Appearance-Based Vision and the Automatic Generation of Object Recognition Programs
Abstract
The generation of recognition programs by hand is a time-consuming, labor-intensive task that typically results in a special purpose program for the recognition of a single object or a small set of objects. Recent work in automatic code generation has demonstrated the feasibility of automatically generating object recognition programs from CAD-based descriptions of objects. Many of the programs which perform automatic code generation employ a common paradigm of utilizing explicit object and sensor models to predict object appearances; we refer to the paradigm as appearance-based vision, and refer to the programs as vision algorithm compilers (VACs). A CAD-like object model augmented with sensor-specific information like color and reflectance, in conjunction with a sensor model, provides all the information needed to predict the appearance of an object under any specified set of viewing conditions. Appearances, characterized in terms of feature values, can be predicted in two ways: analytically, or synthetically. In relatively simple domains, feature values can be analytically determined from model information. However, in complex domains, the analytic prediction method is impractical. An alternative method for appearance prediction is to use an appearance simulator to generate synthetic im ages of objects which can then be processed to extract feature values. In this paper, we discuss the paradigm of appearance-based vision and present in detail two specific VACs: one that computes feature values analytically, and a second that utilizes an appearance simulator to synthesize sample images.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 1992
- Accession Number
- ADA253972
Entities
People
- Katsushi Ikeuchi
- Keith D. Gremban
Organizations
- Carnegie Mellon University