Wszystkie pola: network techniques - Katalog OPAC zbiorów

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Tytuł:: Surface casting defects inspection using vision system and neural network techniques
Autorzy:: Świłło, S. J.
Perzyk, M.
Powiązania:: https://bibliotekanauki.pl/articles/380699.pdf
Data publikacji:: 2013
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: nondestructive testing
machined aluminum die castings
image processing algorithms
vision system inspection
neural network
badanie nieniszczące
odlewnictwo ciśnieniowe
algorytm przetwarzania obrazu
inspekcja wizyjna
sieć neuronowa
Opis:: The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.
Źródło:: Archives of Foundry Engineering; 2013, 13, 4; 103-106
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
Autorzy:: Sherif, Fatma
Mohamed, Wael A.
Mohra, A.S.
Powiązania:: https://bibliotekanauki.pl/articles/226719.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: melanoma
skin cancer
convolutional neural network
deep learning
Opis:: In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
Źródło:: International Journal of Electronics and Telecommunications; 2019, 65, 4; 597-602
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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