Thesis Details
AdaBoost v počítačovém vidění
In this thesis, we present the local rank differences (LRD). These novel image features are invariant to lighting changes and are suitable for object detection in programmable hardware, such as FPGA. The performance of AdaBoost classifiers with the LRD was tested on a face detection dataset with results which are similar to the Haar-like features which are the state of the art in real-time object detection. These results together with the fact that the LRD are evaluated much faster in FPGA then the Haar-like features are very encouraging and suggest that the LRD may be a solution for future hardware object detectors. We also present a framework for experiments with boosting methods in computer vision. This framework is very flexible and, at the same time, offers high learning performance and a possibility for future parallelization. The framework is available as open source software and we hope that it will simplify work for other researchers.
Boosting, AdaBoost, Local Rank Differences, LRD, Computer Vision, Face Detection, Haar-like Features, WaldBoost
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT), člen
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Křena Bohuslav, Ing., Ph.D. (DITS FIT BUT), člen
Šlapal Josef, prof. RNDr., CSc. (DADM FME BUT), člen
@mastersthesis{FITMT4987,
author = "Michal Hradi\v{s}",
type = "Master's thesis",
title = "AdaBoost v po\v{c}\'{i}ta\v{c}ov\'{e}m vid\v{e}n\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2007,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/4987/"
}