7 papers
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore…
Probabilistic Color Constancy
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +3
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene…
INTEL-TAU: A Color Constancy Dataset
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high…
Bag of Color Features For Color Constancy
Firas Laakom, Nikolaos Passalis, Jenni Raitoharju +4
In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces th…
Color Constancy Convolutional Autoencoder
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional auto…
On Finding Gray Pixels
Yanlin Qian, Joni-Kristian Kämäräinen, Jarno Nikkanen +1
We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived u…