4,122 research outputs found
Confidence driven TGV fusion
We introduce a novel model for spatially varying variational data fusion,
driven by point-wise confidence values. The proposed model allows for the joint
estimation of the data and the confidence values based on the spatial coherence
of the data. We discuss the main properties of the introduced model as well as
suitable algorithms for estimating the solution of the corresponding biconvex
minimization problem and their convergence. The performance of the proposed
model is evaluated considering the problem of depth image fusion by using both
synthetic and real data from publicly available datasets
Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case
We investigate the problem of scanning and prediction ("scandiction", for
short) of multidimensional data arrays. This problem arises in several aspects
of image and video processing, such as predictive coding, for example, where an
image is compressed by coding the error sequence resulting from scandicting it.
Thus, it is natural to ask what is the optimal method to scan and predict a
given image, what is the resulting minimum prediction loss, and whether there
exist specific scandiction schemes which are universal in some sense.
Specifically, we investigate the following problems: First, modeling the data
array as a random field, we wish to examine whether there exists a scandiction
scheme which is independent of the field's distribution, yet asymptotically
achieves the same performance as if this distribution was known. This question
is answered in the affirmative for the set of all spatially stationary random
fields and under mild conditions on the loss function. We then discuss the
scenario where a non-optimal scanning order is used, yet accompanied by an
optimal predictor, and derive bounds on the excess loss compared to optimal
scanning and prediction.
This paper is the first part of a two-part paper on sequential decision
making for multi-dimensional data. It deals with clean, noiseless data arrays.
The second part deals with noisy data arrays, namely, with the case where the
decision maker observes only a noisy version of the data, yet it is judged with
respect to the original, clean data.Comment: 46 pages, 2 figures. Revised version: title changed, section 1
revised, section 3.1 added, a few minor/technical corrections mad
Online and Offline Character Recognition Using Alignment to Prototypes
Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pairwise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., Dynamic Time Warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.Office of Naval Research (N00014-03-1-0108); National Science Foundation (IIS-0308213, EIA-0202067
FPGA-Based Portable Ultrasound Scanning System with Automatic Kidney Detection
Bedsides diagnosis using portable ultrasound scanning (PUS) offering comfortable diagnosis with various clinical advantages, in general, ultrasound scanners suffer from a poor signal-to-noise ratio, and physicians who operate the device at point-of-care may not be adequately trained to perform high level diagnosis. Such scenarios can be eradicated by incorporating ambient intelligence in PUS. In this paper, we propose an architecture for a PUS system, whose abilities include automated kidney detection in real time. Automated kidney detection is performed by training the Viola–Jones algorithm with a good set of kidney data consisting of diversified shapes and sizes. It is observed that the kidney detection algorithm delivers very good performance in terms of detection accuracy. The proposed PUS with kidney detection algorithm is implemented on a single Xilinx Kintex-7 FPGA, integrated with a Raspberry Pi ARM processor running at 900 MHz
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponential
family distributions whose natural parameter resides in a reproducing kernel
Hilbert space. Key to our approach is a novel technique, doubly dual embedding,
that avoids computation of the partition function. This technique also allows
the development of a flexible sampling strategy that amortizes the cost of
Monte-Carlo sampling in the inference stage. The resulting estimator can be
easily generalized to kernel conditional exponential families. We establish a
connection between kernel exponential family estimation and MMD-GANs, revealing
a new perspective for understanding GANs. Compared to the score matching based
estimators, the proposed method improves both memory and time efficiency while
enjoying stronger statistical properties, such as fully capturing smoothness in
its statistical convergence rate while the score matching estimator appears to
saturate. Finally, we show that the proposed estimator empirically outperforms
state-of-the-artComment: 22 pages, 20 figures; AISTATS 201
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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