preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.0399.v1 (registering DOI)

Preprint Review Version 1 This version is not peer-reviewed

Version 1 : Received: 3 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (08:55:37 CEST)

Traditional computer vision techniques aim to extract meaningful information from images but often depend on manual feature engineering, making it difficult to handle complex real-world scenarios. Fractional Calculus (FC), which extends derivatives to non-integer orders, provides a flexible way to model systems with memory effects and long-term dependencies, making it a powerful tool for capturing fractional rates of variation. Recently, Neural Networks (NNs) have demonstrated remarkable capabilities in learning complex patterns directly from raw data, automating computer vision tasks and enhancing performance. Therefore, the use of fractional calculus in neural network-based computer vision is a powerful method to address existing challenges by effectively capturing complex spatial and temporal relationships in images and videos. This paper presents a survey of Fractional Calculus Neural Network-based (FC NN-based) computer vision techniques for denoising, enhancement, object detection, segmentation, restoration, and NN compression. The survey compiles existing FFC NN-based approaches, elucidates underlying concepts, and identifies open questions and research directions. By leveraging FC's properties, FC NN-based approaches offer a novel way to improve the robustness and efficiency of computer vision systems.

computer vision; neural networks; fractional calculus; object detection; denoising; segmentation; image generation

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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