Call for artists and scientists
ILLUSIONS PARADE (part of ECVP 2015)
event on Tuesday, August 25, 6-11pm
& Furnace, Liverpool
NEW SUBMISSION DEADLINE 25 APRIL
Parade is an event aiming to bring together artists and scientists in a unique venue.
This is a fantastic opportunity for sharing expertise and passion and building
prepare one pdf file containing:
Your name, a brief description of your activity (max 100 words) and your
website address (or one useful link to learn more about your work);
A description (max 500 words) of one piece of work;
Three pictures to illustrate the proposed work. In case your artwork is a video
then submit a short preview (max 10 MB);
A list of support materials needed for the presentation.
and Camp & Furnace can provide tables, electrical outlets, some wall space,
poster boards and a limited number of projectors and screen upon request. The
organizers will try to help you with setting up. Exhibitors will be responsible
to bring along any other necessary equipment for their work.
send a single PDF file containing the above information and the video (if
new deadline is the 25th of April 2015 (5pm GMT).
decision will be made by the 14th May 2015.
to the Illusions parade webpage:
to the Illusions parade facebook page:
to the VSAC webpage:
2nd MICCAI CHALLENGE ON LIVER ULTRASOUND TRACKING 2015 (CLUST15)OCTOBER 9, 2015, MUNICH, GERMANY
http://clust.ethz.ch The purpose of this challenge is to present the current state-of-the-art in automated tracking of anatomical liver structures in ultrasound sequences. We invite participants to evaluate their methods on a dataset of at least 70 liver ultrasound sequences from volunteers under free-breathing. These will include 2D and 3D sequences, where liver landmarks and the liver boundary (e.g. diaphragm) annotated in the first frame shall be tracked for the whole sequence. Training data (40% of all data) and part of the test data (40%) will be released by mid of April. The remaining 20% of the test data will be distributed during the MICCAI event for on-site assessment of the methods. Tracking results shall be submitted by mid June. Tracking performance will be evaluated (based on manual annotations) by the organizers. Manuscripts (4-8 LNCS pages), describing the used algorithm and reporting the results for the test data, will be reviewed for acceptance to the full-day MICCAI CLUST event. The accepted contributions will be invited for on-site assessment and presented at the CLUST workshop. Their performance for the whole test dataset set will be summarized in a joint journal paper.IMPORTANT DATES Release of training data April 9, 2015 Release of test data April 15, 2015 Tracking results submission deadline June 15, 2015 Paper submission deadline June 22, 2015 Notification of acceptance July 7, 2015 Submission deadline for accepted papers July 22, 2015 Workshop date October 9, 2015ORGANIZING COMMITTEE Valeria De Luca, ETH Zurich, Switzerland Emma Harris, Institute of Cancer Research, Sutton, UK Ali Kamen, Siemens Corporate Technology, Princeton, USA Muyinatu Lediju Bell, Johns Hopkins University, Baltimore, USA Christine Tanner, ETH Zurich, Switzerland For more information and data download see http://clust.ethz.ch
On behalf of all organizers, Christine Tanner
26TH BRITISH MACHINE VISION CONFERENCE
7-10 September 2015, Swansea, UK
(CALL FOR PAPERS)
ANNOUNCEMENT: submission date is revised to
Monday 4 May to allow sufficient gap between major computer vision conference deadlines.
The British Machine Vision Conference (BMVC) is one of the major international conferences on computer vision and related areas. It is organised by the British Machine Vision Association (BMVA).
The 26th BMVC will be held at Swansea University Singleton Campus, 7th-10th September 2015. The University Singleton Campus is set in a rolling parkland overlooking the majestic sweep of Swansea Bay, the start of the famously
dramatic Gower coastline comprised of twenty-one bays and coves.
BMVC2015 is a high quality single-track conference, comprising oral presentations and poster sessions (with oral acceptance <10% in the last 6 years). The conference features two keynote presentations and a conference tutorial,
and has five associated workshops on the last day of the conference, including a PhD student workshop.
TOPICS OF INTEREST
Topics include, but are not limited to:
Statistics and machine learning for vision
Stereo, calibration, geometric modelling and processing
Face and gesture recognition
Early and biologically inspired vision
Motion, flow and tracking
Segmentation and grouping
Image processing techniques and methods
Texture, shape and colour
Document processing and recognition
Vision for quality assurance, medical diagnosis, etc.
Vision for visualisation, interaction, and graphics
Visualisation for computer vision
Visual analytics for computer vision
Object detection and recognition
Video analysis and event recognition
Illumination and reflectance
BMVC 2015 features a half day conference tutorial on the 7th September. The tutorial is particularly beneficial to research students and early career researchers who are working in this field. We are honoured, with Chris Bishop,
to have such a prominent researcher in the field of pattern recognition and machine learning to deliver the tutorial.
Prof. Christopher Bishop
Chief Research Scientist, Microsoft.
Chris Bishop has a B.A. in Physics with First Class Honours from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh with a thesis on quantum field theory supervised by David Wallace and Peter Higgs.
In 1998 he joined the Microsoft Research Laboratory in Cambridge where he became Deputy Managing Director, and later the Chief Research Scientist. He is a Partner in Microsoft, and is head of the Machine Learning and Perception group. In 2010 he was awarded
the accolade of Distinguished Scientist, representing the highest level of research distinction within Microsoft, and was the first person in Europe to hold this title. At the same time as he joined Microsoft Research, he was elected to a Chair of Computer
Science at the University of Edinburgh where he is a member of the Institute for Adaptive and Neural Computation in the School of Informatics. He is also a Fellow of Darwin College, Cambridge. He has been elected Fellow of the Royal Academy of Engineering,
and Fellow of the Royal Society of Edinburgh, and has been awarded two Honorary Doctor of Science degrees. His research interests include probabilistic approaches to machine learning, as well as their applications in industry, commerce, and healthcare.
BMVC invites two leading researchers in the field to present their work at the conference. We are grateful to the following speakers who have agreed to give keynote lectures at the conference.
Prof. Ron Kimmel
Technion – Israel Institute of Technology
Ron Kimmel is a Professor of Computer Science at the Technion where he holds the Montreal Chair in Sciences. He held a post-doctoral position at UC Berkeley and a visiting professorship at Stanford University. He has worked
in various areas of image and shape analysis in computer vision, image processing, and computer graphics. Kimmel’s interest in recent years has been non-rigid shape processing and analysis, medical imaging and computational biometry, numerical optimisation
of problems with a geometric flavour, and applications of metric geometry and differential geometry. Kimmel is an IEEE Fellow for his contributions to image processing and non-rigid shape analysis. He is an author of two books, an editor of one, and an author
of numerous articles. He is the founder of the Geometric Image Processing Lab. and a founder and advisor of several successful image processing and analysis companies.
Prof. Kristen Grauman
University of Texas at Austin
Kristen Grauman is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. Her research in computer vision and machine learning focuses on visual search and object recognition.
Before joining UT-Austin in 2007, she received her Ph.D. in the EECS department at MIT, in the Computer Science and Artificial Intelligence Laboratory. She is an Alfred P. Sloan Research Fellow and Microsoft Research New Faculty Fellow, a recipient of NSF
CAREER and ONR Young Investigator awards, the Regents' Outstanding Teaching Award from the University of Texas System in 2012, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial
Intelligence, and a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013. She and her collaborators were recognised with the CVPR Best Student Paper Award in 2008 for their work on hashing algorithms for large-scale image retrieval,
and the Marr Best Paper Prize at ICCV in 2011 for their work on modelling relative visual attributes.
Authors are invited to submit full-length high-quality papers in image processing and machine vision. Papers covering theory and/or application areas of computer vision are invited for submission. Submitted papers will be
refereed on their originality, presentation, empirical results, and quality of evaluation. All papers will be reviewed *doubly blind*, normally by three members of our international programme committee. Please note that BMVC is a single track meeting with
oral and poster presentations. Paper submission instructions are available at the conference website:
Paper submission site is now open:
Monday 4 May
Friday 03 July
Friday 24 July
Friday 07 August
Monday 07 September
Tuesday 08 – Thursday 10 September
Thursday 20 September
Xianghua Xie, Swansea University, UK
Mark Jones, Swansea University, UK
Gary Tam, Swansea University, UK
Post-doctoral fellowship: cortical integration timescales across the ventral stream
We are looking for postdoc candidates for a European Union financed project investigating how temporal integration properties change at different levels of the processing hierarchy for meaningful objects, scenes and events. The fellow would work together with David Melcher and Scott Fairhall. Specifically, we are seeking a postdoc with advanced expertise in fMRI as demonstrated by two first author fMRI publications. Salary would be in the range of circa 24,000 – 30,000 euro (net) per year, commensurate with experience.
The Center for Mind/Brain Sciences (CIMeC) at the University of Trento offers a vibrant research setting with state-of-the-art neuroimaging methodologies, including a research-only MRI scanner, MEG, EEG and TMS, as well as behavioral, eye tracking and motion tracking laboratories.
Melcher Active Perception Lab: http://ift.tt/1HWNwNA
For informal inquiries about this position email:
david (dot) melcher (AT) unitn (dot) it
Neural Metrics 2.0 : Connectomics & Large-Scale Methods
The Donders Institute for Brain, Cognition and Behaviour/Radboud University is organizing a summer school on neural metrics with the aim to get participants acquainted with the quantitative analysis of neural organisation and function. In Neural Metrics 2.0 we will build on the success from the previous year, focussing on methods for understanding brain networks such as connectomics as well as large scale and Bayesian methods, with world class speakers, hands-on tutorials, student projects and an interactive debate. The topics covered range from cellular connectomics to human functional connectomics.
The course is designed for PhD students and starting postdoctoral researchers working at the interface between cognitive neuroscience and the application of advanced methods. Please consult the Radboud Summer school website (http://ift.tt/1mIHQjQ) for details on the program, social events and registration. Further details for the Neural Metrics Summer School can be found below and on the website (http://ift.tt/1MDj6UR).
Dates : Monday 10 August – Friday 14 August 2015 (1 week)
Application Deadline : 15 June 2015
Course leaders :
Bernhard Englitz, Marcel van Gerven, Fleur Zeldenrust, Tansu Celikel
Donders Institute for Brain, Cognition and Behaviour & Radboud University
Participant profile :
This course was developed for PhD students and early postdoctoral researchers working at the interface between cellular and cognitive neuroscience requiring advanced methods of analysis. This includes research in the field of Neuroscience with an MSc in Biology, Computer Science, Psychology, Physics, Al, Mathematics, Engineering or a similar major.
Admission requirements :
As part of the admission procedure, we ask you to send us your CV and a brief motivation letter in which you explain your interest in our course.
Course fee : 600 Euros
The course fee includes the registration fee, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities. Accommodation is available for the course participants (additional charges apply). For details please see http://ift.tt/1CQWdp8
• 15% discount for students and PhD candidates from Radboud University and partner universities,
• 10% Early bird discount deadline: 1 April 2015
***********************************************CFP – DLMIA 2015 – a MICCAI workshop***********************************************
1st Workshop on Deep Learning in Medical Image Analysis (in conjunction with MICCAI 2015)
Call for Papers
Deep learning has produced promising results outperforming some state-of-the-art approaches for a couple of problems, such as face detection and recognition, speech recognition and image classification. It is expected that these algorithms can have a large impact on medical image analysis applications, such as computer-aided diagnosis, image segmentation, image annotation and retrieval, image registration and multimodal image analysis. However, only a few works have used deep learning methods in the context of medical-oriented applications, such as breast cancer and skin lesion detection, organs recognition and image-based disease identification.
Additionally, there is a little effort on model selection of deep learning techniques, which poses an interesting problem, since we may face hundreds of parameters, being a near-exhaustive search on this high-dimensional search space impractical. The problem gets worse in large image-based datasets, which have been commonly used in several recent papers. Given the large amount of parameters, some authors have argued that a random search may perform satisfactory well for some applications. However, a hand tuning of the parameters may limit our understanding about how well the techniques can generalize and describe data.
Deep Learning in Medical Image Analysis (DLMIA 2015) is the first workshop in conjunction with MICCAI 2015 that aims at fostering the area of computer-aided medical diagnosis, as well as meta-heuristic-based model selection concerning deep learning techniques.
– Image description by means of deep learning techniques;
– Medical imaging-based diagnosis using deep learning;
– Medical signal-based diagnosis using deep learning;
– Medical image reconstruction using deep learning;
– Deep learning model selection;
– Meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures;
– Deep learning-oriented applications.
– Full Paper Submission: June 10th, 2015
– Notification of Acceptance: June 30th, 2015
– Camera-ready Version: July 10th, 2015
– Conference Date: October 5th, 2015 (TBD)
Juergen Schmidhuber, IDSIA, Switzerland
Gustavo Carneiro, University of Adelaide
João Manuel R. S. Tavares, Universidade do Porto
Andrew P. Bradley, University of Queensland
João Paulo Papa, Universidade Estadual Paulista
Jacinto C. Nascimento, Instituto Superior Tecnico
Jaime S. Cardoso, Universidade do Porto
Zhi Lu, University of Adelaide
Vasileios Belagiannis, Technische Universität München