Model-based recognition in robot vision

1986/03/01 by Roland T. Chin, R.T. Chin, Charles R. Dyer · 2 citations
Computer Science · Engineering · #Image and Object Detection Techniques #Industrial Vision Systems and Defect Detection #Robotics and Sensor-Based Localization

paper · pdf · doi:10.1145/6462.6464

openalex publication_date 1986/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.

Citations

• A Laser Time-of-Flight Range Scanner for Robotic Vision
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