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Wyszukujesz frazę "image detection" wg kryterium: Temat

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    Wyświetlanie 1-9 z 9
    Tytuł:
    LEDs based video camera pose estimation
    Autorzy:
    Sudars, K.
    Cacurs, R.
    Homjakovs, I.
    Judvaitis, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/200249.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    camera pose estimation
    image keypoint detection and matching
    3D point reconstruction
    object localization and tracking
    oszacowanie ustawienia kamery
    rekonstrukcja modelu 3D
    lokalizacja obiektu
    śledzenie obiektu
    Opis:
    For 3D object localization and tracking with multiple cameras the camera poses have to be known within a high precision. The paper evaluates camera pose estimation via a fundamental matrix and via the known object in environment of multiple static cameras. A special feature point extraction technique based on LED (Light Emitting Diodes) point detection and matching has been developed for this purpose. LED point detection has been solved searching local maximums in images and LED point matching has been solved involving patterned time functions for each light source. Emitting LEDs have been used as sources of known reference points instead of typically used feature point extractors like ORB, SIFT, SURF etc. In such a way the robustness of pose estimation has been obtained. Camera pose estimation is significant for object localization using the networks with multiple cameras which are going to an play increasingly important role in modern Smart Cities environments.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 4; 897-905
    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/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:
    U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images
    Autorzy:
    Les, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2173575.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    kidney detection
    medical image processing
    U-net
    frames partitioning
    volumetric analysis
    wykrywanie nerek
    przetwarzanie obrazu medycznego
    partycjonowanie ramek
    analiza objętościowa
    Opis:
    This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137051
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images
    Autorzy:
    Les, Tomasz
    Powiązania:
    https://bibliotekanauki.pl/articles/2090740.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    kidney detection
    medical image processing
    U-net
    frames partitioning
    volumetric analysis
    wykrywanie nerek
    przetwarzanie obrazu medycznego
    partycjonowanie ramek
    analiza objętościowa
    Opis:
    This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137051, 1--9
    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
    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:
    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:
    Coda wave interferometry for monitoring the fracture process of concrete beams under bending test
    Autorzy:
    Knak, Magdalena
    Wojtczak, Erwin
    Rucka, Magdalena
    Powiązania:
    https://bibliotekanauki.pl/articles/27311414.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    non-destructive testing
    concrete beams
    crack detection
    fracture process
    ultrasonic waves
    coda wave interferometry
    digital image correlation
    badania nieniszczące
    wykrywanie pęknięć
    proces pękania
    fale ultradźwiękowe
    interferometria fal koda
    cyfrowa korelacja obrazów
    belka betonowa
    Opis:
    Early detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially when it comes to using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were performed on concrete beams subjected to mechanical degradation in a 3-point bending test. Ultrasonic waves were registered on a specimen surface by piezoelectric transducers located at several points. Then, the signals were processed taking advantage of wave scattering due to micro-crack disturbances. For early-stage damage detection, coda wave interferometry was used. The novelty of the work concerns the application of the complex decorrelation matrix and the moving reference trace approach for better distinguishment of sensors located in different parts of a crack zone. To enhance coda wave-based damage identification results, optical imaging of crack development was performed by means of digital image correlation measurement. The results obtained showed that the coda wave interferometry technique can be successfully used as a quantitative measure of changes in the structure of concrete. The results also indicated that the course of decorrelation coefficient curves enabled the identification of three stages during degradation, and it depended on the location of acquisition points versus the crack zone.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e144118
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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