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A connectionist model for corner detection in binary and gray images.

J Basak¹, D Mahata

¹Machine Intelligence Unit, Indian Statistical Institute, Calcutta, 700 035, India.

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

A novel connectionist model detects corner points in images using pixel cornerity and neural networks. This method accurately identifies corners in noisy and open-boundary images, enhancing image analysis.

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Published on: November 2, 2012

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Corner detection is crucial for image analysis and computer vision tasks.
Existing methods face challenges with noisy images and open object boundaries.

Purpose of the Study:

To develop a robust connectionist model for accurate corner point detection in binary and grayscale images.
To enhance corner detection capabilities for challenging image conditions.

Main Methods:

A connectionist model with state dynamics was developed, assigning initial cornerity vectors to each pixel.
A cooperative neural network framework updates cornerity based on neighborhood information.
Stable network states reveal dominant corner points via local maxima analysis.

Main Results:

The model successfully detects corner points in noisy images and for open object boundaries.
The dynamics were extended to effectively utilize edge information from grayscale images.
Experimental validation on synthetic and real-life images confirmed model effectiveness.

Conclusions:

The developed connectionist model offers a stable and convergent approach to corner detection.
It demonstrates superior performance in handling image noise and incomplete object boundaries.
The model provides a valuable tool for advanced image analysis applications.