Object detection in watershed partitioned gray-level images

Gray-level image segmentation is the first task for any image analysis process, and is necessary to distinguish the objects of interest from the background. Segmentation is a complex task, especially when the gray-level distribution along the image is such that sets of pixels characterized by a given gray-level are interpreted by a human observer as belonging to the foreground in certain parts of the image, and to the background in other parts, depending on the local context. It very seldom happens that the background is characterized by an almost uniform gray-level. Thus, in the majority of cases, segmentation cannot be achieved by simply thresholding the image, i.e., by assigning all pixels with gray-level lower than a given threshold to the background and all remaining pixels to the foreground. One of the most often adopted segmentation techniques is based on a preliminary partition of the input gray-level image into regions, homogeneous with respect to a given property, to successively classify the obtained regions in two classes (foreground and background). In this paper, we follow this approach and present a powerful method to discriminate regions in a partition of a gray-level image obtained by using the watershed transformation. The basic idea underlying the classification is that for a wide class of graylevel images, e.g., a number of biological images, the boundary between the foreground and the background is perceived where locally maximal changes in gray-level occur through the image. Our classification procedure works well even starting from a standard watershed partition, i.e., without resorting to seed selection and region growing. However, we will also briefly discuss new criteria to be used when applying digging and flooding techniques in the framework of watershed transformation, so as to produce a less fragmented partition of the image. By using the so obtained partition of the gray-level image, the successive classification is facilitated and the quality of the obtained results is improved. Some hints regarding the use of multi-scale image representation to reduce the computational load will also be introduced.