[visionlist] Call for papers – 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019) – London – Deadline: 7 December 2018


[visionlist] Call for Book Chapters on Deep Biometrics

Dear Colleagues,


We would like to invite you to contribute a chapter for the upcoming volume entitled “Deep Biometrics†to be published by Springer, the largest global
scientific, technical, and medical ebook publisher. The volume will be available both in print and in ebook format by late 2018/early 2019 on SpringerLink, one of the leading science portals that includes more than 8 million documents, an ebook collection
with more than 160,000 titles, journal archives digitized back to the first issues in the 1840s, and more than 30,000 protocols and 290 reference works.


Below is a short description of the volume:

Recent development in machine learning, particularly deep learning, has brought out drastic impact on Biometrics, which is a classic topic to utilize Machine
Learning for biometric identification. Particularly, Deep Learning can benefit from the training with large unlabelled datasets via semi-supervised or unsupervised learning.


This book aims to highlight recent research advances in biometrics using semi-supervised and unsupervised new methods such as Deep Neural Networks, Deep
Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, Ensemble Methods, and so on, and exploit these novel methods in the emerging new areas such as privacy and security issues, cancellable biometrics and soft biometrics, smart
cities, big biometric data, biometric banking, medical biometrics, and healthcare biometrics, etc..


The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy. Topics of interest include:
(but not limited to)

• Deep Learned Biometric Features

• Convolutional Neural networks

• Deep Stacked Autoencoder

• Deep Face Detection

• Deep Gait Recognition

• Biometrics in Cybersecurity

• Biometrics in Cognitive Robot

• Healthcare Biometrics

• Medical Biometrics

• Biometrics in Social Computing

• Biometric Block Chain

• Privacy and Security Issues

• Iris, Fingerprints, DNA, Palmprints

• Gait, EEG, Heart rates

• Multimodal Fusion

• Soft Biometrics

• Cancellable Biometrics

• Big data issues in Biometrics

• Biometrics for Internet of things

Each contributed chapter is expected to present a novel research study, a comparative study, or a survey of the literature. Note that there will be no publication
fees for accepted chapters.


Important Dates:

  Submission of abstracts: as soon as possible

  Notification of initial editorial decisions: 2-3 days after abstract submission

  Submission of full-length chapters Dec 15, 2018

  Notification of final editorial decisions Jan 15, 2019

  Submission of revised chapters Feb 15, 2019


All submissions should be done via EasyChair:


Original artwork and a signed copyright release form will be required for all accepted chapters. For author instructions, please visit:


Please feel free to contact us via email (perceptualscience@outlook.com,
or any editors below) regarding your chapter ideas.

Editorial Board

• Dr Richard Jiang

   Computer and Information Sciences,

   Northumbria University, United Kingdom


ʉۢ Dr Weizhi Meng

   Applied Mathematics & Computer Science

   Technical University of Denmark, Denmark


• Professor Chang-Tsun Li

   School of Computing and Mathematics,

   Charles Sturt University, Australia


ʉۢ Professor Christophe Rosenberger

   Computer Security





All questions about submissions can be emailed to
or any editor in the board.


Many thanks!


Kind Regards,

Editors of the Book


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this message or any attachment is virus free or has not been intercepted and/or amended.

This message is intended solely for the addressee and may contain confidential and/or legally privileged information. Any use, disclosure or reproduction without the sender’s explicit consent is unauthorised and may be unlawful. If you have received this message
in error, please notify Northumbria University immediately and permanently delete it. Any views or opinions expressed in this message are solely those of the author and do not necessarily represent those of the University. Northumbria University email is provided
by Microsoft Office365 and is hosted within the EEA, although some information may be replicated globally for backup purposes. The University cannot guarantee that this message or any attachment is virus free or has not been intercepted and/or amended.

[visionlist] PhD Openings in Computational Photography and Signal Processing at Tsinghua University

[visionlist] [CFP] SPIE Multimodal Sensing and Artificial Intelligence: Technologies and Applications

Internationales Congress CenterMunich, Germany 24 – 27 June 2019


The conference goal is to provide a unique forum for discussing how Artificial Intelligence could provide benefits to multi-modal image analysis and processing. Multi-modal imaging refers to systems able to acquire multiple 2D or 3D information about real scenes, with different sensing modality (ex: 3D point clouds, visible and infrared images, thermal images, hyperspectral sensing, and so on) and is used on a broad range of sensing-based applications. Artificial Intelligence, on the other hand, found recently a new renaissance, thanks to Machine Learning and Deep Learning paradigms successfully applied for addressing very challenging image interpretation tasks. In this context, researchers, developers and practitioners are encouraged to present the latest advance, highlighting how multi-modal sensing technologies and applications can benefit from using Artificial Intelligence based methodologies. The conference, with a specific emphasis on exploiting Artificial Intelligence methodologies, is focused on both: a) the metric performance of sensors and algorithms for producing the most accurate and reliable geometric measurements and models; and b) applications in different fields. 

The conference targets topics related to multimodal imaging systems (calibration, performance, accuracy, etc.) and their application in various tasks such as object recognition, motion estimation, 3D reconstruction, autonomous mobile robot navigation, quality control, assembly in manufacturing, security, environment monitoring, medical imaging, holography, biomedical imaging. Themes such as industrial inspection, material and component testing, virtual museums, motion analysis, mobile robot navigation, marketing and tourism, human body modeling, maritime sciences, medicine, aerospace, automotive, agrifood, security and the exploration of remote and hazardous sites, just to name a few, provide the contexts in which multi-modal sensing and AI methodologies can be synergically applied. We invite submission of original research contributions, as well as demonstrations of successful applications in, but not limited to, the following technical areas Multimodal Sensing: Technology
3D passive sensors 

3D active sensors 

hyperspectral imaging 

light-field 3D sensing 

full-field methods for inspection (holography, shearography, DIC) 


interferometry . 
Multimodal Sensing: Processing
calibration and measurements 

pose estimation 

image and range based modelling 

3D passive reconstruction 

3D active reconstruction 

motion analysis 

multiview analysis 

real-time processing technology 

expert system for detection and diagnosis of defects 

embedded vision systems. 
Multimodal Sensing: Applications
object recognition 

scene interpretation 

autonomous robot navigation 



environmental monitoring 

surface quality control 

industrial Inspection 

face analysis 

nondestructive testing methods 

noninvasive inspection techniques 

automation for material testing 

development compact systems for in-situ inspection 

monitoring of civil infrastructures (bridges, highways, buildings, Railways) 

sensors for homeland-security 

innovative systems for imaging and display systems 

intelligent farm 

intelligent factory 

intelligent building 

intelligent systems in health and medicine 


Important Dates

Abstract Due:9 January 2019Author Notification:26 February 2019Manuscript Due Date:17 April 2019

Conference Chair

Ettore Stella, CNR (Italy) 

Conference Co-Chairs

Shahriar Negahdaripour, Univ. of Miami (United States) 

Dariusz Ceglarek, The Univ. of Warwick (UK) 

Christian Möller, Fraunhofer-Institut für Fertigungstechnik und Angewandte Materialforschung (Germany) 

Program Committee

Andrei Asiminov, Technische Univ. Delft (Netherlands) 

Salah Bourennane, Institut Fresnel (France) 

Cosimo Distante, CNR/Univ. del Salento (Italy) 

Pietro Ferraro, Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello” (Italy) 

Marc P. Georges, Liège Univ. (Belgium) 

Antonio Lanzotti, Univ. degli Studi di Napoli Federico II (Italy) 

Luiz Marcos Garcia Gonçalves, UFRN (Brazil) 

Michele Meo, Univ. of Bath (United Kingdom) 

Thomas B. Moeslund, Aalborg Univ. (Denmark) 

Nicola Mosca, CNR (Italy) 

Vito Pagliarulo, Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello” (Italy) 

Clive Roberts, The Univ. of Birmingham (United Kingdom) 

Pierre R. Slangen, Mines Alès (France) 

Rocco Zito, Flinders Univ. (Australia) 

[visionlist] Computer Scientist in Neuroscience Lab

Computer Scientist / Machine Learning Specialist in Neuroscience lab

Full-time. Vienna, Austria.

The Zimmer lab in the Department of Neurobiology (University of Vienna) located at the IMP, part of the Vienna Biocenter Campus (https://ift.tt/2QyWagw), is seeking for a highly motivated computer scientist. Our lab is interested in how neural network dynamics in the brain perform computations to process information. This is the key problem in neuroscience puzzling researchers world- wide. We are taking a holistic approach to solve this problem, using a whole brain activity imaging technique, in a small model animal called C. elegans. We are able to record the activity of every single neuron, in real-time, while animals are freely behaving. This is impossible in any other model system, but key to understand how the brain functions. The challenge is to extract meaningful results from these complex datasets, here is where your role will be essential.

What you will do

●  Develop state-of-the-art computer vision and machine learning algorithms for data extraction from 4D image volumes.

●  Develop machine learning approaches for classification of anatomical structures.

●  Implementation and evaluation of computational approaches for the analysis of animal behavior andhigh dimensional time-series of neuronal activity.

●  Communicate your results to biologists, physicists, mathematicians.

●  Support the team with your computational skills.

What we expect

●  PhD degree or professional working experience in computer science or related subject

●  Experience and strong interest in computer vision, machine learning

●  Working knowledge of Python, MATLAB

●  Familiarity with Linux & bash scripting

●  Interest in biology and neuroscience in particular

●  Independent, out-of-the-box thinking

●  Willingness to learn new things

What we can offer

●  Challenging projects in a high profile scientific environment.

●  International interdisciplinary team.

●  Professional development.

●  Flexible working hours.

●  Starting date: as soon as possible.

Please submit your application to Prof. Dr. Manuel Zimmer (manuel.zimmer@imp.ac.at)

[visionlist] Faculty Position in Psychology

The Faculty of Arts and Social Sciences, Sabancı University, invites applications for a full-time faculty position in Psychology. It is for candidates who have recently completed their PhD (or are near completion) as well as mid-career applicants.

We are interested in candidates with a strong research record in any area of psychology (especially Clinical, Cognitive, Social, Developmental, or Neuroscience). Candidates must show clear potential to produce top quality scholarly publications, and should be able to sustain strong international academic and research connections.

Sabancı University is an innovative and world-leading private institution, currently rated to be the best university in Turkey (THE). It is strongly committed to interdisciplinary research and teaching. The Faculty of Arts and Social Sciences is organized around multidisciplinary degree programs including Psychology, Political Science, International Studies, Cultural Studies, Visual Arts, and Economics. The university admits the highest-ranking students from Turkey, and significant numbers of students from abroad. Faculty members are provided with excellent research support, private health insurance, and housing facilities on campus.

Review of applications will begin immediately and continue until the position is filled. The successful candidate is expected to start in September 2019.

Interested applicants should submit (i) a cover letter (ii) research statement (iii) teaching documents and statement providing details on the applicant’s teaching experience and preferences, (iv) a CV, (v) at least two research papers, (vi) three letters of reference in the online application form of the university at:



For further questions you can contact:

Olesya Blazhenkova, Psychology Program Coordinator, olesya@sabanciuniv.edu

İnci Ceydeli, Faculty Administrative Manager, inci@sabanciuniv.edu

[visionlist] Postdoc position in computational modeling of human object recognition at NYU

Announcing a 3-year postdoctoral research position in Denis Pelli’s lab at NYU in Psychology, affiliated with Center for Neural Science, supported by NIH grant to Denis Pelli (PI), Jon Winawer, & Yann LeCun, in computational modeling of human object recognition in clutter. Crowding is a computational bottleneck in human object recognition. Is crowding just a nuisance, or is it the key to understanding how an object is recognized by a limited number of neurons? A trained ConvNet mimics many aspects of human letter recognition within a single crowding unit (Ziskind et al. 2014). However, some things cannot be recognized by people unless they extend across several crowding units, and human learning generalizes across scale and other dimensions. We’d like to extend our modeling of object recognition to explain that. Ongoing results on efficiency (contrast thresholds in noise) and the need for several crowding units provide modeling clues (Pelli 2018). fMRI collaboration with Winawer’s lab is documenting conservation of crowding distance in area V4 (Zhou et al. 2018). 

Pelli’s lab is in the Psychology Dept., on the same elevator as the Center for Neural Science and our 3T magnet, two blocks from Courant and Facebook. Salary will be $48,432 or higher, based on experience.

For informal inquiries, email denis.pelli@nyu.edu

To apply, please visit: https://ift.tt/2FeGVI7

Ziskind, A.J., Hénaff, O., LeCun, Y., & Pelli, D.G. (2014) The bottleneck in human letter recognition: A computational model. Vision Sciences Society, St. Pete Beach, Florida, May 16-21, 2014, 56.583. https://ift.tt/2qElTbW

D. Pelli (2018) Despite a 100-fold drop in cortical magnification, a fixed-size letter is recognized equally well at eccentricities of 0 to 20 deg. How can this be?. Journal of Vision 2018;18(10):26. https://ift.tt/2Fl7nQv

J. Zhou, N. Benson, J. Winawer, D. Pelli (2018) Conservation of crowding distance in human V4. Journal of Vision2018;18(10):856. https://jov.arvojournals.org/article.aspx?articleid=2699845&resultClick=1