Dynamic Graphics Project
40 St. George Street
Canada M5S 2E4
[last name without apostrophe]@dgp.toronto.edu
Phone: +1 416 946 8495
Fax: +1 416 978 4765
4th International Symposium on Non-Photorealistic Animation and Rendering (NPAR 2006), Jun. 5 - Jun. 7, 2006. Annecy, France.
Peter O'Donovan and David Mould
Felt is mankind's oldest and simplest textile, composed of a pressed mass of fibers. Images can be formed directly in the fabric by arranging the fibers to represent the image before pressure is applied, a process called "felt painting". Here, we describe an automated synthesis method that transforms input images into felt-painted images.
Paper Animated Felt Test
Using Semantic Web Methods for
Distributed Learner Modeling
2nd International Workshop on Applications of Semantic Web Technologies for E-Learning (SW-EL 04) held in conjunction with the International Semantic Web Conference (ISWC 2004), Nov. 7 - Nov. 11, 2004. Hiroshima, Japan
Mike Winter, Chris Brooks, Gord McCalla, Jim Greer, Peter O'Donovan
Here describe a semantic web approach
for representing student models based on
distributed student data from learning environments
where the learner uses multiple applications and resources
to accomplish learning tasks. We also present a proposal
for revising those student models based on
arbitrary, web-based learner actions.
Learning View-based Mixture of Experts for Human Action Recognition
CSC2539 (Topics in Computer Vision: Visual Motion Analysis)
Many methods for action recognition use a view-independent approach
where actions from different views are treated identically. However, this results in
models which must deal with significantly different motions from different views
such as classifying a boxer from a rear view versus a side view. In this paper,
I explore the use of a view-based Mixture of Experts (MoE) model where each
expert is trained on data from a relative view between the camera and the subject.
This allows the experts to model a particular view and results in improved classification rates.
Seperate view and action classifiers were trained using both SVMs and LD-CRF models and the results
compared on the HumanEVA dataset.
Static Gesture Recognition with Restricted
CSC2515 (Introduction to Machine Learning)
In this paper I investigate a new technique for the recognition of static gestures
(poses) from laptop camera images. I apply Restricted Boltzmann Machines
(RBMs) to model the manifold of 3 human gestures: pointing, thumbs up, fingers
spread, as well as the default no-gesture case. The generative RBM model
performs significantly better than other classification techniques including classical
discriminative neural networks, and k-Nearest Neighbors on dimensionality
Paper Gesture Examples
Optical Flow: Techniques and Applications
Using Optical Flow for Stabilizing Image Sequences
CMPT400 (Honours Thesis Course)
This thesis was partly a survey of the optical flow literature and partly a project implementing stablization of shaky video sources. Stabilization was accomplished with a simple region segmentation and classification step to determine the background of the sequence. The movement of the background was then filtered with a Kalman filter and translated to stabilize the video.
Paper Video 1 Video 2 Video 3