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- Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia
- IEEE Transactions on Pattern Analysis and Machine…
- 2004

This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high-dimensional, three steps are taken to make the search practical: 1) define… (More)

This paper presents a novel recognition framework which is based on matching shock graphs of 2D shape outlines, where the distance between two shapes is defined to be the cost of the least action path deforming one shape to another. Three key ideas render the implementation of this framework practical. First, the shape space is partitioned by defining an… (More)

- Niloofar Gheissari, Thomas B. Sebastian, Richard I. Hartley
- 2006 IEEE Computer Society Conference on Computer…
- 2006

In many surveillance applications it is desirable to determine if a given individual has been previously observed over a network of cameras. This is the person reidentification problem. This paper focuses on reidentification algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Person… (More)

- Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia
- IEEE Trans. Pattern Anal. Mach. Intell.
- 2003

We present a novel approach to finding a correspondence (alignment) between two curves. The correspondence is based on a notion of an alignment curve which treats both curves symmetrically. We then define a similarity metric based on the alignment curve using two intrinsic properties of the curve, namely, length and curvature. The optimal correspondence is… (More)

- Thomas B. Sebastian, Benjamin B. Kimia
- Signal Processing
- 2001

The type of representation used in describing shape can have a significant impact on the effectiveness and efficiency of a recognition strategy. Shape has been represented by a point set, outline curve and the shock graph (or medial axis). The curve-based representation can be viewed as point-based representation with additional organization, namely, order… (More)

The in vivo investigation of joint kinematics in normal and injured wrist requires the segmentation of carpal bones from 3D (CT) images, and their registration over time. The non-uniformity of bone tissue, ranging from dense cortical bone to textured spongy bone, the irregular shape of closely packed carpal bones, small inter-bone spaces compared to the… (More)

This paper examines issues arising in applying a previously developed edit-distance shock graph matching technique to indexing into large shape databases. This approach compares the shock graph topology and attributes to produce a similarity metric, and results in 100% recognition rate in querying a database of approximately 200 shapes. However, indexing… (More)

We present an approach to computing a curve atlas based on deriving a correspondence between two curves. This correspondence is based on a notion of an alignment curve and on a measure of similarity between the intrinsic properties of the curve, namely, length and curvature. The optimal correspondence is found by an efficient dynamicprogramming method. This… (More)

We present a 2D shape recognition and classification method based on matching shape outlines. The correspondence between outlines (curves) is based on a notion of an alignment curve and on a measure of similarity between the intrinsic properties of the curve, namely, length and curvature, and is found by an efficient dynamic-programming method. The… (More)

- Thomas B. Sebastian, Benjamin B. Kimia
- ICPR
- 2002

This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the “curse of dimensionality”. Thus, techniques designed for searching metric… (More)