Very Fast Rotation and Scale Invariant Multiple Template Matching Using Histogram-based Associative Network
Year: 2025
Type: article
Abstract: This paper introduces a very fast method for histogram multi-template matching using a fast Multi-layer Perceptron (MLP) based auto associative memory. The method achieves a high-speed improvement by rapidly evaluating the hidden unit outputs. We demonstrate that the hidden unit outputs can be rapidly calculated for any region and scale in a constant time using the integral image technique. Compared to traditional methods like the conventional, distributive, and integral histogram methods, the proposed approach outperforms them with speedups of up to thousands of times. Furthermore, it preserves the desirable properties of rotation and scale invariance.
Authors: Leila Essannouni, Fedwa Essannouni
Institution: Mohammed V University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411094560