Thesis Details
Rozpoznávání pozic a gest
This thesis inquires the existing methods on the field of image recognition with regards to gesture recognition. Some methods have been chosen for deeper study and these are to be discussed later on. The second part goes in for the concenpt of an algorithm that would be able of robust gesture recognition based on data acquired within the AMI and M4 projects. A new ways to achieve precise information on participants position are suggested along with dynamic data processing approaches toward recognition. As an alternative, recognition using Gaussian Mixture Models and periodicity analysis are brought in. The gesture class in focus are speech supporting gestures. The last part demonstrates the results and discusses future work.
color model, segmentation, Gauss Function, normal distribution, convolution, filtering, exponencial filter, tracking, Gaussian Mixture Model, GMM, recognition, classification
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT), člen
Fučík Otto, doc. Dr. Ing. (DCSY FIT BUT), člen
Sojka Eduard, doc. Dr. Ing. (VŠB-TUO), člen
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT), člen
@mastersthesis{FITMT7225,
author = "Leo\v{s} Ji\v{r}\'{i}k",
type = "Master's thesis",
title = "Rozpozn\'{a}v\'{a}n\'{i} pozic a gest",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2008,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/7225/"
}