3,469 research outputs found
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different
materials, which correlates with human similarity judgments. We first create a
database of 9,000 rendered images depicting objects with varying materials,
shape and illumination. We then gather data on perceived similarity from
crowdsourced experiments; our analysis of over 114,840 answers suggests that
indeed a shared perception of appearance similarity exists. We feed this data
to a deep learning architecture with a novel loss function, which learns a
feature space for materials that correlates with such perceived appearance
similarity. Our evaluation shows that our model outperforms existing metrics.
Last, we demonstrate several applications enabled by our metric, including
appearance-based search for material suggestions, database visualization,
clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure
Attribute-preserving gamut mapping of measured BRDFs
Reproducing the appearance of real-world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out-of-gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two-step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low-dimensional intuitive appearance space recently proposed by Serrano et al. [SGM*16], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image-based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute-preserving material editing
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction
Digital Color Imaging
This paper surveys current technology and research in the area of digital
color imaging. In order to establish the background and lay down terminology,
fundamental concepts of color perception and measurement are first presented
us-ing vector-space notation and terminology. Present-day color recording and
reproduction systems are reviewed along with the common mathematical models
used for representing these devices. Algorithms for processing color images for
display and communication are surveyed, and a forecast of research trends is
attempted. An extensive bibliography is provided
Ridge Regression Approach to Color Constancy
This thesis presents the work on color constancy and its application in the field of computer vision. Color constancy is a phenomena of representing (visualizing) the reflectance properties of the scene independent of the illumination spectrum. The motivation behind this work is two folds:The primary motivation is to seek ‘consistency and stability’ in color reproduction and algorithm performance respectively because color is used as one of the important features in many computer vision applications; therefore consistency of the color features is essential for high application success. Second motivation is to reduce ‘computational complexity’ without sacrificing the primary motivation.This work presents machine learning approach to color constancy. An empirical model is developed from the training data. Neural network and support vector machine are two prominent nonlinear learning theories. The work on support vector machine based color constancy shows its superior performance over neural networks based color constancy in terms of stability. But support vector machine is time consuming method. Alternative approach to support vectormachine, is a simple, fast and analytically solvable linear modeling technique known as ‘Ridge regression’. It learns the dependency between the surface reflectance and illumination from a presented training sample of data. Ridge regression provides answer to the two fold motivation behind this work, i.e., stable and computationally simple approach. The proposed algorithms, ‘Support vector machine’ and ‘Ridge regression’ involves three step processes: First, an input matrix constructed from the preprocessed training data set is trained toobtain a trained model. Second, test images are presented to the trained model to obtain the chromaticity estimate of the illuminants present in the testing images. Finally, linear diagonal transformation is performed to obtain the color corrected image. The results show the effectiveness of the proposed algorithms on both calibrated and uncalibrated data set in comparison to the methods discussed in literature review. Finally, thesis concludes with a complete discussion and summary on comparison between the proposed approaches and other algorithms
New software for comparing the color gamuts generated by printing technologies
In the color industry, it is vital to know the color gamut of a given device. Several tools for visualizing and comparing color gamuts are available but they each have some drawbacks. Therefore, the aim of this work was to develop and validate new software for comparing the color gamuts generated by printing devices; we also developed an automated color measurement system. The software simultaneously represents the gamuts in the 3D CIELAB space. It also calculates the Gamut Comparison Index and the volume using two algorithms (Convex Hull and Alpha Shapes). To evaluate the performance of our software, we first compared the results it obtained for the color gamuts with those from other comparison methods such as representation in the CIE 1931 chromaticity diagram or other color spaces. Next, we used Interactive Color Correction in 3 Dimensions (ICC3D) software to compare the gamut representations and volumes. Our software allowed us to identify differences between color gamuts that were not discriminated by other methods. This new software will enable the study and comparison of gamuts generated by different printing technologies and using different printing substrates, International Color Consortium profiles, inks, and light sources, thereby helping to achieve high quality color images.Optics Group (FQM151, University of Granada)University of Granada (pre-doctoral contract, Training Programme for Research Staff, FPU)Funding for open access charge: University of Granada/CBU
A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data
Investigating the pleiotropic effects of genetic variants can increase
statistical power, provide important information to achieve deep understanding
of the complex genetic structures of disease, and offer powerful tools for
designing effective treatments with fewer side effects. However, the current
multiple phenotype association analysis paradigm lacks breadth (number of
phenotypes and genetic variants jointly analyzed at the same time) and depth
(hierarchical structure of phenotype and genotypes). A key issue for high
dimensional pleiotropic analysis is to effectively extract informative internal
representation and features from high dimensional genotype and phenotype data.
To explore multiple levels of representations of genetic variants, learn their
internal patterns involved in the disease development, and overcome critical
barriers in advancing the development of novel statistical methods and
computational algorithms for genetic pleiotropic analysis, we proposed a new
framework referred to as a quadratically regularized functional CCA (QRFCCA)
for association analysis which combines three approaches: (1) quadratically
regularized matrix factorization, (2) functional data analysis and (3)
canonical correlation analysis (CCA). Large-scale simulations show that the
QRFCCA has a much higher power than that of the nine competing statistics while
retaining the appropriate type 1 errors. To further evaluate performance, the
QRFCCA and nine other statistics are applied to the whole genome sequencing
dataset from the TwinsUK study. We identify a total of 79 genes with rare
variants and 67 genes with common variants significantly associated with the 46
traits using QRFCCA. The results show that the QRFCCA substantially outperforms
the nine other statistics.Comment: 64 pages including 12 figure
A Paradigm for color gamut mapping of pictorial images
In this thesis, a paradigm was generated for color gamut mapping of pictorial images. This involved the development and testing of: 1.) a hue-corrected version of the CIELAB color space, 2.) an image-dependent sigmoidal-lightness-rescaling process, 3.) an image-gamut- based chromatic-compression process, and 4.) a gamut-expansion process. This gamut-mapping paradigm was tested against some gamut-mapping strategies published in the literature. Reproductions generated by gamut mapping in a hue-corrected CIELAB color space more accurately preserved the perceived hue of the original scenes compared to reproductions generated using the CIELAB color space. The results of three gamut-mapping experiments showed that the contrast-preserving nature of the sigmoidal-lightness-remapping strategy generated gamut-mapped reproductions that were better matches to the originals than reproductions generated using linear-lightness-compression functions. In addition, chromatic-scaling functions that compressed colors at a higher rate near the gamut surface and less near the achromatic axis produced better matches to the originals than algorithms that performed linear chroma compression throughout color space. A constrained gamut-expansion process, similar to the inverse of the best gamut-compression process found in this experiment, produced reproductions preferred over an expansion process utilizing unconstrained linear expansion
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