. In this context, artificial
intelligence-based algorithms and more specifically, Artificial Neural
Networks, have been consolidated as a powerful tool for unsupervised tasks,
and therefore, for color quantization purposes. In this work, a novel
approach to color quantization is presented based on the Growing Neural
Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of
trees is learnt instead of a general graph. Experimental results support the
use of GNF for image quantization tasks where it overcomes other
self-organized models including SOM, GHSOM and GNG. Future work will include
more datasets and different competitive models to compare to.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/growing-neural-forest-based-color-quantization-ap
plied-to-rgb-images/188758

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188758

ARTICLE 3

An Iterative Method for 3D Body Registration Using a Single RGB-D Sensor

Victor Villena-Martinez (Department of Computer Technology, University of
Alicante, Alicante, Spain), Andres Fuster-Guillo (Department of Computer
Technology, University of Alicante, Alicante, Spain), Marcelo Saval-Calvo
(Department of Computer Technology, University of Alicante, Alicante,
Spain), Jorge Azorin-Lopez (Department of Computer Technology, University of
Alicante, Alicante, Spain)

In this paper, the problem of 3D body registration using a single RGB-D
sensor is approached. It has been guided by three main requirements:
low-cost, unconstrained movement and accuracy. In order to fit them, an
iterative registration method for accurately aligning data from single RGB-D
sensor is proposed. The data is acquired while a person rotates in front of
the camera, without the need of any external marker or constraint about its
pose. The articulated alignment is carried out in a model-free approach in
order to be more consistent with the real data. The iterative method is
divided in stages, contributing to each other by the refinement of a
specific part of the acquired data. The exploratory results validate the
proposed method that is able to feed on itself in each iteration improving
the final result by a progressive iteration, with the required precision
under the conditions of affordability and unconstrained movement
acquisition.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/an-iterative-method-for-3d-body-registration-usin
g-a-single-rgb-d-sensor/188759

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188759

ARTICLE 4

A Compilation of Methods and Datasets for Group and Crowd Action Recognition

Luis Felipe Borja (Universidad Central del Ecuador, Quito, Ecuador), Jorge
Azorin-Lopez (Department of Computer Technology, University of Alicante,
Alicante, Spain), Marcelo Saval-Calvo (Department of Computer Technology,
University of Alicante, Alicante, Spain)

The human behaviour analysis has been a subject of study in various fields
of science (e.g. sociology, psychology, computer science). Specifically, the
automated understanding of the behaviour of both individuals and groups
remains a very challenging problem from the sensor systems to artificial
intelligence techniques. Being aware of the extent of the topic, the
objective of this paper is to review the state of the art focusing on
machine learning techniques and computer vision as sensor system to the
artificial intelligence techniques. Moreover, a lack of review comparing the
level of abstraction in terms of activities duration is found in the
literature. In this paper, a review of the methods and techniques based on
machine learning to classify group behaviour in sequence of images is
presented. The review takes into account the different levels of
understanding and the number of people in the group.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-compilation-of-methods-and-datasets-for-group-a
nd-crowd-action-recognition/188760

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188760

ARTICLE 5

A Review of Infrastructures to Process Big Multimedia Data

Jaime Salvador (Universidad Central del Ecuador, Quito, Ecuador), Zoila Ruiz
(Universidad Central del Ecuador, Quito, Ecuador), Jose Garcia-Rodriguez
(University of Alicante, Alicante, Spain)

In the last years, the volume of information is growing faster than ever
before, moving from small to huge, structured to unstructured datasets like
text, image, audio and video. The purpose of processing the data is aimed to
extract relevant information on trends, challenges and opportunities; all
these studies with large volumes of data. The increase in the power of
parallel computing enabled the use of Machine Learning (ML) techniques to
take advantage of the processing capabilities offered by new architectures
on large volumes of data. For this reason, it is necessary to find
mechanisms that allow classify and organize them to facilitate to the users
the extraction of the required information. The processing of these data
requires the use of classification techniques that will be reviewed. This
work analyzes different studies carried out on the use of ML for processing
large volumes of data (Big Multimedia Data) and proposes a classification,
using as criteria, the hardware infrastructures used in works of machine
learning parallel approaches applied to large volumes of data.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-review-of-infrastructures-to-process-big-multim
edia-data/188761

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188761

ARTICLE 6

Automatic Learning Improves Human-Robot Interaction in Productive
Environments: A Review

Mauricio Andres Zamora Hernandez (University of Costa Rica, San Pedro de
Montes de Oca, Costa Rica), Eldon Caldwell Marin (University of Costa Rica,
San Pedro de Montes de Oca, Costa Rica), Jose Garcia-Rodriguez (University
of Alicante, Alicante, Spain), Jorge Azorin-Lopez (Department of Computer
Technology, University of Alicante, Alicante, Spain), Miguel Cazorla
(University of Alicante, Alicante, Spain)

In the creation of new industries, products and services -- all of which are
advances of the Fourth Industrial Revolution -- the human-robot interaction
that includes automatic learning and computer vision are elements to
consider since they promote collaborative environments between people and
robots. The use of machine learning and computer vision provides the tools
needed to increase productivity and minimizes delivery reaction times by
assisting in the optimization of complex production planning processes. This
review of the state of the art presents the main trends that seek to improve
human-robot interaction in productive environments, and identifies
challenges in research as well as in industrial - technological development
in this topic. In addition, this review offers a proposal on the needs of
use of artificial intelligence in all processes of industry 4.0 as a crucial
linking element among humans, robots, intelligent and traditional machines;
as well as a mechanism for quality control and occupational safety.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/automatic-learning-improves-human-robot-interacti
on-in-productive-environments/188762

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=188762

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: 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
www.igi-global.com/calls-for-papers/international-journal-computer-vision-im
age/1181

Best regards.

Jose


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: [hidden email]
skype: jose.garcia.alicante
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.dtic.ua.es_-7Ejgarcia_&d=DwIFAw&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=4okc_unsaHJvTKKxIDcaCpl1AeaG183RcOmq1F-hc4w&s=KFk9rjzTYHzgd2YsafuFyDu7ocyxpR2h8ZS0nWoCrRM&e=



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