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    Wyświetlanie 1-6 z 6
    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:
    IMAGE PATTERN ANALYSIS WITH IMAGE POTENTIAL TRANSFORM
    Autorzy:
    Oleg, Butusov
    Dikusar, Vasily
    Powiązania:
    https://bibliotekanauki.pl/articles/452838.pdf
    Data publikacji:
    2018
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
    Tematy:
    binary image transform
    distance and potential transform
    statistical indices
    geometric signatures
    pattern analysis
    pattern recognition
    Opis:
    Pattern analysis with image transform based on potential calculation was considered. Initial gray-scale image is sliced into equidistant levels and resulting binary image was prepared by joining of some levels to one binary image. Binary image was transformed under assumption that white pixels in it may be considered as electric charges or spins. Using this assumption Ising model and Coulomb model interaction between white pixels was used for image potential transform. The transform was calculated using moving window. The resulting gray-scale image was again transformed to binary image using the thresholding on 0.5 level. Further binary images were analyzed using statistical indices (average, standard deviation, skewness, kurtosis) and geometric signatures: area, eccentricity, Euler number, orientation and perimeter. It was found that the most suitable geometric signature for pattern configuration analysis of Ising potential transform (IPT) and Coulomb potential transform (CPT) is area value. Similarly the most suitable statistics is distance statistics between white pixels.
    Źródło:
    Metody Ilościowe w Badaniach Ekonomicznych; 2018, 19, 1; 12-27
    2082-792X
    Pojawia się w:
    Metody Ilościowe w Badaniach Ekonomicznych
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A binary representation for real-valued, local feature descriptors
    Autorzy:
    Oszust, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/384335.pdf
    Data publikacji:
    2017
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    SIFT
    SURF
    LDAHash
    binary tests
    image matching
    image recognition
    Opis:
    The usage of real-valued, local descriptors in computer vision applications is ofen constrained by their large memory requirements and long matching time. Typical approaches to the reduction of their vectors map the descriptor space to the Hamming space in which the obtained binary strings can be efficiently stored and compared. In contrary to such techniques, the approach proposed in this paper does not require a data-driven binarisation process, but can be seen as an extension of the floating-point descriptor computation pipeline with a step that allows turning it into a binary descriptor. In this step, binary tests are performed on values determined for pixel blocks from the described image patch. In the paper, the proposed approach is described and applied to two popular real-valued descriptors, SIFT and SURF. The paper also contains a comparison of the approach with state-of-the-art binarisation techniques and popular binary descriptors. The results demonstrate that the proposed representation for real-valued descriptors outperforms other methods on four demanding benchmark image datasets.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2017, 11, 1; 3-9
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Content-based image retrieval using a signature graph and a self-organizing map
    Autorzy:
    Van, T. T.
    Le, T. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/330159.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    binary signature
    similarity measure
    signature graph
    image retrieval
    sygnatura binarna
    miara podobieństwa
    wyszukiwanie obrazu
    Opis:
    In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (content-based image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 2; 423-438
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The application of decision binary trees to assess the usefulness of the digital terrain model in studying the relationships between relief and vegetation in the Polish High Tatra
    Autorzy:
    Kącki, Karol
    Powiązania:
    https://bibliotekanauki.pl/articles/2030343.pdf
    Data publikacji:
    2006-06-01
    Wydawca:
    Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
    Tematy:
    relief - vegetation relationship
    Decision Binary Trees (DBT)
    Digital Terrain Model (DTM)
    Ikonos XS
    image classification
    geoinformation
    Opis:
    The relationships between individual components of the natural environment have long been an object of research (Kostrowicki, Wójcik, 1972; Rączkowska, Kozłowska, 1994; Kozłowska, Rączkowska, 1996). This paper is an attempt to analyse the relationships between two geocomponents of the natural environment: relief and vegetation, from a perspective contrary to the one currently prevailing in the literature of the subject. This approach assumes that relief, with its dominant role as a component strongly affecting the formation of the remaining factors, can be indicative in character and as such can represent basie factors that help determine and anticipate the occurrences of certain plant communities as well as locations with no vegetation. Using geoinformation data along with the tools to process them, an attempt was made to assess the usefulness of the DTM (Digital Terrain Model) to identify selected plant communities, rock and water. The development of a model of the relationships between the relief and the vegetation is an attempt to capture the correspondence between the parameters characterising the relief, calcułated using the DTM model and classes of objects, with the use of information obtained from an Ikonos XS image. This model was subseąuently used to draw a map o f the land cover for a part of the Gąsienicowa Valley in the High Tatra (Dolina Gąsienicowa). For the purpose of this exercise, a techniąue of data classification called DBT (Decision Binary Trees) was used.
    Źródło:
    Miscellanea Geographica. Regional Studies on Development; 2006, 12; 305-313
    0867-6046
    2084-6118
    Pojawia się w:
    Miscellanea Geographica. Regional Studies on Development
    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
      Wyświetlanie 1-6 z 6

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