13th Conference on Computer and Robot Vision
Victoria, British Columbia.   June 1-6, 2016.
Welcome to the home page for CRV 2016 which will be held at the University of Victoria, Victoria, British Columbia.
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.
- Under construction. We will keep you posted of new developments through this website.
|Paper submission||March 3, 2015|
|Acceptance/rejection notification||March 27, 2015|
|Revised camera-ready papers||April 10, 2015|
|Early registration||April 20, 2015 (Registration Website)|
|Conference||June 3-5, 2015|
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
The higher-level view of the joint conference program which also includes the AI and GI meetings is available here.
CRV 2016 Co-Chairs
- Faisal Qureshi, University of Ontario Institute of Technology (UOIT)
- Steven L. Waslander, University of Waterloo
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 Beauchemin, 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 Girdhar, 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
- 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
- President: Gregory Dudek, McGill University
- Treasurer: John Barron, Western University
- Secretary: Jim Little, University of British Columbia
CRV 2015 will feature eight exciting symposia on subtopics related to computer and robot vision.
Novel Imaging TechniquesWed. June 3, 9 AM- 10:00 AM
- 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
"Immersive Stereo Capture: the Omnipolar Camera"
With the recent advances in virtual reality headsets, there is a strong interest in reproducing the real environment using stereoscopic immersive (omnistereo) capture. This talk will introduce the problems of combining full immersive capture with stereo depth in the context of human stereo perception. A multi-camera setup, the "omnipolar camera", will also be presented. It relies on the epipolar geometry to reduce parallax artefacts and to simplify image stitching.
LIDARWed. June 3, 2:00-3:00 PM
- Ruisheng Wang, Univ. of Calgary
"Scene Parsing Using Graph Matching on Street View Data"
In this talk, a street scene parsing scheme that takes advantages of images from perspective cameras and range data from LiDAR is presented. First, pre-processing on the image set is performed and the corresponding point cloud is segmented according to semantics and transformed into an image pose. A graph matching approach is introduced into our parsing framework, in order to identify similar sub-regions from training and test images in terms of both local appearance and spatial structure. By using the sub-graphs inherited from training images, as well as the cues obtained from point clouds, this approach can effectively interpret the street scene via a guided MRF inference. We further introduce low-rank regularization into the graph matching and reformulate the low-rank graph matching problem into a standard semidefinite proragmming problem, which is much easier to solve. The matching performance is enhanced and experimental results show a promising performance of our approach.
- Gunho Sohn, York Univ.
"3D Infrastructure Scene Reconstruction Using Laser Point Clouds
LiDAR (Light Detection and Ranging) is an emerging remote sensing technology that directly measures the distance between the sensor and a target surface using the latest time-of-flight technology, thus providing massive and highly accurate three-dimensional point clouds. Over the last decade, LiDAR has been rapidly adopted as a primary sensor in Geomatics community for supporting a wide range of applications such as bathymetry, forestry, mining, ecology, topographic mapping, and engineering. One of primary research interests in Geomatics is to reconstruct “As-Built” infrastructure models, approximating the existing infrastructure conditions modelled with semantically rich primitives. Having such accurate model representation allows us to conduct high-precision risk analysis, inventory update and management. However, like many other vision tasks, automatically generating large-scale “As-Built” models still remains unresolved research problems. Thus, today’s practice used for the infrastructure management heavily relies on human-centric and time consuming process. This presentation will introduce the latest research activities at York University, studying image understanding and model reconstruction of building facades and rooftops, single trees, railways and power lines using LiDAR point clouds.
Autonomous RobotsThurs. June 4, 9:00-10:00 AM
- Tim Barfoot, Univ. of Toronto
"Long-Term Visual Route Following for Mobile Robots"
I will describe a particular approach to visual route following for mobile robots that we have developed, called Visual Teach & Repeat (VT&R), and what I think the next steps are to make this system usable in real-world applications. We can think of VT&R as a simple form of simultaneous localization and mapping (without the loop closures) along with a path-tracking controller; the idea is to pilot a robot manually along a route once and then be able to repeat the route (in its own tracks) autonomously many, many times using only visual feedback. VT&R is useful for such applications as load delivery (mining), sample return (space exploration), and perimeter patrol (security). Despite having demonstrated this technique for over 500 km of driving on several different robots, there are still many challenges we must meet before we can say this technique is ready for real-world applications. These include (i) visual scene changes such as lighting, (ii) physical scene changes such as path obstructions, and (iii) vehicle changes such as tire wear. I’ll discuss our progress to date in addressing these issues and the next steps moving forward.
- 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.
Vision for GraphicsThurs. June 4, 2:30-3:30 PM
- 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"
An image worth a thousand words. From images, we humans are able to infer the 3D shape of an object and to decompose the object into semantically meaningful parts. Now, is it possible to teach computers to do these tasks? Two recent research projects that work along this direction will be presented in this talk. The first one investigates how the 3D modeling of flower head can be facilitated using a single photo of the flower. The core idea is that flower head typically consists of petals of similar 3D geometries, yet their observed shapes on 2D images vary due to differences in projecting directions. Exploiting this variation allows us to reconstruct the 3D geometry of the petals from a single image. The second project studies how to segment 3D models into semantically meaningful parts based on knowledge learned from labeled 2D images. Here the input 3D model is treated as a collection of 2D projections, which are labeled using training images of similar objects. The 3D model is then segmented by summarizing the labeling for its projections. Here the key is, for each query projection, how to retrieve objects with similar semantic parts and transfer their labels over.
Intelligent VehiclesThurs. June 4, 4 PM - 5 PM
- Raquel Urtasun, Univ. of Toronto
"Towards Affordable Self Driving Cars"
Developing autonomous systems that are able to assist humans in everyday's tasks is one of the grand challenges in modern computer science. Notable examples are personal robotics for the elderly and people with disabilities, as well as autonomous driving systems which can help decrease fatalities caused by traffic accidents. In order to perform tasks such as navigation, recognition and manipulation of objects, these systems should be able to efficiently extract 3D knowledge of their environment. In this talk, I'll show how graphical models provide a great mathematical formalism to extract this knowledge. In particular, I'll focus on a few examples, including 3D reconstruction, 3D object and layout estimation and self-localization.
- Steven Beauchemin, Western University
Title: "Vehicular Instrumentation for the Study of Driver Intent and Related Applications"
We describe a vehicular instrumentation for the study of driver intent. Our instrumented vehicle is capable of recording the 3D gaze of the driver and relating it to the frontal depth map obtained with a stereo system in real-time, including the sum of vehicular parameters actuator motion, speed, and other relevant driving parameters. Additionally, we describe other real-time algorithms that are implemented in the vehicle, such as a frontal vehicle recognition system, a free lane space estimation method, and a GPS position-correcting technique using lane recognition as land marks.
Vision and LearningFri. June 5, 9:00 AM- 10:00 AM
- 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 DetectionFri. June 5, 11:00 AM- 12:00 PM
- 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.
Human-Robot Interaction and Assistive TechFri. June 5, 4:00 PM- 5:00 PM
- Dana Kulic, Univ. of Waterloo
"Human Motion Analysis for Rehabilitation"
Mobility improvement for patients is one of the primary concerns of physiotherapy rehabilitation. Providing the physiotherapist and the patient with a quantified and objective measure of progress can be beneficial for monitoring the patient's performance and providing guidance and feedback. In this talk, we describe a system for data collection, on-line pose estimation, segmentation and user interface for patients undergoing lower body rehabilitation. An approach for quantifying patient performance is also introduced. Results from multiple studies evaluating the system with patients undergoing rehabilitation following joint replacement surgery will be presented.
- Babak Taati, Toronto Rehabilitation Institute
"Computer vision systems in dementia care"
Computer vision systems can play a role in providing care to individuals living with dementia. In this talk, I will first briefly review vision-based systems to provide assistance to older adults with dementia and to assist with usability studies for this population. I will then present preliminary results on assessing the cognitive status of older adults by way of monitoring common activities of daily living. Early identification of dementia can potentially lead to improved quality of life both for older adults with dementia and their family and caregivers who can better plan informal/formal care in advance.
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.
- CRV 2015: Halifax, Nova Scotia, Jun 3-5, 2015
- CRV 2014: Montreal, Quebec, May 7-9, 2014
- CRV 2013: Regina, Saskatchewan, May 28-31, 2013
- CRV 2012: Toronto, Ontario, May-28-30, 2012
- CRV 2011: St-John's, Newfoundland and Labrador, May-25-27, 2011
- CRV 2010: Ottawa, Ontario, 31 May-2 June 2010
- CRV 2009: Kelowna, British Columbia, 25-27 May 2009
- CRV 2008: Windsor, Ontario, 28-30 May 2008
- CRV 2007: Montreal, Quebec, 28-30 May 2007
- CRV 2006: Quebec City, Quebec, 7-9 May 2006
- CRV 2005: Victoria, British Columbia, 9-11 May 2005
- CRV 2004: London, Ontario, 17-19 May 2004.
Description : 2009-0605-Victoria-Harbor-PAN
Credit : © Bobak Ha'Eri - Own work. Licensed under CC BY 3.0 via Wikimedia Commons