A Survey of Image Segmentation by the Classical Method and Resonance Algorithm

Computer vision and recognition plays more important role on intelligent control (Chen & Hwang, 1998). For an intelligent system, it is necessary to acquire the information of the external world by sensors, to recognize its position and the surrounding situation. Camera is one of the most important sensors for computer vision. That is to say, the intelligent system endeavours to find out what is in an image taken by the camera: traffic signs, obstacles or guidelines. For image analysis, image segmentation is needed, which means to partition an image into several regions that have homogeneous texture feature. This process is usually realized by the region-based, boundarybased or edge-based method (Castleman, 1998). And from the viewpoint of clustering, it is divided into supervised and unsupervised texture segmentation. Since before segmentation, the intelligent control system seldom knows the feature of the image, e.g. which type and how many types of textures exist in an image, thus the unsupervised segmentation algorithm is always needed, although it is more difficult than the supervised method (Dai, Zhao & Zhao, 2007). In this chapter, the classical method (Agui & Nagao, 2000) and the resonance theory (Heins & Tauritz, 1995; He & Chen, 2000) are proposed respectively for image segmentation. The classical method is simple but practicable, which will be introduced in section 2. But for some situations, it is not suitable for complex image segmentation (e.g., the gradient variations of intensity in an image). We know that human vision can recognize the same texture that has gradient variations of intensity. And many image segmentation methods are proposed based on the change of intensity (Nakamura & Ogasawara, 1999; Deguchi & Takahashi, 1999). But they always fail to handle the wide-ranged gradations in intensity (Jahne, 1995). It is usually difficult to give a suitable threshold for pixel-based image processing methods to deal with this gradation. Resonance algorithm is an unsupervised method to generate the region (or feature space) from similar pixels (or feature vectors) in an image. It tolerates gradual changes of texture to some extent for image segmentation. The purpose of section 3 is to propose the resonancetheory-based method for image segmentation, which means that the same texture in an image will be resonated into one region by seed pixels. This method assumes that the