369 research outputs found
Edge-Aware Image Color Appearance and Difference Modeling
The perception of color is one of the most important aspects of human vision.
From an evolutionary perspective, the accurate perception of color is crucial
to distinguishing friend from foe, and food from fatal poison. As a result,
humans have developed a keen sense of color and are able to detect subtle
differences in appearance, while also robustly identifying colors across
illumination and viewing conditions. In this paper, we shall briefly review
methods for adapting traditional color appearance and difference models to
complex image stimuli, and propose mechanisms to improve their performance. In
particular, we find that applying contrast sensitivity functions and local
adaptation rules in an edge-aware manner improves image difference predictions
Modulated sparse superposition codes for the complex AWGN channel
This paper studies a generalization of sparse superposition codes (SPARCs)
for communication over the complex additive white Gaussian noise (AWGN)
channel. In a SPARC, the codebook is defined in terms of a design matrix, and
each codeword is a generated by multiplying the design matrix with a sparse
message vector. In the standard SPARC construction, information is encoded in
the locations of the non-zero entries of the message vector. In this paper we
generalize the construction and consider modulated SPARCs, where information in
encoded in both the locations and the values of the non-zero entries of the
message vector. We focus on the case where the non-zero entries take values
from a phase-shift keying (PSK) constellation. We propose a computationally
efficient approximate message passing (AMP) decoder, and obtain analytical
bounds on the state evolution parameters which predict the error performance of
the decoder. Using these bounds we show that PSK-modulated SPARCs are
asymptotically capacity achieving for the complex AWGN channel, with either
spatial coupling or power allocation. We also provide numerical simulation
results to demonstrate the error performance at finite code lengths. These
results show that introducing modulation to the SPARC design can significantly
reduce decoding complexity without sacrificing error performance
Empirical Bayes Estimators for Sparse Sequences.
The problem of estimating a high-dimensional sparse vector θ ∈ ℝ n from an observation in i.i.d. Gaussian noise is considered. An empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior, is analyzed and compared with the well-known soft-thresholding estimator using squared-error loss as a measure of performance. We obtain concentration inequalities for the Stein's unbiased risk estimate and the loss function of both estimators. Depending on the underlying θ, either the proposed empirical Bayes (eBayes) estimator or soft-thresholding may have smaller loss. We consider a hybrid estimator that attempts to pick the better of the soft-thresholding estimator and the eBayes estimator by comparing their risk estimates. It is shown that: i) the loss of the hybrid estimator concentrates on the minimum of the losses of the two competing estimators, and ii) the risk of the hybrid estimator is within order 1/√n of the minimum of the two risks. Simulation results are provided to support the theoretical results
Real-time failure-tolerant control of kinematically redundant manipulators
Includes bibliographical references.This work considers real-time fault-tolerant control of kinematically redundant manipulators to single locked-joint failures. The fault-tolerance measure used is a worst-case quantity, given by the minimum, over all single joint failures, of the minimum singular value of the post-failure Jacobians. Given any end-effector trajectory, the goal is to continuously follow this trajectory with the manipulator in configurations that maximize the fault-tolerance measure. The computation required to track these optimal configurations with brute-force methods is prohibitive for real-time implementation. We address this issue by presenting algorithms that quickly compute estimates of the worst-case fault-tolerance measure and its gradient. Real-time implementations are presented for all these techniques, and comparisons show that the performance of the best is indistinguishable from that of brute-force implementations.This work was supported by Sandia National Laboratories under contract number AL-3011
Real-time failure-tolerant control of kinematically redundant manipulators
Includes bibliographical references (pages 1115-1116).This work considers real-time fault-tolerant control of kinematically redundant manipulators to single locked-joint failures. The fault-tolerance measure used is a worst-case quantity, given by the minimum, over all single joint failures, of the minimum singular value of the post-failure Jacobians. Given any end-effector trajectory, the goal is to continuously follow this trajectory with the manipulator in configurations that maximize the fault-tolerance measure. The computation required to track these optimal configurations with brute-force methods is prohibitive for real-time implementation. We address this issue by presenting algorithms that quickly compute estimates of the worst-case fault-tolerance measure and its gradient. Comparisons show that the performance of the best method is indistinguishable from that of brute-force implementations. An example demonstrating the real-time performance of the algorithm on a commercially available seven degree-of-freedom manipulator is presented
Joint Deep Image Restoration and Unsupervised Quality Assessment
Deep learning techniques have revolutionized the fields of image restoration
and image quality assessment in recent years. While image restoration methods
typically utilize synthetically distorted training data for training, deep
quality assessment models often require expensive labeled subjective data.
However, recent studies have shown that activations of deep neural networks
trained for visual modeling tasks can also be used for perceptual quality
assessment of images. Following this intuition, we propose a novel
attention-based convolutional neural network capable of simultaneously
performing both image restoration and quality assessment. We achieve this by
training a JPEG deblocking network augmented with "quality attention" maps and
demonstrating state-of-the-art deblocking accuracy, achieving a high
correlation of predicted quality with human opinion scores.Comment: 4 Pages, 2 figures, 3 table
Correlation between bioassayed plasma levels of FK 506 and lymphocyte growth from liver transplant biopsies with histological evidence of rejection.
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