Face Tracking Using the Dynamic Grey World Algorithm
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
A computer vision based human-robot interface
Autonomous robotic systems
Random projection and orthonormality for lossy image compression
Image and Vision Computing
A simple oriented mean-shift algorithm for tracking
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Principal components analysis has been studied by the computer vision community as a source of features for recognition of faces, objects and scenes [9], [13]. The use of the dominant principal components as "holistic" features for recognition has provided new insights into view invariant and illumination invariant recognition. While this approach remains controversial for recognition because of problems in segmentation and normalization, under certain circumstances, principal components are optimal for reconstruction, and thus well suited for coding and compression of images. In such applications, precise tracking rather than segmentation, is sufficient as a pre-processing step.In this paper we describe experiments with the use of principal components as a technique for coding and compression for video streams of talking heads. We describe a new robust tracking technique for normalizing the position and size of faces. We provide results of preliminary experiments with compression rates and image reconstruction quality using orthogonal basis coding for video communications. We show that a typical video sequence of a talking head can often be coded in less than 16 dimensions.