The Representation and Matching of Categorical Shape

Abstract

We present a framework for categorical shape recognition. The coarse shape of an object is captured by a multiscale blob decomposition, representing the compact and elongated parts of an object at appropriate scales. These parts, in turn, map to nodes in a directed acyclic graph, in which edges encode both semantic relations (parent/child or sibling) as well as geometric relations. Given two image descriptions each represented as a directed acyclic graph, we draw on spectral graph theory to derive a new algorithm for computing node correspondence in the presence of noise and occlusion. In computing correspondence, the similarity of two nodes is a function of their topological (graph) contexts, their geometric (relational) contexts and their node contents. We demonstrate the approach on the domain of view-based 3-D object recognition.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2005
Accession Number
ADA524828

Entities

People

  • Ali Shokoufandeh
  • Clas Jonsson
  • Diego Macrini
  • Lars Bretzner
  • M. F. Demirci
  • Sven Dickinson

Organizations

  • Drexel University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Science
  • Computer Vision
  • Databases
  • Detection
  • Eigenvalues
  • Geometry
  • Graph Theory
  • Identification
  • Machine Learning
  • Models
  • Object Recognition
  • Orientation (Direction)
  • Recognition
  • Three Dimensional
  • Two Dimensional
  • Vascular System Injuries

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.