| Abstract [eng] |
This paper represents an embryo cleavage-stage classification algorithm. There are used statistical feature extraction methods and two classification methods: Classification with training and classification without training. The main problem of this work is detection of early embryo cleavage stages. The aim is to adapt the proper classification method. The first part of this paper represents the analysis of the literature, and the methods used by other researchers examining similar issues. The second part of this research represents the proposed algorithm. There are introduced proposed methods. For the feature extraction proposed statistical methods: entropy, invariant moments and principal components analyses. For the classification are used neural networks and K-nearest neighbor method. The proposed method is checked by experiment. It is expected that this method will work well in video sequences. The Master's thesis consists of an introduction, three chapters, references and author of publications on the topic of the Master. General master's thesis consists of 70 pages, numbered 26 formulas, 103 pictures and 8 tables. References list includes sources. |
