Fast and accurate edge-based segmentation with no contour smoothingin 2-D real images

We propose an edge-based segmentation algorithm built on a new type of active contour which is fast, has a low computational complexity and does not introduce unwanted smoothing on the retrieved contours. The contours are always returned as closed chains of points, resulting in a very useful base for subsequent shape representation techniques.     

Using the Median Distance to Compare Object Shapes in Content-Based Image Retrieval

Turning Angles (TAs) representation is considered one of the most interesting methods for representing object shapes in content-based image retrieval systems. Nevertheless, the distance commonly used to measure the similarity between shapes represented by TAs, the Euclidean one, is generally too sensitive to small variations in shapes.In this paper we present a new distance between shapes represented by TA, namely the Median distance, specially devised to minimize the effects of small variations in shapes. Its analytical properties are discussed and experimental results are provided and compared with those obtained by applying traditional techniques based on Euclidean distance. The Median distance has been implemented in the Automatic Image Storage and Retrieval (AISR) system, which allows storage and content-based retrieval of 2D images.