<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=ISO-8859-15">
</head>
<body text="#000000" bgcolor="#FFFFFF">
<p>[sorry for multiple posting]<br>
</p>
<p>Dear colleagues,<br>
please find below the Call for Paper submission for a Special
Issue on "Bioinspired Computer Vision" on Electronics (ISSN
2079-9292). This special issue belongs to the section "Computer
Science & Engineering".<br>
<br>
Guest Editor: Dr. Manuela Chessa - University of Genoa, Italy<br>
<br>
<b>Deadline for manuscript submissions: 29 February 2020. </b><br>
<br>
Full details at:<br>
<a
href="https://www.mdpi.com/journal/electronics/special_issues/bioinspired_computer_vision">https://www.mdpi.com/journal/electronics/special_issues/bioinspired_computer_vision</a><br>
<br>
Contacts:<br>
<a class="moz-txt-link-abbreviated" href="mailto:manuela.chessa@unige.it">manuela.chessa@unige.it</a> (Guest Editor)<br>
<a class="moz-txt-link-abbreviated" href="mailto:stephanie.wang@mdpi.com">stephanie.wang@mdpi.com</a> (Assistant Editor)<br>
<br>
===================================<br>
Special Issue Information<br>
<br>
Bioinspired computer vision approaches usually aim to replicate
the results obtained by standard algorithms, by using neuromorphic
paradigms. The performance obtained by bioinspired approaches is
often lower than that of standard algorithms, both in terms of
reliability of the results and in terms of speed. Today, there are
many computer vision algorithms that are successfully applied in
several fields, such as robotics, autonomous navigation, video
surveillance, facial recognition, and, more recently, augmented
reality. On the other hand, it is well known that biological
vision systems are able to extract and analyze the information
that is contained in complex, cluttered, and noisy environments,
in order to solve vital tasks, such as navigating and recognizing
shapes and persons, finding food, or escaping from danger.
Biological visual systems are able to perform these tasks with
both high sensitivity and strong reliability. Moreover, they are
able to solve challenging computational problems, such as scene
segmentation, local and global optical flow computation, 3D
perception or extracting the meaning of complex objects or
movements, in an efficient and quick manner.<br>
<br>
The main aim of this Special Issue is to seek high-quality
submissions that present and discuss the recent achievements in
the development of bioinspired models for solving vision tasks,
especially focusing on real-world complex situations and
addressing modern benchmarking datasets.<br>
<br>
The topics of interest include, but are not limited to the
following:<br>
<br>
-Bioinspired computer vision for motion and stereo analysis<br>
-Bioinspired computer vision for scene understanding<br>
-Bioinspired computer vision for face, gesture, and shape
recognition<br>
-Bioinspired computer vision for the real world: Robustness,
learning, adaptability, self-assessment, and failure recovery<br>
-Performance evaluation of bioinspired approaches<br>
-Bioinspired computer vision for surveillance and security
applications<br>
-Bioinspired computer vision for virtual and augmented reality
applications<br>
-Hardware and high-performance implementations of bioinspired
approaches<br>
-Bioinspired computer vision and deep learning<br>
<br>
Guest Editor: Dr. Manuela Chessa - University of Genoa, Italy<br>
<br>
</p>
<pre class="moz-signature" cols="72">--
Manuela Chessa, PhD
Assistant Professor in Computer Science
University of Genova - Dept. of Informatics, Bioengineering, Robotics, and Systems Engineering
Via Dodecaneso 35, 16146 Genova - ITALY
Office: Valletta Puggia 3rd floor, room 329
Tel: +39 010 353 6663
URL: <a class="moz-txt-link-abbreviated" href="http://www.dibris.unige.it/en/chessa-manuela">www.dibris.unige.it/en/chessa-manuela</a></pre>
</body>
</html>