9,345 research outputs found
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Pooling is a ubiquitous operation in image processing algorithms that allows
for higher-level processes to collect relevant low-level features from a region
of interest. Currently, max-pooling is one of the most commonly used operators
in the computational literature. However, it can lack robustness to outliers
due to the fact that it relies merely on the peak of a function. Pooling
mechanisms are also present in the primate visual cortex where neurons of
higher cortical areas pool signals from lower ones. The receptive fields of
these neurons have been shown to vary according to the contrast by aggregating
signals over a larger region in the presence of low contrast stimuli. We
hypothesise that this contrast-variant-pooling mechanism can address some of
the shortcomings of max-pooling. We modelled this contrast variation through a
histogram clipping in which the percentage of pooled signal is inversely
proportional to the local contrast of an image. We tested our hypothesis by
applying it to the phenomenon of colour constancy where a number of popular
algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and
Double-Opponency). For each of these methods, we investigated the consequences
of replacing their original max-pooling by the proposed
contrast-variant-pooling. Our experiments on three colour constancy benchmark
datasets suggest that previous results can significantly improve by adopting a
contrast-variant-pooling mechanism
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
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
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
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