Fourier Transform Layer for Fast Foreground Segmentation in Samples’ Images of Tissue Biopsies

Żak, Jerzy; Siemion, Krzysztof; Roszkowiak, Łukasz; Korzyńska, Anna

doi:10.1007/978-3-030-83704-4_12

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Jakub Zak et al suggest a convolution and FFT-based method for image classification. In this research, convolution operator is used to filter the image features for classification and Fourier Transform helps to speed up the performance of method [57]. Xue-Hua Wu et al presented a paper based on Hough transform for finding zebra crossing by determining road image edges.…”

Section: E Hough Transform Based Methodsmentioning

confidence: 99%

An Astute Automaton Model for Objects Extraction Using Outer Totality Cellular Automata (OTCA)

Sharma,

Basheer

et al. 2023

IEEE Access

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Object formation is imperative to the recent computer vision, pattern recognition, healthcare, and automation applications. The objects are generated from images by defining edges and the segmentation process. This article introduced a novel method, Outer Totality Cellular Automata (OTCA), for defining actual and continuous edges of the image objects. The OTCA analysis nearby 25 neighbourhood pixels of all the pixels and generate a unique and efficient threshold. The proposed method has three primary functions, i.e. vitality, rule mapping, and improved morphological functions. The key objectives are image smoothing, neighbourhood analysis, defining game of life rule, and edges smoothing. Notably, the proposed method aimed to segment different coloured images, i.e., RGB, HSV, and YUV. The proposed method also aimed to produce more truthful results on blurred, reflected, shaded night vision images. The experimental process demonstrates using standard open-source datasets and validated using image quality assessment parameters, i.e., entropy, PSNR, SSIM, and MSE. The results claim 3% − 12% more structural analogous, factual, and accurate than existing classical methods and recent searches.

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Section: E Hough Transform Based Methodsmentioning

confidence: 99%