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	Provedor de dados Agronomy (1) BABT (1) Ciência e Agrotecnologia (1) Autor Acerbi Júnior,Fausto Weimar (1) Bueno,Inácio Thomaz (1) Ganganagowdar, Narendra Veranagouda (1) Haji,Salih Omer (1) Mello,José Márcio de (1) Siddaramappa, Hareesha Katiganere (1) Silveira,Eduarda Martiniano de Oliveira (1) Yousif,Raghad Zuhair (1) Mais... Palavra-chave Feature extraction (3) Artificial neural networks (1) Classification (1) Computer Science and Engineering (1) Computer Vision (1) Computer-aided iagnosis (1) Geostatistics (1) Image Processing (1) Remote sensing (1) Run-Length Matrix (1) SVM and DT classifiers (1) Soft Computing White Wholes (WW) grade cashew kernel images (1) Thyroid Nodule (1) Mais... Tipo do documento Info:eu-repo/semantics/article (3) Ano 2019 (1) 2017 (1) 2016 (1) País Brazil (3) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy BABT Haji,Salih Omer; Yousif,Raghad Zuhair. Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which... Tipo: Info:eu-repo/semantics/article Palavras-chave: Thyroid Nodule; SVM and DT classifiers; Computer-aided iagnosis; Feature extraction; Run-Length Matrix. Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132019000100608 Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil Ciência e Agrotecnologia Silveira,Eduarda Martiniano de Oliveira; Acerbi Júnior,Fausto Weimar; Mello,José Márcio de; Bueno,Inácio Thomaz. ABSTRACT Object-based change detection is a powerful analysis tool for remote sensing data, but few studies consider the potential of temporal semivariogram indices for mapping land-cover changes using object-based approaches. In this study, we explored and evaluated the performance of semivariogram indices calculated from remote sensing imagery, using the Normalized Differential Vegetation Index (NDVI) to detect changes in spatial features related to land cover caused by a disastrous 2015 dam failure in Brazil’s Mariana district. We calculated the NDVI from Landsat 8 images acquired before and after the disaster, then created objects by multiresolution segmentation analysis based on post-disaster images. Experimental semivariograms were computed within... Tipo: Info:eu-repo/semantics/article Palavras-chave: Remote sensing; Geostatistics; Feature extraction. Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542017000500554 Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks Agronomy Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.  A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.  Tipo: Info:eu-repo/semantics/article Palavras-chave: Computer Science and Engineering; Computer Vision; Image Processing; Soft Computing White Wholes (WW) grade cashew kernel images; Feature extraction; Artificial neural networks; Classification. Ano: 2016 URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861 Registros recuperados: 3 Primeira ... 1 ... Última

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