Learning and surface boundary feedbacks for colour natural scene perception

Pernas, Francisco Javier Díaz; Martínez‐Zarzuela, Mario; Antón-Rodríguez, M.; González-Ortega, D.

doi:10.1016/j.asoc.2017.07.055

Cited by 4 publications

(3 citation statements)

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“…Наложение фильтра на изображение осуществляется путем дискретной свертки с импульсной характеристикой или ядром фильтра с помощью сдвигающегося окна, поэтому для центра окна принимают значения . По формуле Эйлера экспоненту из формулы (1) можно переписать в виде (2) Такая запись фильтра образует квадратурную пару, состоящую из фильтров для определения симметричных и антисимметричных компонентов. При этом в большинстве случаев достаточно применения фильтра для определения симметричных компонент:…”

Section: рис 2 схема слоев выделения краевunclassified

“…При построении нейронных сетей, выполняющих функцию сегментации изображений, возникает задача точного определения границ. Для этого чаще всего используют фильтр Габора (в частности модели HMAX [1] и LPREEN [2] используют именно фильтр Габора). При этом возникает проблема выделения кривых.…”

Section: Introductionunclassified

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New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

Kugaevskikh

2021

jour

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This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.

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Section: рис 2 схема слоев выделения краевunclassified

Section: Introductionunclassified

New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

Kugaevskikh

2021

jour

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“…Unlike previous CNN-based works [ 10 , 23 , 24 , 25 , 26 , 27 ], our neural network includes three processing pathways to deal with the information in three spatial scales. Previous research conducted by our research group and inspired in the inherent multiscale operation of the Human Visual System (HVS) [ 28 ] led us to propose this multiscale processing strategy for tumor classification on the assumption that a multiscale approach would be effective extracting discriminant texture features of different kinds of tumors. The proposed model is very coherent with the HVS processing.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

et al. 2021

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In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.

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Bio-Inspired End-Stopped Neuron Model for the Curves Segmentation

Kugaevskikh

2020

2020 International Russian Automation Conference (RusAutoCon)

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Learning and surface boundary feedbacks for colour natural scene perception

Cited by 4 publications

References 29 publications

New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

Bio-Inspired End-Stopped Neuron Model for the Curves Segmentation

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