12,579 research outputs found
Unsupervised Learning for Color Constancy
Most digital camera pipelines use color constancy methods to reduce the
influence of illumination and camera sensor on the colors of scene objects. The
highest accuracy of color correction is obtained with learning-based color
constancy methods, but they require a significant amount of calibrated training
images with known ground-truth illumination. Such calibration is time
consuming, preferably done for each sensor individually, and therefore a major
bottleneck in acquiring high color constancy accuracy. Statistics-based methods
do not require calibrated training images, but they are less accurate. In this
paper an unsupervised learning-based method is proposed that learns its
parameter values after approximating the unknown ground-truth illumination of
the training images, thus avoiding calibration. In terms of accuracy the
proposed method outperforms all statistics-based and many learning-based
methods. An extension of the method is also proposed, which learns the needed
parameters from non-calibrated images taken with one sensor and which can then
be successfully applied to images taken with another sensor. This effectively
enables inter-camera unsupervised learning for color constancy. Additionally, a
new high quality color constancy benchmark dataset with 1707 calibrated images
is created, used for testing, and made publicly available. The results are
presented and discussed. The source code and the dataset are available at
http://www.fer.unizg.hr/ipg/resources/color_constancy/.Comment: 15 pages, 16 figure
Learning Invariant Color Features for Person Re-Identification
Matching people across multiple camera views known as person
re-identification, is a challenging problem due to the change in visual
appearance caused by varying lighting conditions. The perceived color of the
subject appears to be different with respect to illumination. Previous works
use color as it is or address these challenges by designing color spaces
focusing on a specific cue. In this paper, we propose a data driven approach
for learning color patterns from pixels sampled from images across two camera
views. The intuition behind this work is that, even though pixel values of same
color would be different across views, they should be encoded with the same
values. We model color feature generation as a learning problem by jointly
learning a linear transformation and a dictionary to encode pixel values. We
also analyze different photometric invariant color spaces. Using color as the
only cue, we compare our approach with all the photometric invariant color
spaces and show superior performance over all of them. Combining with other
learned low-level and high-level features, we obtain promising results in
ViPER, Person Re-ID 2011 and CAVIAR4REID datasets
Deep Structured-Output Regression Learning for Computational Color Constancy
Computational color constancy that requires esti- mation of illuminant colors
of images is a fundamental yet active problem in computer vision, which can be
formulated into a regression problem. To learn a robust regressor for color
constancy, obtaining meaningful imagery features and capturing latent
correlations across output variables play a vital role. In this work, we
introduce a novel deep structured-output regression learning framework to
achieve both goals simultaneously. By borrowing the power of deep convolutional
neural networks (CNN) originally designed for visual recognition, the proposed
framework can automatically discover strong features for white balancing over
different illumination conditions and learn a multi-output regressor beyond
underlying relationships between features and targets to find the complex
interdependence of dif- ferent dimensions of target variables. Experiments on
two public benchmarks demonstrate that our method achieves competitive
performance in comparison with the state-of-the-art approaches
A Novel Framework for Highlight Reflectance Transformation Imaging
We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa
Learning Perspective Undistortion of Portraits
Near-range portrait photographs often contain perspective distortion
artifacts that bias human perception and challenge both facial recognition and
reconstruction techniques. We present the first deep learning based approach to
remove such artifacts from unconstrained portraits. In contrast to the previous
state-of-the-art approach, our method handles even portraits with extreme
perspective distortion, as we avoid the inaccurate and error-prone step of
first fitting a 3D face model. Instead, we predict a distortion correction flow
map that encodes a per-pixel displacement that removes distortion artifacts
when applied to the input image. Our method also automatically infers missing
facial features, i.e. occluded ears caused by strong perspective distortion,
with coherent details. We demonstrate that our approach significantly
outperforms the previous state-of-the-art both qualitatively and
quantitatively, particularly for portraits with extreme perspective distortion
or facial expressions. We further show that our technique benefits a number of
fundamental tasks, significantly improving the accuracy of both face
recognition and 3D reconstruction and enables a novel camera calibration
technique from a single portrait. Moreover, we also build the first perspective
portrait database with a large diversity in identities, expression and poses,
which will benefit the related research in this area.Comment: 13 pages, 15 figure
The Past and the Present of the Color Checker Dataset Misuse
The pipelines of digital cameras contain a part for computational color
constancy, which aims to remove the influence of the illumination on the scene
colors. One of the best known and most widely used benchmark datasets for this
problem is the Color Checker dataset. However, due to the improper handling of
the black level in its images, this dataset has been widely misused and while
some recent publications tried to alleviate the problem, they nevertheless
erred and created additional wrong data. This paper gives a history of the
Color Checker dataset usage, it describes the origins and reasons for its
misuses, and it explains the old and new mistakes introduced in the most recent
publications that tried to handle the issue. This should, hopefully, help to
prevent similar future misuses.Comment: 5 pages, 4 figure
Mimicking the In-Camera Color Pipeline for Camera-Aware Object Compositing
We present a method for compositing virtual objects into a photograph such
that the object colors appear to have been processed by the photo's camera
imaging pipeline. Compositing in such a camera-aware manner is essential for
high realism, and it requires the color transformation in the photo's pipeline
to be inferred, which is challenging due to the inherent one-to-many mapping
that exists from a scene to a photo. To address this problem for the case of a
single photo taken from an unknown camera, we propose a dual-learning approach
in which the reverse color transformation (from the photo to the scene) is
jointly estimated. Learning of the reverse transformation is used to facilitate
learning of the forward mapping, by enforcing cycle consistency of the two
processes. We additionally employ a feature sharing schema to extract evidence
from the target photo in the reverse mapping to guide the forward color
transformation. Our dual-learning approach achieves object compositing results
that surpass those of alternative techniques
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
On Finding Gray Pixels
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 using the Dichromatic Reflection Model and is
learning-free. GI allows to estimate one or multiple illumination sources in
color-biased images. On standard single-illumination and multiple-illumination
estimation benchmarks, GI outperforms state-of-the-art statistical methods and
many recent deep methods. GI is simple and fast, written in a few dozen lines
of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab
code.Comment: appear in IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 2019. 9 pages, 7 figures. this article is an
extension of arXiv:1803.0832
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
Underwater images suffer from color distortion and low contrast, because
light is attenuated while it propagates through water. Attenuation under water
varies with wavelength, unlike terrestrial images where attenuation is assumed
to be spectrally uniform. The attenuation depends both on the water body and
the 3D structure of the scene, making color restoration difficult.
Unlike existing single underwater image enhancement techniques, our method
takes into account multiple spectral profiles of different water types. By
estimating just two additional global parameters: the attenuation ratios of the
blue-red and blue-green color channels, the problem is reduced to single image
dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an
existing library of water types. Each type leads to a different restored image
and the best result is automatically chosen based on color distribution.
We collected a dataset of images taken in different locations with varying
water properties, showing color charts in the scenes. Moreover, to obtain
ground truth, the 3D structure of the scene was calculated based on stereo
imaging. This dataset enables a quantitative evaluation of restoration
algorithms on natural images and shows the advantage of our method
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