12th Conference on Computer and Robot Vision

Halifax, Nova Scotia.   June 3-5, 2015.

Welcome to the home page for CRV 2015 which will be held at Dalhousie and at Saint Mary's Universities.

CRV is an annual conference hosted in Canada, and co-located with Graphics Interface (GI) and Artificial Intelligence (AI). A single registration covers attendance in all three conferences. Please see the AI/GI/CRV general conference website for more information.

The CRV proceedings are published through the Conference Publishing Services (CPS). Accepted papers will be submitted to Xplore. Xplore has published CRV accepted papers since 2004. See archive for links to previous years' papers.


  • We will accept late submissions up to Friday March 6, 9 AM EST. Review assignments will be finalized by end of that day.

  • The online submission site for CRV 2015 is now open. Submission instructions are found at the "Submissions" tab.

  • Call For Papers (PDF)

  • We are excited to announce the Keynote speakers for 2015: Kostas Daniilidis (U. Penn) and Demetri Terzopoulos (UCLA).

Important Dates:

Paper submission March 3, 2015 (*extended to Fri. March 6, 9 AM EST*)
Acceptance/rejection notification March 27, 2015
Revised camera-ready papers April 20, 2015
Early registration April 8, 2015 (Registration Website)
Conference June 3-5, 2015

Conference History

In 2004, the 17th Vision Interface conference was renamed the 1st Canadian Conference on Computer and Robot Vision. In 2011, the name was shortened to Conference on Computer and Robot Vision.

CRV is sponsored by the Canadian Image Processing and Pattern Recognition Society (CIPPRS).


CRV 2015 Program

This page will contain detailed CRV program information. The higher-level view of the joint conference program, which also includes the AI and GI meetings will be found here.





Paper Submission Instructions

Please refer to the Call For Papers for information on the goals and scope of CRV.

The online submission site for CRV 2015 is now open. The paper submission deadline is 3 March 2015, 11:59 PM PST. Please note that this is a firm a deadline.

The CRV review process is single-blind: authors are not required to anonymize submissions. Submissions must be between 4 to 8 pages (two-column) long. Submissions less than 6 pages will most likely be considered for poster sessions only. Use the following templates to prepare your CRV submissions:

Please direct any questions regarding the paper sumbmission process to the conference co-chairs by emailing computerrobotvision2015 "at" gmail.com

CRV 2015 Co-Chairs

  • Michael Langer, McGill University
  • Faisal Qureshi, University of Ontario Institute of Technology (UOIT)

CRV 2015 Program Committee

  • Mohand Said Allili, Université du Québec en Outaoauis, Canada
  • Robert Allison, York University, Canada
  • Alexander Andreopoulos, IBM Research, Canada
  • John Barron, University of Western Ontario, Canada
  • Steven Beacuchemin, University of Western Ontario, Canada
  • Robert Bergevin, Université Laval, Canada
  • Guillaume-Alexandre Bilodeau, École Polytechnique Montréal, Canada
  • Pierre Boulanger, University of Alberta, Canada
  • Jeffrey Boyd, University of Calgary, Canada
  • Marcus Brubaker, University of Toronto, Canada
  • Neil Bruce, University of Manitoba, Canada
  • Gustavo Carneiro, University of Adelaide, Australia
  • James Clark, McGill University, Canada
  • David Clausi, University of Waterloo, Canada
  • Dana Cobzas, University of Alberta, Canada
  • Jack Collier, DRDC Suffield, Canada
  • Kosta Derpanis, Ryerson University, Canada
  • Gregory Dudek, McGill University, Canada
  • James Elder, York University, Canada
  • Mark Eramian, University of Saskatchewan, Canada
  • Frank Ferrie, University of McGill, Canada
  • Alexander Ferworn, Ryerson University, Canada
  • Paul Fieguth, Waterloo, Canada
  • Brian Funt, Simon Fraser University, Canada
  • Philippe Giguère, Laval University, Canada
  • Yogesh Girdhard, Woods Hole Oceanographic Institute, USA
  • Minglun Gong, Memorial University of Newfoundland, Canada
  • Michael Greenspan, Queens University, Canada
  • Kamal Gupta, Simon Fraser University, Canada
  • Wolfgang Heidrich, University of British Columbia
  • Jesse Hoey, University of waterloo, Canada
  • Andrew Hogue, University of Ontario Institute of Technology, Canada
  • Jessy Hoey, University of Waterloo, Canada
  • Andrew Hogue, University of Ontario Institute of Technology, Canada
  • Randy Hoover, South Dakota School of Mines and Technology, USA
  • Martin Jagersand, University of Alberta, Canada
  • Michael Jenkin, York University, Canada
  • Allan Jepson, University of Toronto, Canada
  • Hao Jiang, Boston College, USA
  • Pierre-Marc Jodoin, Université de Sherbrooke, Canada
  • Jonathan Kelly, University of Toronto, Canada
  • Dana Kulic, University of Waterloo, Canada
  • Robert Laganière, University of Ottawa, Canada
  • Jean-Francois Lalonde, Laval University, Canada
  • Jochen Lang, University of Ottawa, Canada
  • Cathy Laporte, ETS Montreal, Canada
  • Denis Laurendeau, Laval University, Canada
  • Howard Li, University of New Brunswick, Canada
  • Jim Little, University of British Columbia, Canada
  • Shahzad Malik, University of Toronto, Canada
  • Scott McCloskey, Honeywell Labs, USA
  • David Meger, McGill University, Canada
  • Jean Meunier, Universite de Montreal, Canada
  • Max Mignotte, Universite de Montreal, Canada
  • Gregor Miller, University of British Columbia, Canada
  • Greg Mori, Simon Fraser University, Canada
  • Christopher Pal, École Polytechnique Montréal, Canada
  • Pierre Payeur, University of Ottawa, Canada
  • Cédric Pradalier, Georgia Tech. Lorraine, France
  • Yiannis Rekleitis, University of South Carolina, USA
  • Junaed Sattar, Clarkson University, USA
  • Christian Scharfenberger, University of Waterloo, Canada
  • Angela Schoellig, University of Toronto, Canada
  • Kaleem Siddiqi, McGill University, Canada
  • Gunho Sohn, York University, Canada
  • Minas Spetsakis, York University, Canada
  • Uwe Stilla, Technische Universitaet Muenchen, Germany
  • Graham Taylor, University of Guelph, Canada
  • Lan Tian, Stanford University, USA
  • Chi Hay Tong, University of Oxford, United Kingdom
  • John Tsotsos, York University, Canada
  • Olga Veksler, University of Western Ontario, Canada
  • Ruisheng Wang, University of Calgary, Canada
  • Yang Wang, University of Manitoba, Canada
  • Steven Waslander, Waterloo University, Canada
  • Alexander Wong, Waterloo University, Canada
  • Robert Woodham, University of British Columbia
  • Yijun Xiao, University of Edinburgh United Kingdom
  • Herb Yang, University of Alberta, Canada
  • Alper Yilmaz, Ohio State University, USA
  • John Zelek, University of Waterloo Ontario, Canada
  • Hong Zhang, University of Alberta, Canada

CIPPRS Executive

  • President: Gregory Dudek, McGill University
  • Treasurer: John Barron, Western University
  • Secretary: Jim Little, University of British Columbia

Keynote Speakers

We will have two Keynote speakers in 2015: Kostas Daniilidis (U. Penn) on June 3, and Demetri Terzopoulos (UCLA) on June 5. More information including Title, Abstract, and Bio will be announced later in the Fall.





Invited Symposia

CRV 2015 will feature 8 exciting symposia on subtopics related to computer and robot vision.

Autonomous Robots

  • Tim Barfoot, Univ. of Toronto

    "Long-Term Visual Route Following for Mobile Robots"

  • Howard Li, Univ. of New Brunswick

    "Perception, Navigation and Target Localization for Autonomous UAVs"

    Unmanned Aerial Vehicles (UAVs) and robots usually are related to situations involving hazardous environments, repetitive and menial tasks. UAVs can be used in many areas, such as surveillance, forestry management, mine hunting, automatic inspection of power plants and refineries, and disposal of hazardous materials. In this talk, we will present our current research in UAVs and robotics. We will present the sensing, perception, navigation, and localization methods. Simultaneous Localization and Mapping algorithms will be introduced. Results of our current research in robotics and unmanned vehicles will be presented.

Human-Robot Interaction and Assistive Tech

  • Dana Kulic, Univ. of Waterloo

    "Human Motion Analysis for Rehabilitation"

  • Babak Taati, Toronto Rehabilitation Institute

    "Computer vision systems in dementia care"

Vision and Learning

  • Graham Taylor, Univ. of Guelph

    "Learning Representations with Multiplicative Interactions"

    Representation learning algorithms are machine learning algorithms which involve the learning of features or explanatory factors. Deep learning techniques, which employ several layers of representation learning, have achieved much recent success in machine learning benchmarks and competitions, however, most of these successes have been achieved with purely supervised learning methods and have relied on large amounts of labeled data. In this talk, I will discuss a lesser-known but important class of representation learning algorithms that are capable of learning higher-order features from data. The main idea is to learn relations between pixel intensities rather than the pixel intensities themselves by structuring the model as a tri-partite graph which connects hidden units to pairs of images. If the images are different, the hidden units learn how the images transform. If the images are the same, the hidden units encode within-image pixel covariances. Learning such higher-order features can yield improved results on recognition and generative tasks. I will discuss recent work on applying these methods to structured prediction problems.

  • Yang Wang, Univ. of Manitoba

    "Recognizing and Localizing Novel Objects"

    A lot of progress has been made in object recognition in the last few years. Now we have reasonably accurate systems that can recognize thousands of object categories. We also have good object detectors for a handful of object classes. However, since the number of object is so big and new object classes might emerge over time, it is not clear whether the standard supervised learning approach is the final solution for object recognition. In this talk, I will discuss our recent work on transfer learning for recognizing and localizing objects for which we do not have training data.

Object Detection

  • Sven Dickinson, Univ. of Toronto

    "Detecting Symmetric Parts in Cluttered Scenes"

    Perceptual grouping played a prominent role in support of early object recognition systems, which typically took an input image and a database of shape models and identified which of the models was visible in the image. Using intermediate-level shape priors, causally related shape features were grouped into discriminative shape indices that were used to prune the database down to a few promising candidates that might account for the query. In recent years, however, the recognition (categorization) community has focused on the object detection problem, in which the input image is searched for a specific target object. Since indexing is not required to select the target model, perceptual grouping is not required to construct a discriminative shape index. As a result, perceptual grouping activity at our major conferences has diminished. However, there are clear signs that the recognition community is moving from appearance back to shape, and from detection back to multi-class object categorization. Shape-based perceptual grouping will play a critical role in facilitating this transition. One of the most powerful mid-level shape priors is symmetry, which forms the basis for many approaches to part-based object modeling and recognition. In this talk, I will review our recent progress on detecting symmetric parts in cluttered scenes.

  • Sanja Fidler, Univ. of Toronto

    "Understanding Complex Scenes and People That Talk about Them"

    Language is an important link between high level semantic concepts and more low level visual perception. A successful robotic platform needs to both, understand the visual world and the lingual instructions given by the human user in order to react appropriately. In this talk, I'll present our recent work on 3D understanding of indoor scenes, and show how natural sentential descriptions can be exploited to improve 3D visual parsing.

Intelligent Vehicles

Vision for Graphics

  • Jean-Francois Lalonde, Laval Univ.

    "Understanding outdoor lighting in vision and graphics"

    Outdoor illumination creates challenges for computer vision and graphics alike. In vision, algorithms routinely get confused by strong shadows, highlights, and glare. In graphics, simulating the extreme dynamic range of outdoor lighting needs to be done accurately to realistically synthesize these effects. In this talk, I will present approaches that aim to improve our understanding of natural lighting with applications in both vision and graphics. First, I will briefly present approaches that rely on a physically-based illumination model to infer scene and illumination properties from time-lapse sequences and single images, by explicitly reasoning about the illumination conditions. Second, I will present recent work that relies on a data-driven model, trained on a novel dataset of 8,000+ HDR photographs of daytime skies. We leverage this new dataset to 1) automatically estimate the illumination conditions in image collections, which allows us to seamlessly insert virtual objects in the images, and 2) characterize the behavior of photometric stereo under natural lighting.

  • Minglun Gong, Memorial Univ.

    "Modeling and analyzing 3D shapes using clues from 2D images"

Novel Imaging Techniques

  • Marcus Brubaker, Univ. of Toronto

    "Efficient 3D Molecular Structure Estimation with Electron Cryomicroscopy"

    Discovering the 3D structure of molecules such as proteins and viruses is a fundamental research problem in biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D structures from 2D images. This talk reviews the computational problems in Cryo-EM which are closely related to classical vision problems such as object detection, multiview reconstruction and computed tomography. Finally, a framework is introduced for reconstruction of 3D molecular structure which exploits modern methods for stochastic optimization and importance sampling. The result is a method which is efficient, robust to initialization and flexible.

  • Sebastien Roy, Univ. de Montreal

    "The Omnipolar Camera"


  • Ruisheng Wang, Univ. of Calgary

    "Scene Parsing Using Graph Matching on Street View Data"

  • Gunho Sohn, York Univ.

    Title TBA

Links to Previous Conferences

This page archives aa historical content from past CRV meetings. A second source for some of this information is maintained at the CIPPRS website.