13,656 research outputs found
Mixed Pooling Neural Networks for Color Constancy
International audienceColor constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of these approaches are handcrafted and mostly rely on strong empirical assumptions, e.g., that the average reflectance in a scene is gray. State-of-the-art approaches can perform very well on some given datasets but poorly adapt on some others. In this paper, we have investigated how neural networks-based approaches can be used to deal with the color constancy problem. We have proposed a new network architecture based on existing successful hand-crafted approaches and a large number of improvements to tackle this problem by learning a suitable deep model. We show our results on most of the standard benchmarks used in the color constancy domain
Skin color correction via convolutional neural networks in 3D fringe projection profilometry
Fringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquired either by projecting a white image or a black image and relying on ambient light. Color Constancy is essential for a faithful digital record, although the optical properties of biological tissue make color reproducibility challenging. Furthermore, color perception is highly dependent on the illuminant. Here, we describe a deep learning-based method for skin color correction in FPP. We trained a convolutional neural network using a skin tone color palette acquired under different illumination conditions to learn the mapping relationship between the input color image and its counterpart in the sRGB color space. Preliminary experimental results demonstrate the potential for this approach
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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