1,498 research outputs found
Convolutional Color Constancy
Color constancy is the problem of inferring the color of the light that
illuminated a scene, usually so that the illumination color can be removed.
Because this problem is underconstrained, it is often solved by modeling the
statistical regularities of the colors of natural objects and illumination. In
contrast, in this paper we reformulate the problem of color constancy as a 2D
spatial localization task in a log-chrominance space, thereby allowing us to
apply techniques from object detection and structured prediction to the color
constancy problem. By directly learning how to discriminate between correctly
white-balanced images and poorly white-balanced images, our model is able to
improve performance on standard benchmarks by nearly 40%
Color Constancy Convolutional Autoencoder
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 autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
Color Cerberus
Simple convolutional neural network was able to win ISISPA color constancy
competition. Partial reimplementation of (Bianco, 2017) neural architecture
would have shown even better results in this setup
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