Multiresolution image segmentation

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. The investigation of various Daubechies wavelets shows that the Haar wavelet is the best suited wavelet for the proposed algorithms and the investigated applications. For still image segmentation the Resolution Mosaic Expectation Maximization (RM-EM) algorithm is proposed. The principle of this algorithm is that the conventional EM algorithm is applied to a resolution mosaic of the image as a kind of pre-processing. 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. Fast motions are detected better in the first analysis levels whereas slow motions or motions of big objects in the deeper layers. That is why a combination of different levels gives the best results. 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. All results are compared with others obtained from published work. The new algorithms show robustness against noise and changing ambient conditions and gave better segmentation results.