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Deep multiresolution color constancy

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

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 helmikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2381-8549

Conference

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

Tiivistelmä

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.

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