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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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Object Tracking in Video
Sojma, Zdeněk ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This master's thesis describes principles of the most widely used object tracking systems in video and then mainly focuses on characterization and on implementation of an interactive offline tracking system for generic color objects. The algorithm quality consists in high accuracy evaluation of object trajectory. The system creates the output trajectory from input data specified by user which may be interactively modified and added to improve the system accuracy. The algorithm is based on a detector which uses a color bin features and on the temporal coherence of object motion to generate multiple candidate object trajectories. Optimal output trajectory is then calculated by dynamic programming whose parameters are also interactively modified by user. The system achieves 15-70 fps on a 480x360 video. The thesis describes implementation of an application which purpose is to optimally evaluate the tracker accuracy. The final results are also discussed.
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Exploitation of Graphics Processor as Accelerator - OpenCL Technology
Hrubý, Michal ; Jošth, Radovan (referee) ; Zemčík, Pavel (advisor)
This work deals with the OpenCL technology and its use for the task of object detection. The introduction is devoted to description of OpenCL fundamentals, as well as basic theory of object detection. Next chapter of the work is analysis, with design proposal which takes into consideration the possibilities of OpenCL. Further, there's description of implementation of detection application and experimental evaluation of detector's performance. The last chapter summarizes the achieved results.
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Human-Machine Interface Based on Gestures
Charvát, Jaroslav ; Beran, Vítězslav (referee) ; Bartoň, Radek (advisor)
Master's thesis "Human-Machine Interface Based on Gestures" depicts the theoretical background of the computer vision and gesture recognition. It describes more in detail different methods that were used to create the application. Practical part of this thesis consists of the description of the developed program and its functionality. Using this application, user should be able to control computer by gestures of both right and left hands and also his head. The program is primarily based on the skin detection that is followed by the recognition of palms and head gestures. There were used two essential methods for these actions, AdaBoost and PCA.
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AdaBoost in Computer Vision
Hradiš, Michal ; Zemčík, Pavel (referee) ; Potúček, Igor (advisor)
In this thesis, we present the local rank differences (LRD). These novel image features are invariant to lighting changes and are suitable for object detection in programmable hardware, such as FPGA. The performance of AdaBoost classifiers with the LRD was tested on a face detection dataset with results which are similar to the Haar-like features which are the state of the art in real-time object detection. These results together with the fact that the LRD are evaluated much faster in FPGA then the Haar-like features are very encouraging and suggest that the LRD may be a solution for future hardware object detectors. We also present a framework for experiments with boosting methods in computer vision. This framework is very flexible and, at the same time, offers high learning performance and a possibility for future parallelization. The framework is available as open source software and we hope that it will simplify work for other researchers.
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Pattern Recognition in Image Using Classifiers
Juránek, Roman ; Španěl, Michal (referee) ; Herout, Adam (advisor)
An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the method of construction of strong classifiers will be explained. WaldBoost extension to the algorithm will be described. The thesis deals with image features that are often used as element of weak classifiers. Brief introduction to pattern recognition in context of computer vision will be outlined in the begining of the work. Also some widely used methods of classifier training will be presented. An object detection library based on AdaBoost classifiers was developed as part of the work. The library was used in implementation of software that in praktice demonstrates object detection in videosquences. Last part of the work describes tool for training of AdaBoost classifiers.
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Recognizing Faces within Image
Svoboda, Pavel ; Žák, Pavel (referee) ; Švub, Miroslav (advisor)
The essence of face recognition within the image is generally computer vision, which provides methods and algorithms for the implementation. Some of them are described just in this work. Whole process is split in to three main phases. These are detection, aligning of detected faces and finally its recognition. Algorithms which are used to applied in given issue and which are still in progress from todays view are mentioned in every phase. Implementation is build up on three main algorithms, AdaBoost to obtain the classifier for detection, method of aligning face by principal features and method of Eigenfaces for recognizing. There are theoretically described except already mentioned algorithms neural networks for detection, ASM - Active Shape Models algorithm for aligning and AAM - Active Appearance Model for recognition. In the end there are tables of data retrieved by implemented system, which evaluated the main implementation.
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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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Face Detection in Video
Kolman, Aleš ; Řezníček, Ivo (referee) ; Polok, Lukáš (advisor)
The project is focused on face detection in video. Firstly, it contains a summary of basic color models. Secondly, you can find the description and comparison of the basic methods for detection of human skin with a practical example of implementation of parametric detector. Thirdly, a theoretical basis for face detection and face tracking in a video containing a list of basic concepts and methods of this issue follows. Greater emphasis is placed on the description of machine learning algorithm AdaBoost and description of the possible application of the Kalman filter for the purpose of face tracking. Design, implementation and testing of library accomplished within the master thesis are listed in the final part of this thesis.
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