Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "SIFT" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-6 z 6
    Tytuł:
    PCA based modification of SIFT-like methods for object class recognition
    Autorzy:
    Owczarek, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/115449.pdf
    Data publikacji:
    2011
    Wydawca:
    Fundacja na Rzecz Młodych Naukowców
    Tematy:
    object recognition
    principle component analysis
    SIFT
    Opis:
    This paper discusses a novel PCA based modification of standard SIFT and PCA-SIFT algorithms for the purpose of object class recognition. New descriptors intended to be simultaneously distinctive enough to describe the difference between features belonging to separate categories and general enough to capture the variations among features from the same class are proposed. The experimental results, gained for a test database, showing the reliability of introduced approach are presented.
    Źródło:
    Challenges of Modern Technology; 2011, 2, 1; 23-26
    2082-2863
    2353-4419
    Pojawia się w:
    Challenges of Modern Technology
    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:
    Identification of Garbage in the River Based on The YOLO Algorithm
    Autorzy:
    Suprapto, Bhakti Yudho
    Kelvin
    Kurniawan, Muhammad Arief
    Ardela, Muhammad Kevin
    Hikmarika, Hera
    Husin, Zainal
    Dwijayanti, Suci
    Powiązania:
    https://bibliotekanauki.pl/articles/2055273.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    control
    identification
    HSV and sift method
    USV
    yolo algorithm
    Opis:
    This paper discusses the identification of garbage using the YOLO algorithm. In the rivers, it is usually difficult to distinguish between garbage and plants, especially when it is done in real-time and at the time of too much light. Therefore, there is a need of an appropriate method. The HSV and SIFT methods were used as preliminary tests. The tests were quite successful even in close condition, however, there were still many problems faced in using this method since it is only based on pixel and shape readings. Meanwhile, YOLO algorithm was able to identify garbage and water hyacinth even though they were closed to each other.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 4; 727--733
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution
    Autorzy:
    Benlakhdar, Said
    Rziza, Mohammed
    Thami, Rachid Oulad Haj
    Powiązania:
    https://bibliotekanauki.pl/articles/29520269.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    statistical image modeling
    SIFT
    mixture gamma distribution
    uniform discrete curvelet transform
    classification
    Opis:
    Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in wavelet domain to describe textured images and rely on number of principles which give its internal coherence and originality. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets, show that the proposed algorithm has a good relevance as it provides higher classification accuracy compared to some other well known models see (Kohavi, 1995). Moreover, it shows other advantages relied to Noise-resistant and rotation invariant.
    Źródło:
    Computer Methods in Materials Science; 2020, 20, 3; 95-106
    2720-4081
    2720-3948
    Pojawia się w:
    Computer Methods in Materials Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of animals to determine the migration potential at the construction of new infrastructure
    Autorzy:
    Matuska, S.
    Hudec, R.
    Benco, M.
    Zachariasova, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/393291.pdf
    Data publikacji:
    2013
    Wydawca:
    Polskie Stowarzyszenie Telematyki Transportu
    Tematy:
    SIFT
    SURF descriptor
    SVM classifier
    animal classification
    deskryptor SURF
    klasyfikator SVM
    klasyfikacja zwierząt
    Opis:
    At the planning and construction of new infrastructures, the information about migration potential of animals in a target area is needed. This information will be used to design of migration corridors for wild animals. To determine the migration potential of animals based on distributed video camera system, new methods for object recognition and classification are developed. In general, an object recognition system consists of three steps, namely, the image feature extraction from the training database, training the classifier and evaluation of query image of object/animal. In this paper, an extraction of local key point by SIFT or SURF descriptors, bags of key points method in combination with SVM classifier and two hybrid key points detection methods are proposed in detail.
    Źródło:
    Archives of Transport System Telematics; 2013, 6, 4; 26-30
    1899-8208
    Pojawia się w:
    Archives of Transport System Telematics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Vision analysis system for autonomous landing of micro drone
    Autorzy:
    Skoczylas, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/386669.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    unmanned aerial vehicle
    micro drone
    image analysis
    CCD camera
    keypoints descriptors
    SIFT
    SURF
    BRISK
    object tracking
    CAMSHIFT
    bezzałogowy obiekt latający
    dron
    analiza obrazów
    śledzenie obiektu
    Opis:
    This article describes a concept of an autonomous landing system of UAV (Unmanned Aerial Vehicle). This type of device is equipped with the functionality of FPV observation (First Person View) and radio broadcasting of video or image data. The problem is performance of a system of autonomous drone landing in an area with dimensions of 1m × 1m, based on CCD camera coupled with an image transmission system connected to a base station. Captured images are scanned and landing marker is detected. For this purpose, image features detectors (such as SIFT, SURF or BRISK) are utilized to create a database of keypoints of the landing marker and in a new image keypoints are found using the same feature detector. In this paper results of a framework that allows detection of definedmarker for the purpose of drone landing field positioning will be presented.
    Źródło:
    Acta Mechanica et Automatica; 2014, 8, 4; 199-203
    1898-4088
    2300-5319
    Pojawia się w:
    Acta Mechanica et Automatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies