National Repository of Grey Literature 6 records found  Search took 0.00 seconds. 
Face recognition
Škrobák, Dalibor ; Číka, Petr (referee) ; Kyselý, František (advisor)
This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.
The simulation of biometric protection systems working on the face recognition principle
Dubský, Milan ; Rampl, Ivan (referee) ; Atassi, Hicham (advisor)
The aim of this work is to realize a system in the Matlab-Simulink environment, which will be able to detect and recognize the human face from the input image. The created model will actually simulate the biometric security systems working on the principle of face recognition. The work is divided into two parts. In the first part, several methods for face detection from image are described. We focused on the symptomatic oriented and color segmentation methods. The pattern matching method is also described and implemented; the advantage ofthe pattern matching that it can be used either for face detection or face recognition. The second part of this work contains a description of the face recognition. Where PCA (Principal Component Analysis) are used for this task, this part of the work also includes experimental results of tests performed on our methods.
Static model of scene
Sikora, Jan ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
This thesis deal with various methods of background detection and with it related motion detection in a scene. It's progressing from simplest methods to more comlex. For every one are reviewed the possibilities of using and her drawbacks. In introduction are described various types of scenes according to background and foreground type e.g . according to movement objects speed or presence of movement in background. Is proposed several common or specific improvements for obtaining better background even by using simple method. Next part of work solve real situation of shaking camera. There are tested two basic methods for optical stabilization. The first is registration of images by template matching. Alternative method used interest points (corners). Both methods are closely examinate and is sought best way to match following pictures. Except shaking of camera this work deal with rotating camera and in theory solve detection background from cameras placed on ridden car. Part of work is creation database of different types scenes
Static model of scene
Sikora, Jan ; Petyovský, Petr (referee) ; Richter, Miloslav (advisor)
This thesis deal with various methods of background detection and with it related motion detection in a scene. It's progressing from simplest methods to more comlex. For every one are reviewed the possibilities of using and her drawbacks. In introduction are described various types of scenes according to background and foreground type e.g . according to movement objects speed or presence of movement in background. Is proposed several common or specific improvements for obtaining better background even by using simple method. Next part of work solve real situation of shaking camera. There are tested two basic methods for optical stabilization. The first is registration of images by template matching. Alternative method used interest points (corners). Both methods are closely examinate and is sought best way to match following pictures. Except shaking of camera this work deal with rotating camera and in theory solve detection background from cameras placed on ridden car. Part of work is creation database of different types scenes
The simulation of biometric protection systems working on the face recognition principle
Dubský, Milan ; Rampl, Ivan (referee) ; Atassi, Hicham (advisor)
The aim of this work is to realize a system in the Matlab-Simulink environment, which will be able to detect and recognize the human face from the input image. The created model will actually simulate the biometric security systems working on the principle of face recognition. The work is divided into two parts. In the first part, several methods for face detection from image are described. We focused on the symptomatic oriented and color segmentation methods. The pattern matching method is also described and implemented; the advantage ofthe pattern matching that it can be used either for face detection or face recognition. The second part of this work contains a description of the face recognition. Where PCA (Principal Component Analysis) are used for this task, this part of the work also includes experimental results of tests performed on our methods.
Face recognition
Škrobák, Dalibor ; Číka, Petr (referee) ; Kyselý, František (advisor)
This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.

Interested in being notified about new results for this query?
Subscribe to the RSS feed.