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A fast thresholded linear convolution representation of morphological operations.

B Kisacanin¹, D Schonfeld

¹Dept. of Electr. Eng. and Comput. Sci., Illinois Univ., Chicago, IL.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1994

Summary

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We introduce a fast thresholded linear convolution representation for morphological operations like dilation and erosion. This novel method offers improved efficiency compared to direct implementations in image processing.

Area of Science:

Image Processing
Computer Vision
Signal Processing

Background:

Mathematical morphology is a key technique in image analysis.
Morphological operations like dilation and erosion are fundamental.
Efficient computation of these operations is crucial for real-time applications.

Purpose of the Study:

To propose a novel thresholded linear convolution representation for morphological operations.
To introduce the thresholded linear convolution representation for dilation and erosion specifically.
To compare the computational efficiency of this new representation against direct implementations.

Main Methods:

Developing a thresholded linear convolution approach for morphological operations.
Implementing and evaluating the proposed representation for dilation and erosion.

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Last Updated: Jul 7, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Benchmarking the performance against standard direct convolution methods.

Main Results:

The thresholded linear convolution representation provides a fast method for morphological operations.
Demonstrated efficiency gains over direct implementation of dilation and erosion.
Established a new computational paradigm for mathematical morphology.

Conclusions:

The thresholded linear convolution representation is an efficient alternative for morphological operations.
This approach has significant implications for accelerating image processing tasks.
Mathematical morphology continues to evolve with new efficient computational strategies.