Workshop Abstracts

VideoRec'07 workshop will be held as a Special Session of the CRV'07 conference
on the second day of the conference on Tuesday 13:00 - 18:30 

Oral presentations
Tuesday 15:30 - 18:30 (Maisonneuve - 36th Floor)

1. Detecting, Tracking and Classifying Animals in Underwater Observatory Video
   Duane R. Edgington, Danelle E. Cline, Jerome Mariette, Ishbel Kerkez
   
   Abstract: For oceanographic research, remotely operated underwater vehicles (ROVs) and underwater observatories routinely record several hours of video material every day. Manual processing of such large amounts of video has become a major bottleneck for scientific research based on this data. We have developed an automated system that detects, tracks, and classifies objects that are of potential interest for human video annotators. By pre-selecting salient targets for track initiation using a selective attention algorithm, we reduce the complexity of multi-target tracking. Then, if an object is tracked for several frames, a visual event is created and passed to a Bayesian classifier utilizing a Gaussian mixture model to determine the object class of the detected event.
   
   [Paper] [Poster] 
   
   

2. GET-based map icon Identification for Interaction with Map and Kiosks
   Huiqiong Chen, Derek Reilly
   
   Abstract: This paper presents a GET (Generic Edge Token) based approach of detecting and recognizing objects by their shapes, and applies it to improve our ongoing work that considers ways of interacting with paper maps using a handheld. In our work, the GET-based technique aims to help user better locate points of interest on map by recognizing these icons from images/videos captured by handheld camera. In this method, video/image content can be described using a set of perceptual shape features called GETs. Perceptible object can be extracted from a GET map, and then be compared against pre-defined icon models based on GET shape features in recognition. This method provides a simple, efficient way to locate points of interest on the map, determining handheld location, orientation when combined with RFID (senor-based) technique. The tests show that the GET-based object identification can be executed in reasonable time for the real-time interaction system. Meanwhile, the detections and recognitions are robust under different lighting conditions, camera focus, camera rotation, and distance from the map.
   
   [Paper] [Poster] 
   
   

3. Face Recognition in Video Using Modular ARTMAP Neural Networks 
   M. Barry and E. Granger
   
   Abstract: In video-based of face recognition applications, the What-and-Where Fusion Neural Network (WWFNN) has been shown to reduce the generalization error by accumulating a classifier’s predictions over time, according to each individual in the environment. In this paper, three ARTMAP variants – fuzzy ARTMAP, ART-EMAP (Stage 1) and ARTMAP-IC – are compared for the classification of faces detected in the WWFNN. ART-EMAP (stage 1) and ARTMAP-IC expand on the well-known fuzzy ARTMAP by using distributed activation of category neurons, and by biasing distributed predictions according to the number of time these neurons are activated by training set patterns. The average performance of the WWFNNs with each ARTMAP network is compared to the WWFNN with a reference k-NN classifier in terms of generalization error, convergence time and compression, using a data set of real-world video sequences. Simulations results indicate that when ARTMAP-IC is used inside the WWFNN, it can achieve a generalization error that is significantly higher (about 20% on average) than if fuzzy ARTMAP or ARTEMAP is used. Indeed, ARTMAP-IC is less discriminant than the two other ARTMAP networks in cases with complex decision bounderies, when the training data is limited and unbalanced, as found in complex video data. However, ARTMAP-IC can outperform the others when classes are designed with a larger number of training patterns.
   
   [Paper] [Poster]
   
   

4. A Simple Inter- and Intrascale Statistical Model for Video Denoising in 3-D Complex Wavelet Domain Using a Local Laplace Distribution
   Hossein Rabbani, Mansur Vafadust, Saeed Gazor
   
   Abstract: This paper presents a new video denoising algorithm based on the modeling of wavelet coefficients in each subband with a Laplacian probability density function (pdf) with local variance. Since Laplacian pdf is
   leptokurtic, it is able to model the sparsity of wavelet coefficients. We estimate the local variance of this pdf
   employing adjacent coefficients at same scale and parent scale. This local variance models interscale dependency between adjacent scales and intrascale dependency between spatial adjacent. Within this framework, we design a maximum a posteriori (MAP) estimator for video denoising, which relies on the proposed local pdf. Because separate 3-D transforms, such as ordinary 3-D wavelet transforms, have artifacts that degrade the performance of this transform, we implement our algorithm in 3-D complex wavelet transform. This nonseparable and oriented transform gives a motion-based multiscale decomposition for video that isolates in its subbands motion along different directions. In addition, we use our denoising algorithm in 2-D complex wavelet transform, where the 2-D transform is applied to each frame individually. Despite the simplicity of our method in implementation, it achieves better performance than several denoising methods both visually and in terms of peak signal-to-noise ratio (PSNR).
   
   [Paper] [Video1, Video2]
   
   

5. Automatic extraction of semantic object in image using local brightness variances
   Chee Sun Won
   
   Abstract: This paper deals with the problem of segmenting semantic object in an image. Fully automatic solution of this problem is not possible, but human intervention is needed for outlining the rough boundary of the semantic object to be segmented. Our goal is to make the object extraction automatic after the first semiautomatic segmentation. To achieve our goal, we manipulate the contrast of the object and the background such that any contrast-based object segmentation method can extract the object automatically. 
   
   [Paper] 
   
   

6. Zoom on the evidence with ACE Surveillance (+demo)
   Dmitry O. Gorodnichy, Mohammad A. Ali, Elan Dubrofsky, Kris Woodbeck
   
   Abstract: Despite the population's growing awareness of the need to use surveillance systems for better security in private and business settings, such systems still have not become commonplace. The main reason for this is the amount of time and resources an average user has to dedicate in order to collect video data and then to dig through it searching for a evidence when using traditional DVR-based surveillance systems. Here we present ACE-Surveillance - an automated surveillance technology based on real-time Annotation of Critical Evidence that provides an efficient and low-cost solution to the problem. We describe the main features of this technology as related to its two components: ACE-Capture and ACE-Browser. The first component deals with detection and archival of annotated evidence, which is normally performed on a client's desktop computer. The latter deals with browsing and displaying archived video evidence and can be performed either locally on client's computer or remotely via a dedicated server. A new Zoom-on-the-Evidence browsing technique featured by the ACE Surveillance is introduced.  Demonstrations of running the technology on several real-life long-term monitoring assignments, including its deployment by the NRC commissionaires,  are shown.
   
   [Paper] [Poster] [Link]
   
   

7. Working with computer hands-free using Nouse Perceptual Vision Interface (+demo)
   Dmitry O. Gorodnichy, Elan Dubrofsky, Mohammad A. Ali
   
   Abstract: Normal work with a computer implies being able to perform the following three computer control tasks: 1) pointing , 2) clicking, and 3) typing. Many attempts have been made to make it possible to perform these tasks hands-free using a video image of the user as input. Nevertheless, as reported by assistive technology practitioners, no real vision-based hands-free computer control solution that can be used by disabled users has been produced as of yet. Here we present the Nouse Perceptual Vision Interface (Nouse-PVI) that is hoped to finally offer such as a solution. Evolved from the original Nouse "Nose as Mouse" concept and currently under testing with SCO Health Service, Nouse-PVI has several unique features that make it preferable to other hands-free computer input alternatives. These include i) using the nose tip, which has been confirmed to be very convenient for disabled users, for whom the nose literally becomes a new "finger" that can be used to control the computer; ii) a feedback-providing mechanism called Perceptual Cursor (Nousor) that creates an invisible link between the computer and user and which is very important for control as it allows the user to adjust his/her head motion so that the computer can better interpret it, and iii) a number of design solutions specifically tailored for vision-based data entry using small range head motion, such as motion-based code entry (NouseCode and NouseType), a motion-based virtual keyboard (NouseBoard and NousePad) and a word-by-word letter drawing tool (NouseChalk).  While demonstrating these innovative tools, we also address the issue of the user's ability and readiness to work with a computer in the brand new way, i.e. hands-free. The problem is that a user has to understand that it is not entirely the responsibility of the computer to understand what one wants, but it's also the responsibility of the user. - Just as a conventional computer user cannot move the cursor on the screen without first putting the hand on the mouse, so a perceptual interface user cannot work with a computer until s/he "connects" to it. That is, the computer and user must work as a team for the best control results to be achieved. This presentation is therefore designed to serve both as a guide to those developing vision-based input devices and as a tutorial for those who will be using them.
   
   [Paper] [Poster] [Link]