Study On Image Segmentation Methods Based On Transition Region Theory

Image segmentation is an important technique for image analysis. In the study and application of images, researchers usually focus on some certain parts of images, which are often named target or foreground (the rest parts are called background), generally corresponding to the given particular region. As the essential processing approach and the key point on computer vision study, image segmentation becomes the base target recognition and image understanding, As the results of segmentation have direct effect on sequent recognition and understanding, many reseaerchers are working on new segmentation algorithms and theories.This paper made an intensive study on transition region based image segmentation technique, stressing on non-gradient transition region extraction method. Three methods are presented, one of which is multi-scale transitional region extraction and segmentation method based on orientation information measure, the other is transition region extraction and segmentation method based on image neighborhood unhomogeneity measure, still another is transition region extraction and segmentation based on image fuzzy entropy region unhomogeneity measure. All the three methods are significantly resistant to salt and pepper noices. The essence of the noise resistance lies in the calculation of the measurement which is insensitive to the changes of the grey scales. The three methods are good at the image transition extraction and segmentation, and at noise resistance. The fuzzy entropy region unhomogeneity method has a better effect on mixed noices as a result of the introduction of fuzzy entropy.When an image contains several types of targets, the transition region histogram presents multi-peak distributing, each corresponding to a possible segmentation region. A potential function clustering based multi-threshold segmentation algorithm is adopted to detect adjacent division potential function group of the transition region histogram, the intersection of the curve is segmentation threshold, and then using differentiation and cost function to determine the number of prime types and the threshold. The threshold resulted from the simple algorithm will be accurate and stable. The algorithm also shows faster operating speed and better real time control. The multi-threshold segmentation problem is easily solved in the image segmentation technique based on transition region extraction.When the image is stained by both gaussian noises and pulse noises, neither average filter algorithm nor median filter algorithm can denoise effectively. This paper presents a self-adaptive mixture filter algorithm based on region classification. Using neighborhood homogeneity measurement, the image is divided into different regions where diverse filter methods are employed to deal with the noises. In this way we guaranteed that average filter algorithm has an unusual power to gaussian noises while we also ensured that median filter algorithm is effective to maintain the details of the image.Texture image is a type of complicated image whose texture is the dominate feature. Texture images present local irregularity while as a whole they take on statistical regularity. As texture has a regional characteristic texture will be reflected or measured in a given region of the image. Since grey scale information of pixel alone cannot segment different regions, this paper proposed a monogenic phase based texture segmentation method. Feature image extraction is based on the distribution of monogenic phase information. This method can obtain better segmentation results which well described the features among different textures and between textures and objects.X-ray testing images are important basis of weld defect analysis and quality evaluation, the traditional method is to make circular and bar segmentation with step edges and roof edges.This paper proposed a weld defect segmentation algorithm based on transitional region multiscale industrial X-ray images.Under different scales, the size of the defect can be inspected. Using support vector machine the weld defects are divided into circular and bar defects meanwhile the cracks, gas cavity, slag and lack of penetration can be recognized.