Segmenting video frame images using genetic algorithms

Image segmentation plays an important role in computer vision. It is a process that partitions a digital image into several meaningful regions ,by identifying regions of an image that have common properties while separating regions that are dissimilar. The image segmentation problem is posed as an optimization procedure. In this thesis, an optimization approach based on genetic algorithm is introduced for finding optimal image segmentation. The design and implementation of genetic algorithm image segment or (GSAI) system are described. GSAI system employs finds optimal value using genetic operators "crossover operator and mutation operator". The different proposed / implementation segmentation methods of the GSAI system were tested using Gray image are taken from one films and with size 352x240 pixels for video frames images of In this is work focused on genetic algorithm coefficients which affect in direct and active way in the work of GA to study and analysis dependable video images which are taken from video clips after partitioning to multiple frames

Image segmentation using genetic algorithm and morphological operations

Image segmentation is a fundamental component of picture processing and image analysis. Segmentation of an image entails the division or separation of the image into regions of similar attributes. The most basic attribute for segmentation is the image intensity (luminance for a monochromatic image). Several classical methods for image segmentation exist and it is well known that these methods are more or less heuristic and specific to a particular application. Genetic Algorithms (GA) are stochastic search methods, the functioning of which is inspired by laws of genetics, natural selection and evolution of organisms. Their main attractive characteristic is the ability to deal with hard combinatorial search problems efficiently, where parallel exploration of the search space, eliminates to a large extent the possibility of getting stuck in the local extrema. The basis of the theory is that individuals tend to pass on their traits to their offspring and the fittest of the individuals tend to have more offsprings. In effect, the tendency is to drive the population towards favorable traits. Over long periods of time, entirely new species are produced which are better adapted to a particular ecological condition. This thesis proposes a simple and robust method for image segmentation that is based on the application of Genetic Algorithm and Mathematical Morphology. The image is divided into nonoverlapping subimages and the genetic algorithm is applied to each subimage, starting with initial random populations. Each individual of the population is evaluated using an appropriate fitness function. The best-fit individuals are selected and mated to produce offsprings to form the next generation. Morphological operations are used to produce the next generation along with the crossover and mutation operators. The algorithm converges to yield the final segmented subimage. These segmented subimages then are combined to form the final result. The feasibility of applying genetic algorithm and morphological operations to an image segmentation problem is evaluated and results are presented and discussed

Automatic evolutionary medical image segmentation using deformable models

International audienceThis paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed