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    Wyświetlanie 1-9 z 9
    Tytuł:
    An analog linear SVM image classifier
    Autorzy:
    Festila, L.
    Szolga, L. A.
    Groza, R.
    Hintea, S.
    Cirlugea, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/385015.pdf
    Data publikacji:
    2009
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    support vector machine (SVM)
    analog multipliers
    log-domain th domain
    square-root domain
    image classifier
    Opis:
    A linear Support Vector machine classifier is proposed in this paper. In such SVM architectures based on multiplying laws the main building blocks are multipliers. We propose in this paper multiplying and weighting cells, developed by using a model consisting of a compound of two inverse non-linear functions. This procedure is suitable for VLSI implementation because it permits the use of simple nonlinearized standard log-domain or DA cells that compensate each other nonlinearities to obtain an extended domain of operation. Current-mode ELIN (externally linear internally nonlinear) design is used for its low voltage, low power and high speed characteristics. The resulted parallel-serial classifier was simulated taking into account real parameters of transistors in BICMOS technology.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 2; 82-87
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimized image feature selection using pairwise classifiers
    Autorzy:
    Bazarganigilani, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91755.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    content based image retrieval systems
    content-based image retrieval (CBIR)
    higher feature
    classifier
    Opis:
    In this paper, we introduce an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features.In our research, we improve both feature selection and classifier partition of a CBIR system. Results show great performance of our proposed algorithm.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 2; 147-153
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Could k-NN classifier be Useful in tree leaves recognition?
    Autorzy:
    Horaisová, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/229900.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    binary image
    Fourier transform
    affine invariance
    harmonic analysis
    pattern recognition
    k-NN classifier
    Opis:
    This paper presents a method for affine invariant recognition of two-dimensional binary objects based on 2D Fourier power spectrum. Such function is translation invariant and their moments of second order enable construction of affine invariant spectrum except of the rotation effect. Harmonic analysis of samples on circular paths generates Fourier coefficients whose absolute values are affine invariant descriptors. Affine invariancy is approximately saved also for large digital binary images as demonstrated in the experimental part. The proposed method is tested on artificial data set first and consequently on a large set of 2D binary digital images of tree leaves. High dimensionality of feature vectors is reduced via the kernel PCA technique with Gaussian kernel and the k-NN classifier is used for image classification. The results are summarized as k-NN classifier sensitivity after dimensionality reduction. The resulting descriptors after dimensionality reduction are able to distinguish real contours of tree leaves with acceptable classification error. The general methodology is directly applicable to any set of large binary images. All calculations were performed in the MATLAB environment.
    Źródło:
    Archives of Control Sciences; 2014, 24, 2; 177-192
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Web–based framework for breast cancer classification
    Autorzy:
    Bruździński, T.
    Krzyżak, A.
    Fevens, T.
    Jeleń, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/91866.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    breast cancer
    classification
    cytological image
    aspiration biopsy
    feature vector
    classifier
    multilayer perceptron
    segmentation algorithm
    Opis:
    The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 149-162
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The use of the image analysis method for the segregation of shredded waste in an air classifier
    Wykorzystanie metod analizy obrazu do segregacji odpadów rozdrobnionych w klasyfikatorze pneumatycznym
    Autorzy:
    Zagaj, I.
    Ulbrich, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/396474.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    segregation
    shredded waste
    air classifier
    image analysis method
    segregacja
    odpady rozdrobnione
    klasyfikator pneumatyczny
    metoda analizy obrazu
    Opis:
    Segregation of shredded waste is one of the technologies of waste management, along with issues related to manual segregation, while nowadays it starts to be associated with pneumatic classification. This paper discusses the use of an air classifier and image analysis techniques for the purposes of evaluation of segregation of shredded waste. This type of air classifier plays an important role due to the efficiency of the device and the characteristics of segregation. The authors report the results of research into image analysis of segregation of shredded waste on the basis of the use of image analysis.
    Segregacja odpadów rozdrobnionych jest jedną z technologii gospodarki odpadami, obejmującą zagadnienia związane z segregacją ręczną, która została wzbogacona o klasyfikację pneumatyczną. W niniejszym artykule przedstawiono klasyfikator pneumatyczny oraz możliwości wykorzystania metod analizy obrazu do oceny segregacji odpadów rozdrobnionych. Tego rodzaju klasyfikatory pneumatyczne mają istotne znaczenie ze względu na sprawność urządzenia, a także charakter prowadzenia segregacji. Autorzy przedstawili wyniki badań dotyczących analizy obrazu segregacji odpadów rozdrobnionych w oparciu o wykorzystanie metod analizy obrazu.
    Źródło:
    Civil and Environmental Engineering Reports; 2014, 13; 97-107
    2080-5187
    2450-8594
    Pojawia się w:
    Civil and Environmental Engineering Reports
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Two Dimensional Wavelet Transform for Classification of Power Quality Disturbances
    Autorzy:
    Mollayi, N.
    Mokhtari, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/262752.pdf
    Data publikacji:
    2014
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
    Tematy:
    power quality
    event detection and classification
    two dimensional wavelet transform
    pattern classification
    image processing
    feature
    classifier system
    Opis:
    Identification of voltage and current disturbances is an important task in power system monitoring and protection. In this paper, the application of two-dimensional wavelet transform for characterization of a wide variety range of power quality disturbances is discussed, and a new algorithm, based on image processing techniques is proposed for this purpose. A matrix is formed based on a specified number of cycles in such a way that the samples of voltage signal in each cycle form one row of that matrix. This matrix can be regarded as a two dimensional image. A two-dimensional wavelet transform is used to decompose the image into approximation and details, which contain low frequency and high frequency components along the rows and columns, respectively. Different disturbances result into different special patterns in detail images. By processing the detail images, specific features are defined which can suitably discriminate various types of disturbances. Combination of the feature generation algorithm and a classifier system leads to a smart system for classification of wide variety range of disturbances.
    Źródło:
    Electrical Power Quality and Utilisation. Journal; 2014, 17, 2; 1-7
    1896-4672
    Pojawia się w:
    Electrical Power Quality and Utilisation. Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    CellProfiler and WEKA Tools: Image Analysis for Fish Erythrocytes Shape and Machine Learning Model Algorithm Accuracy Prediction of Dataset
    Autorzy:
    Talapatra, Soumendra Nath
    Chaudhuri, Rupa
    Ghosh, Subhasis
    Powiązania:
    https://bibliotekanauki.pl/articles/1193348.pdf
    Data publikacji:
    2021
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Automatic image analysis
    CellProfiler tool
    Fish erythrocytes quantification
    Machine learning algorithm
    Model classifier accuracy
    Shapes measurement
    WEKA tool
    Opis:
    The first part of the study was detected the number of cells and measurement of shape of cells, cytoplasm, and nuclei in an image of Giemsa-stained of fish peripheral erythrocytes by using CellProfiler (CP, version 2.1.0) tool, an image analysis tool. In the second part, it was evaluated machine learning (ML) algorithm models viz. BayesNet (BN), NaiveBayes (NB), logistic regression (LR), Lazy.KStar (K*), decision tree (DT) J48, Random forest (RF) and Random tree (RT) in the WEKA tool (version 3.8.5) for the prediction of the accuracy of the dataset generated from an image. The CP predicts the numbers and individual cellular area shape (arbitrary unit) of cells, cytoplasm, and nuclei as primary, secondary, and tertiary object data in an image. The performance of model accuracy of studied ML algorithm classifications as per correctly and incorrectly classified instances, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB as per training and testing set of correctly classified instances. In case of performance accuracy of class for K value, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB while lowest values were obtained for mean absolute error (MAE) and root mean squared error (RMSE) in case of RT followed by RF, K*, LR, BN and DTJ48 and comparatively highest value in case of NB as per training and testing set. In conclusion, both tools performed well as an image to the dataset and obtained dataset to rich information through ML modelling and future study in WEKA tool can easily be analysed many biological big data to predict classifier accuracy.
    Źródło:
    World Scientific News; 2021, 154; 101-116
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Categorization of Similar Objects Using Bag of Visual Words and k - Nearest Neighbour Classifier
    Autorzy:
    Artiemjew, P.
    Górecki, P.
    Sopyła, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/298103.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    kategoryzacja obrazu
    metoda k najbliższych sąsiadów
    zbiór słów wizualnych
    Image categorization
    k-Nearest Neighbor Classifier
    Bag of Visual Words
    Opis:
    Image categorization is one of the fundamental tasks in computer vision, it has wide application in methods of artificial intelligence, robotic vision and many others. There are a lot of difficulties in computer vision to overcome, one of them appears during image recognition and classification. The difficulty arises from an image variance, which may be caused by scaling, rotation, changes in a perspective, illumination levels, or partial occlusions. Due to these reasons, the main task is to represent represent images in such way that would allow recognizing them even if they have been modified. Bag of Visual Words (BoVW) approach, which allows for describing local characteristic features of images, has recently gained much attention in the computer vision community. In this article we have presented the results of image classification with the use of BoVW and k - Nearest Neighbor classifier with different kinds of metrics and similarity measures. Additionally, the results of k - NN classification are compared with the ones obtained from a Support Vector Machine classifier.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2012, 15(2); 293-305
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applying LCS to affective image classification in spatial - frequency domain
    Autorzy:
    Lee, P. -M.
    Hsiao, T.-C.
    Powiązania:
    https://bibliotekanauki.pl/articles/91808.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    image classification
    pattern recognition
    Hilbert-Huang transform
    HHT
    empirical mode decomposition
    EMD
    Hilbert transform
    HT
    Extended Classifier Systems
    XCSs
    Area Under Curve
    AUC
    Radial-Basis Function Network
    RBF Network
    LCS
    Opis:
    Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 99-123
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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