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Related Experiment Video

Updated: May 29, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Using polygons to recognize and locate partially occluded objects.

M W Koch¹, R L Kashyap

¹Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13676.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces computer vision algorithms for recognizing and locating partially occluded objects using a generate-test paradigm. The algorithms effectively identify objects even when they overlap or touch, improving scene understanding.

Related Experiment Videos

Last Updated: May 29, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Object recognition and localization are crucial for autonomous systems.
Partial occlusion presents a significant challenge in real-world computer vision tasks.
Existing methods struggle with unknown, overlapping, or touching objects.

Purpose of the Study:

To develop novel computer vision algorithms for recognizing and locating partially occluded objects.
To address the challenge of unknown objects that may touch or overlap within a scene.
To provide a robust framework for object detection in complex environments.

Main Methods:

A generate-test paradigm is employed, iteratively generating and testing hypotheses.
Polygon representations of object boundaries, specifically "corners," guide hypothesis generation and verification.
Polygon moment calculations and association graphs are used for matching and compatibility assessment.
Coordinate transformations map models to scene locations, and region consistency checks verify hypotheses.

Main Results:

The algorithms successfully recognize and locate partially occluded objects.
The use of polygon representations and "corners" proved effective in detection and localization.
Experimental results demonstrate the efficacy of the hypothesis generation and verification phases.
The system accurately estimates object location and orientation even with partial occlusion.

Conclusions:

The presented computer vision algorithms offer a robust solution for object recognition and localization under partial occlusion.
The generate-test paradigm, combined with polygon-based feature extraction, enhances performance in complex scenes.
This approach provides a foundation for more advanced object manipulation and interaction in robotics and AI.