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    Wyświetlanie 1-6 z 6
    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:
    Detection of driver fatigue symptoms using transfer learning
    Autorzy:
    Jakubowski, J.
    Chmielińska, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/201238.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    driver fatigue
    convolutional neural networks
    transfer learning
    AlexNet
    zmęczenie kierowcy
    splotowe sieci neuronowe
    Opis:
    This paper presents the results of the scientific investigations which aimed at developing the detectors of the selected driver fatigue symptoms based on face images. The presented approach assumed using convolutional neural networks and transfer learning technique. In the conducted research the pretrained model of AlexNet was used. The net underwent slight modification of the structure and then the fine-tuning procedure was applied with the use of an appropriate dataset. In this way all detectors of the selected fatigue symptoms were created. The results of conducted computations indicate that it is potentially possible to apply such an approach to the problem of fatigue symptom detection. The values of the overall misclassification rates for the most troublesome symptom are less than 5.5%, which seems to be a quite satisfactory result.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 869-874
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speeding-up convolutional neural networks: A survey
    Autorzy:
    Lebedev, V.
    Lempitsky, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/201708.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolutional neural networks
    resource-efficient computation
    algorithm optimization
    splotowe sieci neuronowe
    efektywne zasoby obliczeniowe
    optymalizacja algorytmu
    Opis:
    Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 799-811
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of convolutional neural networks with anatomical knowledge for brain MRI analysis in MS patients
    Autorzy:
    Stasiak, B.
    Tarasiuk, P.
    Michalska, I.
    Tomczyk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200542.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multiple sclerosis
    convolutional neural networks
    skull stripping
    ventricular system
    stwardnienie rozsiane
    splotowe sieci neuronowe
    system komorowy
    Opis:
    In this paper we consider the problem of automatic localization of multiple sclerosis (MS) lesions within brain tissue. We use a machine learning approach based on a convolutional neural network (CNN) which is trained to recognize the lesions in magnetic resonance images (MRI scans) of the patient’s brain. The training images are relatively small fragments clipped from the MRI scans so – in order to provide additional hints on location of a given clip within the brain structures – we include anatomical information in the training/testing process. Our research has shown that indicating the location of the ventricles and other structures, as well as performing brain tissue classification may enhance the results of the automatic localization of the MS-related demyelinating plaques in the MRI scans.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 857-868
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning versus classical neural approach to mammogram recognition
    Autorzy:
    Kurek, J.
    Świderski, B.
    Osowski, S.
    Kruk, M.
    Barhoumi, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/200919.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    convolutional neural networks
    breast cancer diagnosis
    mammogram recognition
    diagnostic features
    splotowe sieci neuronowe
    diagnostyka raka piersi
    rozpoznawanie
    mammografia
    cechy diagnostyczne
    Opis:
    Automatic recognition of mammographic images in breast cancer is a complex issue due to the confusing appearance of some perfectly normal tissues which look like masses. The existing computer-aided systems suffer from non-satisfactory accuracy of cancer detection. This paper addresses this problem and proposes two alternative techniques of mammogram recognition: the application of a variety of methods for definition of numerical image descriptors in combination with an efficient SVM classifier (so-called classical approach) and application of deep learning in the form of convolutional neural networks, enhanced with additional transformations of input mammographic images. The key point of the first approach is defining the proper numerical image descriptors and selecting the set which is the most class discriminative. To achieve better performance of the classifier, many image descriptors were defined by means of applying different characterization of the images: Hilbert curve representation, Kolmogorov-Smirnov statistics, the maximum subregion principle, percolation theory, fractal texture descriptors as well as application of wavelet and wavelet packets. Thanks to them, better description of the basic image properties has been obtained. In the case of deep learning, the features are automatically extracted as part of convolutional neural network learning. To get better quality of results, additional representations of mammograms, in the form of nonnegative matrix factorization and the self-similarity principle, have been proposed. The methods applied were evaluated based on a large database composed of 10,168 regions of interest in mammographic images taken from the DDSM database. Experimental results prove the advantage of deep learning over traditional approach to image recognition. Our best average accuracy in recognizing abnormal cases (malignant plus benign versus healthy) was 85.83%, with sensitivity of 82.82%, specificity of 86.59% and AUC = 0.919. These results are among the best for this massive database.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 831-840
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition
    Autorzy:
    Szmurło, Robert
    Osowski, Stanisław
    Powiązania:
    https://bibliotekanauki.pl/articles/2173680.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    CNN
    ensemble of classifiers
    face recognition
    feature selection
    convolutional neural networks
    splotowe sieci neuronowe
    zespół klasyfikatorów
    rozpoznawanie twarzy
    wybór funkcji
    Opis:
    The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141004
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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