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    Wyświetlanie 1-7 z 7
    Tytuł:
    Multi-line signal change detection for image segmentation with application in the ceramic tile industry
    Autorzy:
    Sušac, Filip
    Matić, Tomislav
    Aleksi, Ivan
    Keser, Tomislav
    Powiązania:
    https://bibliotekanauki.pl/articles/2173520.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    segmentation
    edge detection
    biscuit tile
    image processing
    visual inspection
    ceramic industry
    segmentacja
    wykrywanie krawędzi
    przetwarzanie obrazu
    oględziny
    przemysł ceramiczny
    Opis:
    In the ceramic industry, quality control is performed using visual inspection in three different product stages: green, biscuit, and the final ceramic tile. To develop a real-time computer visual inspection system, the necessary step is successful tile segmentation from its background. In this paper, a new statistical multi-line signal change detection (MLSCD) segmentation method based on signal change detection (SCD) method is presented. Through experimental results on seven different ceramic tile image sets, MLSCD performance is analyzed and compared with the SCD method. Finally, recommended parameters are proposed for optimal performance of the MLSCD method.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137121, 1--11
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-line signal change detection for image segmentation with application in the ceramic tile industry
    Autorzy:
    Sušac, Filip
    Matić, Tomislav
    Aleksi, Ivan
    Keser, Tomislav
    Powiązania:
    https://bibliotekanauki.pl/articles/2173620.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    segmentation
    edge detection
    biscuit tile
    image processing
    visual inspection
    ceramic industry
    segmentacja
    wykrywanie krawędzi
    przetwarzanie obrazu
    oględziny
    przemysł ceramiczny
    Opis:
    In the ceramic industry, quality control is performed using visual inspection in three different product stages: green, biscuit, and the final ceramic tile. To develop a real-time computer visual inspection system, the necessary step is successful tile segmentation from its background. In this paper, a new statistical multi-line signal change detection (MLSCD) segmentation method based on signal change detection (SCD) method is presented. Through experimental results on seven different ceramic tile image sets, MLSCD performance is analyzed and compared with the SCD method. Finally, recommended parameters are proposed for optimal performance of the MLSCD method.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137121
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Denoising methods for improving automatic segmentation in OCT images of human eye
    Autorzy:
    Stankiewicz, A.
    Marciniak, T.
    Dąbrowski, A.
    Stopa, M.
    Rakowicz, P.
    Marciniak, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/201133.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    optical coherence tomography (OCT)
    image denoising
    image segmentation
    anisotropic diffusion
    wavelet thresholding
    koherentna tomografia optyczna
    OCT
    segmentacja obrazu
    dyfuzja anizotropowa
    Opis:
    This paper presents analysis of selected noise reduction methods used in optical coherence tomography (OCT) retina images (the socalled B-scans). The tested algorithms include median and averaging filtering, anisotropic diffusion, soft wavelet thresholding, and multiframe wavelet thresholding. Precision of the denoising process was evaluated based on the results of automated retina layers segmentation, since this stage (vital for ophthalmic diagnosis) is strongly dependent on the image quality. Experiments were conducted with a set of 3D low quality scans obtained from 10 healthy patients and 10 patients with vitreoretinal pathologies. Influence of each method on the automatic image segmentation for both groups of patients is thoroughly described. Manual annotations of investigated retina layers provided by ophthalmology experts served as reference data for evaluation of the segmentation algorithm.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 1; 71-78
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Finger joint synovitis detection in ultrasound images
    Autorzy:
    Radlak, K.
    Frackiewicz, M.
    Palus, H.
    Smolka, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/947702.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    synovitis
    finger joint
    seeded region growing
    ultrasound imaging
    image segmentation
    obrazowanie
    ultrasonografia
    segmentacja obrazu
    Opis:
    Ultrasonography has proved its usefulness in the evaluation of joint inflammations caused by rheumatoid arthritis. The illness severity is scored by human examiners based on their experience, but some discrepancies in the final diagnosis and treatment frequently occur. Therefore, the main aim of this work is the elaboration of an automatic method of the localization of finger joint inflammation level in ultrasound images. In this paper we propose a novel, fully automated framework for synovitis region segmentation. In our approach we compare several bones and joint localization methods based on the seeded region growing technique, which is combined with different speckle noise filtering algorithms. This technique extracts a region from the image using some predefined criteria of similarity between initially selected point and the pixels in its neighborhood. The seed point is localized automatically as the darkest patch within a small region between two detected finger bones close to the joint. The region affected by synovitis is found using the adopted criterion of homogeneity based on a patch to patch similarity measure. The obtained results exhibit a satisfying accuracy in comparison with the annotations prepared by an expert and the results delivered by semi-automatic methods that require manual bones delineation.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 2; 235-245
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fast multispectral deep fusion networks
    Autorzy:
    Osin, V.
    Cichocki, A.
    Burnaev, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/200648.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multispectral imaging
    data fusion
    deep learning
    convolutional network
    object detection
    image segmentation
    obrazowanie wielospektralne
    fuzja danych
    uczenie głębokie
    sieci splotowe
    wykrywanie obiektów
    segmentacja obrazu
    Opis:
    Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 875-889
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2173573.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136750
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2128156.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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