The purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data. The image data is transformed implicitly by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable. The objective function contains an original data term to evaluate the deviation of the transformed data, within each segmentation region, from the piecewise constant model, and a smoothness, boundary preserving regularization term. The method affords an effective alternative to complex modeling of the original image data while taking advantage of the computational benefits of graph cuts. Using a common kernel function, energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation. A quantitative and comparative performance assessment is carried out over a large number of experiments using synthetic grey level data as well as natural images from the Berkeley database. The effectiveness of the method is also demonstrated through a set of experiments with real images of a variety of types such as medical, synthetic aperture radar, and motion maps.

The purpose of this study is to investigate Synthetic Aperture Radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.

The purpose of this paper is to introduce a fast automated whitenoise estimation method which gives reliable estimates in images with smooth and textured areas. This method is a block-based method that takes image structure into account and uses a measure other than the variance to determine if a block is homogeneous. It uses no thresholds and automates the way that blockbased methods stop the averaging of block variances. The proposed method selects intensity-homogeneous blocks in an image by rejecting blocks ofstructure using a new structure analyzer. The analyzer used is based on high-pass operators and special masks for corners to allow implicit detection of structure and to stabilize the homogeneity estimation. For typical image quality (PSNR of 2040 dB) the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB which is suitable for real applications such as video suweillance or broadcasts. The method performs well even in images with few smooth areas and in highly noisy images.

The goal of this paper is to offer a structured synopsis of the problems in image motion computation and analysis, and of the methods proposed, exposing the underlying models and supporting assumptions. A sufficient number of pointers to the literature will be given, concentrating mostly on recent contributions. Emphasis will be on tile detection, measurement and segmentation of image motion. Tracking, and deformable motion isssues will be also addressed. Finally, a number of related questions which could require more investigations will be presented.