BMVC 2016 Final Call for Papers (Deadline Extension)
27th British Machine Vision Conference
19-22 September 2016, York, UK
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 27th BMVC will be held at the University of York, 19th-22nd September 2016. The university is set on the outskirts of the historic walled city of York which features Roman and Viking history and is home to a number of world renowned tourist attractions ranging from the gothic cathedral York Minster to the National Railway Museum.BMVC 2016 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 associated workshops on the last day of the conference, including a PhD student workshop.
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.Topics include, but are not limited to:
â€¢ Statistics and machine learning for vision
â€¢ Stereo, calibration, geometric modelling and processing
â€¢ Face and gesture recognition
â€¢ Early and biologically inspired vision
â€¢ Motion, flow and tracking
â€¢ Segmentation and grouping
â€¢ Model-based vision
â€¢ Image processing techniques and methods
â€¢ Texture, shape and colour
â€¢ Video analysis
â€¢ Document processing and recognition
â€¢ Vision for quality assurance, medical diagnosis, etc.
â€¢ Vision for visualization, interaction, and graphics
â€¢ Object detection and recognition
â€¢ Video analysis and event recognition
â€¢ Illumination and reflectance
Accepted papers will be included the conference proceedings published and DOI indexed by BMVA.
Submission instructions, paper templates and other details are available on the conference website: http://ift.tt/1NFn396
Paper submission is via CMT at: http://ift.tt/21yx8vM
The submission deadline has been extended to 13th May. Please note that the new deadline is firm. There will be no further extension.
Submission deadline (EXTENDED): 13th May 2016 (11.59pm, Pacific time)
Author notification: 15th July 2016
Camera ready deadline (including 1 page abstract): 29th July
Conference tutorial: Monday 19th September 2016
Main conference: Tuesday 20th â€“ Thursday 22nd September 2016
JOURNAL SPECIAL ISSUE A selection of the best papers will appear in a special edition of the International Journal of Computer Vision (IJCV).
Katsushi IkeuchiUniversity of Tokyo Raquel UrtasunUniversity of Toronto
Abhijeet GhoshImperial College, LondonTopic: Measurement Based Appearance Modelling
Apologies for cross-postingsCall for challenge participationFourth Emotion Recognition in the Wild (EmotiW) Challenge 2016https://sites.google.com/site/emotiw2016/@ ACM International Conference on Multimodal Interaction 2016, Tokyo
position is available in the Vision and Memory Lab of Dr. Timothy Brady (http://ift.tt/1rj7UXg). The lab is based in the UC San Diego Department of Psychology. Initial appointment is for one year, with
flexible start date and opportunity for renewal.
The lab’s research
lies at the intersection of visual cognition and memory. Particular research
topics include the nature of visual working memory capacity, the format of
visual long-term memory representations, and the relationship between ensemble
statistics and scene recognition. We use a variety of methodologies in the
lab, including psychophysics, computational modeling (connectionist, Bayesian, and information theoretic) and EEG.
We are seeking
highly qualified individuals with a recent or soon-to-be-acquired Ph.D. in a
relevant field (e.g., Psychology, Neuroscience, Cognitive Science, Computer
Science), strong quantitative and technical skills, and interests in visual
cognition, visual perception and/or memory. Experience with computational
modeling and/or EEG is desirable. Proficiency in programming is
candidates should send a CV, a brief statement of research interests, the
expected date of availability, and the names and contact info of at least two
references to firstname.lastname@example.org.
Salary is based on
research experience. UCSD is an equal
opportunity employer and all qualified applicants will receive consideration
for employment without regard to race, religion, color, national origin, sex,
sexual orientation, gender identity, age, status as a protected veteran, or
status as a qualified individual with a disability.
[visionlist] 3 Fully Funded PhD Positions at the Cognitive Robotics and Interaction Lab of the Robotics, Brain and Cognitive Sciences Department at the Italian Institute of TechnologyPosted: April 27, 2016
Apologies for cross-posting
PhD Openings at the Cognitive Robotics and Interaction Lab Robotics, Brain
and Cognitive Sciences Department Italian Institute of Technology
In the spirit of the doctoral School on Bioengineering and Robotics the PhD
Program for the curriculum Cognitive Robotics, Interaction and
Rehabilitation Technologies offers interdisciplinary training at the
interface between technology and life-sciences. The objective of the PhD
program is to form scientists and research technologists capable of working
in multidisciplinary teams on projects where human factors play a crucial
role in technological development and design. Robotics and neuroscience
researchers in RBCS share, as a fundamental scientific objective, the study
of physical and social interaction in humans and machines (www.iit.it/rbcs
Among the different research themes proposed I would like to advertise these
VISUO-HAPTIC EXPLORATION STRATEGIES FOR OBJECT RECOGNITION
MAKE HUMANOIDS UNDERSTAND HUMAN ACTIONS
MULTISENSORY HUMAN-ROBOT INTERACTION
The ideal candidates are students with a higher level university degree
willing to invest extra time and effort in blending into a multidisciplinary
team composed of neuroscientists, engineers, psychologists, physicists
working together to investigate brain functions and realize intelligent
machines, rehabilitation protocols and advanced prosthesis.
The successful candidate will also have the opportunity to spend part of
his/her PhD at the Osaka University and the University of Tokyo in the
framework of the Marie Curie IRSES project CODEFROR (www.codefror.eu) , with
the purpose of integrating his/her knowledge with the different expertise
available at these institutes.
International applications are encouraged and will receive logistic support
with visa issues, relocation, etc.
Below you can find more details related to the position and the instructions
on how to apply.
Application deadline: *****10 June 2016, Noon, Italian time*****
CALL FOR PAPERS
ICML 2016 Anomaly Detection Workshop
Date: June 24th, 2016
Location: New York, USA
Submission deadline: May 1st, 2016.
Acceptance decisions: May 10th, 2016.
Anomaly, outlier and novelty detection methods are crucial tools in any data scientist’s inventory and are critical components of many real-world applications. Abnormal user activities can be used to detect credit card fraud, network intrusions or other security breaches. In computational biology, characterization of systematic anomalies in gene expression can be translated into clinically relevant information. With the rise of Internet-of-Things, the task of monitoring and diagnostics of numerous autonomous systems becomes intractable for a human and needs to be outsources to a machine. Early detection of an upcoming earthquake or tsunami can potentially save human lives. These applications make anomaly detection methods increasingly relevant in the modern world.
However, with the advent of Big Data, new challenges and questions are introduced, which will need to be addressed by the next generation of the anomaly and outlier detection algorithms. The goal of our workshop is to survey the existing techniques and discuss new research directions in this area:
1. How can one detect anomalies in streaming data?
2. How to address non-stationarity in data when the data distribution (and anomalies) is changing with time?
3. How to detect anomalies as early as possible?
4. How to perform “structured anomaly detection”? I.e., how to detect anomalies that are sequences, trees, graphs, or in general, data that violates the IID assumptions?
5. Can weakly labeled or partially labeled data help? If so, then how do we take advantage of this labeled data?
6. How can we deal with huge data sizes?
7. Can we learn meaningful data representations for anomaly detection?
8. How can we accurately evaluate performance in settings with strongly unbalanced datasets or positive (and unlabeled) examples only?
9. Can we perform “significant anomaly detection” (e.g., using p-values)?
10. What is an “anomaly”? How does it compare to outliers, novelties and the like and can they be tackled with the same methodologies?
11. How can we explain decisions of anomaly detectors to guide human experts?
We solicit submission of research papers in the area of anomaly, outlier and novelty detection with the focus on the topics outlined above. Relevant papers that have been recently published or presented elsewhere are allowed, provided that previous publications are explicitly acknowledged. Submission must adhere to ICML 2016 style format and max. 4 pages long, including figures (+additional fifth page containing cited references, supplementary material can be provided). As the review process is not blind authors may reveal their identity during submission process. Please submit your manuscripts to email@example.com.
All accepted papers will have a poster presentation and the best papers will be selected for an oral presentation.
Leman Akoglu, Stony Brook University
Thomas Dietterich, Oregon State University
Klaus-Robert Mueller, Berlin Institute of Technology
Clayton Scott, University of Michigan
Bernhard Schoelkopf*, Max Planck Institute for Intelligent Systems
Nico Goernitz (Berlin Institute of Technology)
Marius Kloft (Humboldt University of Berlin)
Vitaly Kuznetsov (Courant Institute)