A general image analysis and segmentation method using fuzzy set classification and learning, which appears ideal for applications involving visual ...
Robert J. Schalkoff, Albrecht E. Carver, Sabri Gurbuz: Image segmentation using trainable fuzzy set classifiers. Visual Information Processing 1999: 9-17.
Abstract. A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy ...
This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years ...
We present an integrated fusion method for large-scale dataset classification. We propose an integrated fusion of fuzzy learning and graph neural networks.
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Oct 22, 2024 · In this article, the optimization of the modified U-Net neural network model extended with fuzzy layers has been studied with the usage of Grid search and ...
Jul 15, 2021 · In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures.
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We combined histogram oriented region and linear discriminant analysis for features extraction. The Neuro-fuzzy classifier is trained using extracted features.
Mar 14, 2024 · FDNN is a hierarchical deep neural network that derives information from both fuzzy and neural representations, the representations are then ...
A pixel-based segmentation is computed here using local features based on local intensity, edges and textures at different scales.