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    Wyświetlanie 1-5 z 5
    Tytuł:
    Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy
    Autorzy:
    Mishra, Akshansh
    Powiązania:
    https://bibliotekanauki.pl/articles/95297.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    local binary patterns
    friction stir welding
    machine learning
    surface defects
    lokalne wzorce binarne
    zgrzewanie tarciowe z mieszaniem materiału
    zgrzewanie tarciowe z przemieszaniem
    FSW
    uczenie maszynowe
    wady powierzchni
    Opis:
    The research reported in this paper focuses on the application of local binary patterns (LBPs) for surface defects detection. The surface defection detection algorithm for friction stir welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the local binary patterns method can be used for real-time surface defects detection in friction stir welded joints.
    Źródło:
    Journal of Mechanical and Energy Engineering; 2020, 4, 1; 27-32
    2544-0780
    2544-1671
    Pojawia się w:
    Journal of Mechanical and Energy Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Texture and gene expression analysis of the MRI brain in detection of Alzheimer’s disease
    Autorzy:
    Bustamam, A.
    Sarwinda, D.
    Ardenaswari, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91834.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    Alzheimer’s disease
    MRI
    Feature Extraction
    Bi-Clustering
    Local Binary Pattern
    LBP
    Opis:
    Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 111-120
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rgb-D face recognition using LBP-DCT algorithm
    Autorzy:
    Kumar, Sunil B L
    Kumari, Sharmila M
    Powiązania:
    https://bibliotekanauki.pl/articles/1956066.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    RGB-D
    kinect
    local binary pattern
    pattern recognition
    feature extraction
    histogram
    face recognition
    lokalny wzorzec binarny
    rozpoznawanie wzorców
    wyodrębnianie cech
    rozpoznawanie twarzy
    Opis:
    Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment
    Źródło:
    Applied Computer Science; 2021, 17, 3; 73-81
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Robust content-based image retrieval using ICCV, GLCM, and DWT-MSLBP descriptors
    Autorzy:
    Chavda, Sagar
    Goyani, Mahesh
    Powiązania:
    https://bibliotekanauki.pl/articles/27312841.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    content-based image retrieval
    improved color coherence vector
    gray-level co-occurrence matrix
    discrete wavelet transform
    multi-scale local binary pattern
    principal component analysis
    linear discriminant analysis
    Opis:
    Content-based image retrieval (CBIR) retrieves visually similar images from a dataset based on a specified query. A CBIR system measures the similarities between a query and the image contents in a dataset and ranks the dataset images. This work presents a novel framework for retrieving similar images based on color and texture features. We have computed color features with an improved color coherence vector (ICCV) and texture features with a gray-level co-occurrence matrix (GLCM) along with DWT-MSLBP (which is derived from applying a modified multi-scale local binary pattern [MS-LBP] over a discrete wavelet transform [DWT], resulting in powerful textural features). The optimal features are computed with the help of principal component analysis (PCA) and linear discriminant analysis (LDA). The proposed work uses a variancebased approach for choosing the number of principal components/eigenvectors in PCA. PCA with a 99.99% variance preserves healthy features, and LDA selects robust ones from the set of features. The proposed method was tested on four benchmark datasets with Euclidean and city-block distances. The proposed method outshines all of the identified state-of-the-art literature methods.
    Źródło:
    Computer Science; 2022, 23 (1); 5--36
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrid texture and gradient modeling for dynamic background subtraction identification systemin tobacco plant using 5G data service
    Autorzy:
    Gowda Thirthe, M.T.
    Chandrika, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/38699145.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    background subtraction
    local binary pattern
    tobacco plant
    texture
    Gaussian mixture model
    illumination change
    plant disease identification system
    usuwanie tła
    lokalny wzorzec binarny
    tytoń
    tekstura
    model mieszaniny Gaussa
    zmiana oświetlenia
    system identyfikacji chorób roślin
    Opis:
    Background: Detecting the plants as objects of interest in any vision-based input sequence is highly complex due to nonlinear background objects such as rocks, shadows,etc. Therefore, it is a difficult task and an emerging one with the development of precision agriculture systems. The nonlinear variations of pixel intensity with illuminationand other causes such as blurs and poor video quality also make the object detection taskchallenging. To detect the object of interest, background subtraction (BS) is widely usedin many plant disease identification systems, and its detection rate largely depends on thenumber of features used to suppress and isolate the foreground region and its sensitivitytoward image nonlinearity. Methodology: A hybrid invariant texture and color gradient-based approach is proposed to model the background for dynamic BS, and its performance is validated byvarious real-time video captures covering different kinds of complex backgrounds and various illumination changes. Based on the experimental results, a simple multimodal featureattribute, which includes several invariant texture measures and color attributes, yieldsfinite precision accuracy compared with other state-of-art detection methods. Experimental evaluation of two datasets shows that the new model achieves superior performanceover existing results in spectral-domain disease identification model. 5G assistance: After successful identification of tobacco plant and its analysis, the finalresults are stored in a cloud-assisted server as a database that allows all kinds of 5G servicessuch as IoT and edge computing terminals for data access with valid authentication fordetailed analysis and references.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 41-54
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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