Systeme der Computer Vision spielen in der Automatisierung vieler Prozesse eine wichtige Rolle. Die wichtigste Aufgabe solcher Systeme ist die Automatisierung des visuellen Erkennungsprozesses und die Extraktion der relevanten Information aus Bildern oder Bildsequenzen. Eine wichtige Komponente dieser Systeme ist die Bildsegmentierung, denn sie bestimmt zu einem großen Teil die Qualitaet des Gesamtsystems. Fuer die Segmentierung von Bildern und Bildsequenzen werden neue Algorithmen vorgeschlagen. Das Konzept der Multiresolution wird als eigenstaendig dargestellt, es existiert unabhaengig von der Wavelet-Transformation. Die Wavelet-Transformation wird zur Verarbeitung von Bildern und Bildsequenzen zu einer 2D- bzw. 3D-Wavelet- Transformation erweitert. Fuer die Segmentierung von Bildern wird der Algorithmus Resolution Mosaic Expectation Maximization (RM-EM) vorgeschlagen. Das Ergebnis der Vorverarbeitung sind unterschiedlich aufgeloesten Teilbilder, das Aufloesungsmosaik. Durch dieses Mosaik lassen sich raeumliche Korrelationen zwischen den Pixeln ausnutzen. Die Verwendung unterschiedlicher Aufloesungen beschleunigt die Verarbeitung und verbessert die Ergebnisse. Fuer die Extraktion von bewegten Objekten aus Bildsequenzen werden neue Algorithmen vorgeschlagen, die auf der 3D-Wavelet-Transformation und auf der Analyse mit 3D-Wavelet-Packets beruhen. Die neuen Algorithmen haben den Vorteil, dass sie sowohl die raeumlichen als auch die zeitlichen Bewegungsinformationen beruecksichtigen. Wegen der geringen Berechnungskomplexitaet der Wavelet-Transformation ist fuer den ersten Segmentierungsschritt Hardware auf der Basis von FPGA entworfen worden. Aktuelle Anwendungen werden genutzt, um die Algorithmen zu evaluieren: die Segmentierung von Magnetresonanzbildern des menschlichen Gehirns und die Detektion von bewegten Objekten in Bildsequenzen von Verkehrsszenen. Die neuen Algorithmen sind robust und fuehren zu besseren Segmentierungsergebnissen. / More and more computer vision systems take part in the automation of various applications. The main task of such systems is to automate the process of visual recognition and to extract relevant information from the images or image sequences acquired or produced by such applications. One essential and critical component in almost every computer vision system is image segmentation. The quality of the segmentation determines to a great extent the quality of the final results of the vision system. New algorithms for image and video segmentation based on the multiresolution analysis and the wavelet transform are proposed. The concept of multiresolution is explained as existing independently of the wavelet transform. The wavelet transform is extended to two and three dimensions to allow image and video processing. For still image segmentation the Resolution Mosaic Expectation Maximization (RM-EM) algorithm is proposed. The resolution mosaic enables the algorithm to employ the spatial correlation between the pixels. The level of the local resolution depends on the information content of the individual parts of the image. The use of various resolutions speeds up the processing and improves the results. New algorithms based on the 3D wavelet transform and the 3D wavelet packet analysis are proposed for extracting moving objects from image sequences. The new algorithms have the advantage of considering the relevant spatial as well as temporal information of the movement. Because of the low computational complexity of the wavelet transform an FPGA hardware for the primary segmentation step was designed. Actual applications are used to investigate and evaluate all algorithms: the segmentation of magnetic resonance images of the human brain and the detection of moving objects in image sequences of traffic scenes. The new algorithms show robustness against noise and changing ambient conditions and gave better segmentation results.

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[pt] A melhoria nas técnicas de obtenção de dados sísmicos fez com que estes dados sejam da ordem de até terabytes, o que gera um grande problema no armazenamento e transmissão dos mesmos. No que se refere ao problema de armazenamento em disco, apesar da queda no preço do disco rígido, o avanço das técnicas de obtenção do dado sísmico e o barateamento destas técnicas de obtenção do dado fazem com que a demanda por espaço em disco cresça mais rápido do que a queda no preço do hardware. Outro fator importante é a limitação de espaço físico para armazenar todos os discos rígidos de forma a garantir a segurança da informação destes discos. Este trabalho tem como objetivo apresentar uma nova técnica de compactação para dados sísmicos utilizando a transformada wavelet 3D. É feito um comparativo com as técnicas de compressão atuais, que utilizam a transformada 2D. / [en] Improvements made in techniques for seismic data acquisition have led to data that are of the order of terabytes, which in turn creates the problem of data storage and transmission of them through the network. In regards to the problem of disk storage, despite the hard disk decreasing price, the improvement and reduction cost in seismic data acquisition techniques has pushed the demand for disk space to grow faster than the reduction of the hardware cost. Another important factor is the space necessary to store all hard drives, to provide security for information from these records. In order to tackle these problems this thesis presents a new technique for seismic data compression using 3D wavelet transform. It is also presented in comparison with current compression techniques which use the 2D transform.

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