Three-dimensional(3D) shape representation is a powerful tool in object recognition that is an essential process in an image processing and analysis system. Skeleton is one of the most widely used representations for object recognition, nevertheless most of the skeletons obtained from conventional methods are susceptible to rotation and noise disturbances. In this paper, we present a new 3D object representation called a modified exoskeleton (mES) which preserves skeleton properties including significant characteristics about an object that are meaningful for object recognition, and is more stable and less susceptible to rotation and noise than the skeletons. Then a 3D shape recognition methodology which determines the similarity between an observed object and other known objects in a database is introduced. Through a number of experiments on 3D artificial objects and real volumetric lung tumors extracted from CT images, it can be verified that our proposed methodology based on the mES is a simple yet efficient method that is less sensitive to rotation, noise, and independent of orientation and size of the objects.
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