A fast training approach to artificial neural networks designed for image segmentation

2002 ◽  
Author(s):  
H.A. Malki ◽  
A. Moghaddamjoo
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Fast Training ◽  
Training Approach ◽  
Artificial Neural

scholarly journals Use of artificial neural networks and geographic objects for classifying remote sensing imagery

CERNE ◽  
2014 ◽  
Vol 20 (2) ◽  
pp. 267-276 ◽  
Author(s):  
Pedro Resende Silva ◽  
Fausto Weimar Acerbi Júnior ◽  
Luis Marcelo Tavares de Carvalho ◽  
José Roberto Soares Scolforo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Forest Inventory ◽  
Landsat Tm ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Artificial Neural ◽  
Test Use

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

scholarly journals Building Recognition in Air and Satellite Photos

2019 ◽  
Author(s):  
Дмитрий Булатицкий ◽  
Dmitriy Bulatitskiy ◽  
Александр Буйвал ◽  
Aleksandr Buyval ◽  
Михаил Гавриленков ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Convolutional Neural Networks ◽  
Information Platform ◽  
Software Complex ◽  
Building Recognition ◽  
Artificial Neural

The paper deals with the algorithms of building recognition in air and satellite photos. The use of convolutional artificial neural networks to solve the problem of image segmentation is substantiated. The choice between two architectures of artificial neural networks is considered. The development of software implementing building recognition based on convolutional neural networks is described. The architecture of the software complex, some features of its construction and interaction with the cloud geo-information platform in which it functions are described. The application of the developed software for the recognition of buildings in images is described. The results of experiments on building recognition in pictures of various resolutions and types of buildings using the developed software are analysed.

scholarly journals Implementasi Jaringan Saraf Tiruan Sebagai Alat Bantu Deteksi Bakteri Staphylococcus Aureus Pada Sayuran

2020 ◽  
Vol 1 (3) ◽  
pp. 258
Author(s):  
Ali Rahmad Pohan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Staphylococcus Aureus ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Image Quality ◽  
Bacterial Detection ◽  
Image Characteristics ◽  
Reduce Image Quality ◽  
Artificial Neural

This study aims to aid bacterial detection through bacterial imagery in vegetables to help identify Staphylococcus aureus bacteria in vegetables. Input to the software is the image of bacteria in vegetables. Bacterial image is processed by grayscaling, thresholding and image segmentation processing methods so that the image characteristics that represent bacteria in vegetables are obtained. One technique that can be used as a tool to observe Staphylococcus aureus is to use artificial neural networks and combine them with image processing. Artificial neural networks function as information processing by inferring information from data that has been received and as a decision maker for data that has been studied. Image processing is the science of manipulating images, which includes techniques to improve or reduce image quality. The detection process using software that has been built can be done well. The process is carried out by matching the value of the exercise cutra backpropagation vector with the image to be detected.

scholarly journals Wound image segmentation using clustering based algorithms

2019 ◽  
Vol 2 (1) ◽  
pp. 570-578
Author(s):  
Ihor Farmaha ◽  
Marian Banaś ◽  
Vasyl Savchyn ◽  
Bohdan Lukashchuk ◽  
Taras Farmaha
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Clustering Algorithms ◽  
Human Interaction ◽  
Clustering Methods ◽  
Insufficient Amount ◽  
Improve Measurement ◽  
Artificial Neural ◽  
Model Training

Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.

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