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Face Detection
Šašinka, Ondřej ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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Detecting Objects in Images
Kubínek, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.
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Image processing using Android device
Korchakov, Sergei ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.
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Detection of characteristic facial features in tele-X-ray image
Hruška, Martin ; Přinosil, Jiří (referee) ; Mišurec, Jiří (advisor)
Description cephalometric images and the characteristic points on the skull for cephalometric analysis. Theoretical analysis of digital image editing and image before the actual detection. The range of possible methods for determining the characteristic points on the face. Experimental verification of edge detectors, Hu moments with neural networks and Haar wavelets with Viola-Jones detector.
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Implementation of Image Classifiers in FPGAs
Kadlček, Filip ; Puš, Viktor (referee) ; Fučík, Otto (advisor)
The thesis deals with image classifiers and their implementation using FPGA technology. There are discussed weak and strong classifiers in the work. As an example of strong classifiers, the AdaBoost algorithm is described. In the case of weak classifiers, basic types of feature classifiers are shown, including Haar and Gabor wavelets. The rest of work is primarily focused on LBP, LRP and LR classifiers, which are well suitable for efficient implementation in FPGAs. With these classifiers is designed pseudo-parallel architecture. Process of classifications is divided on software and hardware parts. The thesis deals with hardware part of classifications. The designed classifier is very fast and produces results of classification every clock cycle.
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Face Detection
Šašinka, Ondřej ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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Detection and Tracking of Small Moving Objects
Filip, Jan ; Zuzaňák, Jiří (referee) ; Hradiš, Michal (advisor)
Thesis deals with the detection and tracking of small moving objects from static images. This work shows a general overview of methods and approaches to detection and tracking of objects. There are also described some other approaches to the whole solution. It also included basic definitions, such a noise, convolution and mathematical morphology. The work described Bayesian filtering and Kalman filter. It described the theory of Wavelets, wavelets filters and transformations. The work deals with different ways of the blob`s detection. It is here the design and implementation of applications, which is based on the wavelets filters and Kalman filter. It`s implemented several methods of background subtraction, which are compared by testing. Testing and application are designed to detect vehicles, which are moving faraway (at least 200 m away).
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Detecting Objects in Images
Kubínek, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This work is dedicated to methods used for object detection in images. There is a summary of several approaches and algorithms to solve this matter, especially AdaBoost algorithm with its improvement, WaldBoost and several features used for object detection. Vital part of this work is dedicated to extending training datasets for classifier training and extending the current object detection framework with histogram of gradients features implementation. Integral part of this work is analysis of results by experiments evaluation.
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Image processing using Android device
Korchakov, Sergei ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
This master’s Thesis focuses on image processing on Android platform and development of an application, that is able to do face detection and recognition in real scene. Thesis gives highlight of modern algorithms of face detection. It first examines and compares the standard features of Android platform (FaceDetector a FaceDetectionListener) and JJIL, OpenIMAJ, OpenCV libraries experiment, and presents the results. For purposes of face recognition was selected OpenCV library. Three different algorithms of identification were tested: FisherFaces, EigenFaces a Local Binary Patterns Histograms. Based on performance comparison best methods were implemented in developed application.
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|
|
Detection of characteristic facial features in tele-X-ray image
Hruška, Martin ; Přinosil, Jiří (referee) ; Mišurec, Jiří (advisor)
Description cephalometric images and the characteristic points on the skull for cephalometric analysis. Theoretical analysis of digital image editing and image before the actual detection. The range of possible methods for determining the characteristic points on the face. Experimental verification of edge detectors, Hu moments with neural networks and Haar wavelets with Viola-Jones detector.
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