Deep multiresolution color constancy

Caglar Aytekin, Jarno Nikkanen, Moncef Gabbouj

    Tutkimustuotos: KonferenssiartikkeliTieteellinenvertaisarvioitu

    3 Sitaatiot (Scopus)

    Abstrakti

    In this paper, a computational color constancy method is proposed via estimating the illuminant chromaticity in a scene by pooling from many local estimates. To this end, first, for each image in a dataset, we form an image pyramid consisting of several scales of the original image. Next, local patches of certain size are extracted from each scale in this image pyramid. Then, a convolutional neural network is trained to estimate the illuminant chromaticity per-patch. Finally, two more consecutive trainings are conducted, where the estimation is made per-image via taking the mean (1st training) and median (2nd training) of local estimates. The proposed method is shown to outperform the state-of-the-art in a widely used color constancy dataset.

    AlkuperäiskieliEnglanti
    Otsikko2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
    KustantajaIEEE COMPUTER SOCIETY PRESS
    Sivut3735-3739
    Sivumäärä5
    ISBN (elektroninen)9781509021758
    DOI - pysyväislinkit
    TilaJulkaistu - 20 helmik. 2018
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING -
    Kesto: 1 tammik. 1900 → …

    Julkaisusarja

    Nimi
    ISSN (elektroninen)2381-8549

    Conference

    ConferenceIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
    Ajanjakso1/01/00 → …

    Julkaisufoorumi-taso

    • Jufo-taso 1

    !!ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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