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Image Descriptors and their Usage for Object Detection
 Bature, Jonathan ; Druckmüllerová, Hana (oponent) ; Procházková, Jana (vedoucí práce)
Face detection in images is a widely explored topic in computer vision. The algorithm that enabled face recognition and set new standards in this area was the Viola-Jones algorithm. This thesis work describes and explain the actual implementation of a face detector based on the Viola Jones algorithm using the Matlab CascadeObjectDetector. This thesis contributes to the exploration of techniques such as principal component analysis (PCA) for dimensional reduction of descriptors to establish an object detection system that achieves the best trade-off between performance and speed. In our approach, face detection is performed by Principal Component Analysis (PCA). The facial image is projected into the facial space that encodes the best variation of known facial images. The space of the face is defined by the eigenface. An eigenface is a set of facial eigenvectors that may not correspond to common facial features such as eyes, nose, and lips. The system works by projecting pre-extracted facial images into a series of facial spaces that represent large deviations between known facial images. Faces are classified as known or unknown faces after matching with an existing face image on the database.
Úplný záznam
Image Descriptors and their Usage for Object Detection
 Bature, Jonathan ; Druckmüllerová, Hana (oponent) ; Procházková, Jana (vedoucí práce)
Face detection in images is a widely explored topic in computer vision. The algorithm that enabled face recognition and set new standards in this area was the Viola-Jones algorithm. This thesis work describes and explain the actual implementation of a face detector based on the Viola Jones algorithm using the Matlab CascadeObjectDetector. This thesis contributes to the exploration of techniques such as principal component analysis (PCA) for dimensional reduction of descriptors to establish an object detection system that achieves the best trade-off between performance and speed. In our approach, face detection is performed by Principal Component Analysis (PCA). The facial image is projected into the facial space that encodes the best variation of known facial images. The space of the face is defined by the eigenface. An eigenface is a set of facial eigenvectors that may not correspond to common facial features such as eyes, nose, and lips. The system works by projecting pre-extracted facial images into a series of facial spaces that represent large deviations between known facial images. Faces are classified as known or unknown faces after matching with an existing face image on the database.
Úplný záznam

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