Region-based fractal image compression using heuristic search.

Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB.

Adaptive classifier integration for robust pattern recognition.

The integration of multiple classifiers promises higher classification accuracy and robustness than can be obtained with a single classifier. This paper proposes a new adaptive technique for classifier integration based on a linear combination model. The proposed technique is shown to exhibit robustness to a mismatch between test and training conditions. It often outperforms the most accurate of the fused information sources. A comparison between adaptive linear combination and non-adaptive Bayesian fusion shows that, under mismatched test and training conditions, the former is superior to the latter in terms of identification accuracy and insensitivity to information source distortion.

Evaluation of vision-based head-trackers for assistive devices.

This paper presents a new evaluation methodology for assistive devices employing head-tracking systems based on an adaptation of the Fitts Test. This methodology is used to compare the effectiveness and performance of a new vision-based head tracking system using face, skin and motion detection techniques with two existing head tracking devices and a standard mouse. The application context and the abilities of the user are combined with the results from the modified Fitts Test to help determine the most appropriate devices for the user. The results suggest that this modified form of the Fitts test can be effectively employed for the comparison of different access technologies.