The boolean map distance: theory and efficient computation

We propose a novel distance function, the boolean map distance (BMD), that defines the distance between two elements in an image based on the probability that they belong to different components after thresholding the image by a randomly selected threshold value. This concept has been explored in a number of recent publications, and has been proposed as an approximation of another distance function, the minimum barrier distance (MBD). The purpose of this paper is to introduce the BMD as a useful distance function in its own right. As such it shares many of the favorable properties of the MBD, while offering some additional advantages such as more efficient distance transform computation and straightforward extension to multi-channel images

Modal matching : a method for describing, comparing, and manipulating digital signals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 134-144).by Stanley Edward Sclaroff.Ph.D

Deformable solids and displacement maps--a multi-scale technique for model recovery and recognition

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1991.Includes bibliographical references (leaves 74-78).by Stanley Edward Sclaroff.M.S

Shape-Based Indexing in a Medical Image Database

In this work, we report on a prototype of a clinical radiograph image database to be indexed by image content. The underlying content-based search engine is based on the modal shape description method for characterizing the shape of a 2-D image region [24]. Using a similarity metric defined in a modal vector space, our system successfully classified radiographic images according to the dental pathologies they depicted. This successful classification demonstrates that the proposed similarity metric effectively captures clinical similarity between images in the database. The prototype is implemented in a Web-based environment, allowing remote users in the field to search a central repository of images. Examples of classification performance and typical queries are provided