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Wyszukujesz frazę "Edge detection" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Salt and pepper noise reduction and edge detection algorithm based on neutrosophic logic
Autorzy:
Arulpandy, P.
Trinita Pricilla, M.
Powiązania:
https://bibliotekanauki.pl/articles/1839286.pdf
Data publikacji:
2020
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
neutrosophic set
digital image processing
image analysis
image denoising
edge detection
Opis:
Neutrosophic set (NS) is a powerful tool to deal with indeterminacy. In this paper, the neutrosophic set is applied to the image domain and a novel edge detection technique is proposed. Noise reduction of images is a challenging task in image processing. Salt and pepper noise is one kind of noise that affects a grayscale image significantly. Generally, the median filter is used to reduce salt and pepper noise; it gives optimum results while compared to other image filters. Median filter works only up to a certain level of noise intensity. Here we proposed a neighborhood-based image filter called nbd-filter, it works perfectly for gray image regardless of noise intensity. It reduces salt and pepper noise significantly at any noise level and produces a noise-free image. Further, we proposed an edge detection algorithm based on the neutrosophic set, it detects edges efficiently for images corrupted by noise and noise-free images. Since most of the real-life images consists of indeterminate regions, neutrosophy is a perfect tool for edge detection. The main advantage of the proposed edge detector is, it is a simple and efficient technique and detect edges more efficient than conventional edge detectors.
Źródło:
Computer Science; 2020, 21 (2); 179-195
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network
Autorzy:
Ashafuddula
Islam, Rafiqul
Powiązania:
https://bibliotekanauki.pl/articles/27312851.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
melanoma detection
medical imaging
image classification
convolutional neuralnetwork
intensity value estimation
canny edge detection
Opis:
Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), Sensitivity (93.76%), Specificity (91.56%), and Precision (90.68%) are achieved.
Źródło:
Computer Science; 2023, 24 (3); 277--296
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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