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Detection, Extraction and Measurement of the Length of the Metacarpal Bones in Images
Ulej, Vojtěch ; Rydlo, Štěpán (referee) ; Drahanský, Martin (advisor)
The aim of this bachelor thesis is to study the literature dealing with the topic of object (specifically bones) recognition in images. Another task is to summarize the existing solutions for detection and measurement of object's length and to design a suitable algorithm for detection and measurement of metacarpal bones in images. First proposed algorithm is based on edge detecting methods. However, this approach has proved unreliable. The second algorithm design is based on machine learning using the convolutional neural network Mask RCNN.
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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
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Detection, Extraction and Measurement of the Length of the Metacarpal Bones in Images
Ulej, Vojtěch ; Rydlo, Štěpán (referee) ; Drahanský, Martin (advisor)
The aim of this bachelor thesis is to study the literature dealing with the topic of object (specifically bones) recognition in images. Another task is to summarize the existing solutions for detection and measurement of object's length and to design a suitable algorithm for detection and measurement of metacarpal bones in images. First proposed algorithm is based on edge detecting methods. However, this approach has proved unreliable. The second algorithm design is based on machine learning using the convolutional neural network Mask RCNN.
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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
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