图像阈值处理.doc

图像阈值处理
Image segmentation studies
Image segmentation is one of the primary steps in image analysis for object identification. The main aim is to recognise homogeneous regions within an image as distinct and belonging to different objects. Segmentation stage does not worry about the identity of the objects. They can be labelled later. The segmentation process can be based on finding the maximum homogeneity in grey levels within the regions identified.
There are several issues related to image segmentation that require detailed review. One of the common problems encountered in image segmentation is choosing a suitable approach for isolating different objects from the background. The segmentation doesn?t perform well if the grey levels of different objects are quite similar. Image enhancement techniques seek to improve the visual appearance of an image. They emphasize the salient features of the original image and simplify the task of image segmentation. The type of operator chosen has a direct impact on the quality of the resultant image. It is expected that an ideal operator will enhance the boundary differences between the objects and their background making the image segmentation task easier. Issues related to segmentation involve choosing good segmentation algorithms, measuring their performance, and understanding their impact on the scene analysis system.
Segmentation techniques
We review primarily those studies that are based on finding object regions in grey-level images. We also mention couple of studies that deal with colour segmentation to highlight how this has been used for outdoor scene analysis. Image segmentation has been approached from a wide variety of perspectives[1]. Our summary is presented for histogram thresholding, edge based segmentation, tree/graph based approaches, region growing, clustering, probabilistic or Bayesian approaches, neural networks for segmentation, and other approaches.
Histogram Thresholding
Ohlander[2] propose