|
|
ASL Fingerspelling Recognition Using Slow Feature Analysis
Winkler, Martin ; Hradiš, Michal (oponent) ; Burget, Lukáš (vedoucí práce)
This work describes the process of testing slow feature analysis as a method of extracting rhobust features from complex image data of american sign language. For purposes of testing a system in python is created that facilitates test runs and offers rich scale of changable specifications to allow the user run various tests in order to determine how viable the method is for classification and recognition of hand shapes. The theoretical part introduces the slow feature analysis, discusses the structure of the system and describes the dataset on which the method is to be observed. In practical part the method was subjected to performance analysis on seen and unseen speakers, its viability with higher number of gestures and some interesting input data formatting in attempt to improve the performance.
|
|
| |
|
| |
|
|
ASL Fingerspelling Recognition Using Slow Feature Analysis
Winkler, Martin ; Hradiš, Michal (oponent) ; Burget, Lukáš (vedoucí práce)
This work describes the process of testing slow feature analysis as a method of extracting rhobust features from complex image data of american sign language. For purposes of testing a system in python is created that facilitates test runs and offers rich scale of changable specifications to allow the user run various tests in order to determine how viable the method is for classification and recognition of hand shapes. The theoretical part introduces the slow feature analysis, discusses the structure of the system and describes the dataset on which the method is to be observed. In practical part the method was subjected to performance analysis on seen and unseen speakers, its viability with higher number of gestures and some interesting input data formatting in attempt to improve the performance.
|
|
| |
|
| |
|
| |
host ::
RSS.