Massively Parallel Bayesian Object Recognition

Abstract

The problem of model-based object recognition is a fundamental one in the field of computer vision, and represents a promising direction for practical applications. We describe the design, analysis, implementation and testing of a system that employs geometric hashing techniques, and can recognize three-dimensional objects from two-dimensional grayscale images. We examine the exploitation of parallelism in object recognition, and analyze the performance and sensitivity of the geometric hashing method in the presence of noise. We also present a Bayesian interpretation of the geometric hashing approach. Two parallel algorithms are outlined: one algorithm is designed for an SIMD hypercube-based machine whereas the other algorithm is more general, and relies on data broadcast capabilities. The first of the two algorithms regards geometric hashing as a connectionist algorithm. The second algorithm is inspired by the method of inverse indexing for data retrieval. We also determine the expected distribution of computed invariants over the hash space: formulas for the distributions of invariants are derived for the cases of rigid, similarity and affine transformations, and for two different distributions (Gaussian and Uniform over a disc) of point features. Formulas describing the dependency of the geometric invariants on Gaussian positional error are also derived for the similarity and affine transformation cases. Finally, we present an interpretation of geometric hashing that allows the geometric hashing algorithm to be viewed as a Bayesian approach to model-based object recognition. This interpretation is a new form of Bayesian-based model matching, and leads to natural, well-justified formulas. The interpretation also provides a precise weighted-voting method for the evidence-gathering phase of geometric hashing. A prototype object recognition system using these ideas has been implemented on a CM-2 Connection Machine.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1992
Accession Number
ADA598403

Entities

People

  • Isidore Rigoutsos

Organizations

  • New York University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computer Science
  • Computer Vision
  • Computers
  • Databases
  • Detectors
  • Gaussian Processes
  • Image Processing
  • Military Aircraft
  • Pattern Recognition
  • Probability Density Functions
  • Random Variables
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects