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Optimal Bit Allocation Among Dependent Quantizers For The Minimum Maximum Distortion Criterion
 Proc. of ICASSP
, 1997
"... for dependent quantizers for the minimum maximum distortion criterion. First we show how minimizing the bit rate for a given maximum distortion can be achieved in a dependent coding framework using dynamic programming #DP#. Then we employ an iterative algorithm to minimize the maximum distortion for ..."
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Cited by 3 (0 self)
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for dependent quantizers for the minimum maximum distortion criterion. First we show how minimizing the bit rate for a given maximum distortion can be achieved in a dependent coding framework using dynamic programming #DP#. Then we employ an iterative algorithm to minimize the maximum distortion
Zeroerror source coding with maximum distortion criterion
 Proc. Data Compression Conference 2002
"... Let finite source and reproduction alphabets X and Y and a distortion measure d: X×Y→[0, ∞) be given. We study the minimum asymptotic rate required to describe a source distributed over X within a (given) distortion threshold D at every sample. The problem is hence a minmax problem, and the distort ..."
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Cited by 3 (1 self)
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Let finite source and reproduction alphabets X and Y and a distortion measure d: X×Y→[0, ∞) be given. We study the minimum asymptotic rate required to describe a source distributed over X within a (given) distortion threshold D at every sample. The problem is hence a minmax problem
The information bottleneck method
, 1999
"... We define the relevant information in a signal x ∈ X as being the information that this signal provides about another signal y ∈ Y. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. ..."
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Cited by 540 (35 self)
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. Understanding the signal x requires more than just predicting y, it also requires specifying which features of X play a role in the prediction. We formalize this problem as that of finding a short code for X that preserves the maximum information about Y. That is, we squeeze the information that X provides
Flexible camera calibration by viewing a plane from unknown orientations
, 1999
"... We propose a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled ..."
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Cited by 511 (7 self)
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We propose a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion
Lossy Source Coding under a Maximum Distortion Constraint with Decoder SideInformation
, 2004
"... A basic problem in information theory is source coding under a distortion constraint when the decoder has sideinformation about the source [1]. Traditionally, the constraint imposed is that the expected distortion between the source and its reconstruction averaged over the block being coded not exce ..."
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not exceed a given value. In certain applications (e.g. medical imaging) however, this constraint is considered too weak: we require that with probability 1 the maximum samplewise distortion in a block not exceed a given value. In this paper we shall focus on the problem of variable length coding of a
Computation of channel capacity and ratedistortion functions
 IEEE Trans. Inform. Theory
, 1972
"... A&r&By defining mutual information as a maximum over an appropriate space, channel capacities can be defined as double maxima and ratedistortion functions as double minima. This approach yields valuable new insights regarding the computation of channel capacities and ratedistortion functi ..."
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Cited by 280 (1 self)
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A&r&By defining mutual information as a maximum over an appropriate space, channel capacities can be defined as double maxima and ratedistortion functions as double minima. This approach yields valuable new insights regarding the computation of channel capacities and ratedistortion
Edge Detection and Ridge Detection with Automatic Scale Selection
 CVPR'96
, 1996
"... When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of ..."
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Cited by 347 (24 self)
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of scale levels when detecting onedimensional features, such as edges and ridges. Anovel concept of a scalespace edge is introduced, defined as a connected set of points in scalespace at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure
A data distortion by probability distribution
 ACM TRANSACTIONS ON DATABASE SYSTEMS
, 1985
"... This paper introduces data distortion by probability distribution, a probability distortion that involves three steps. The first step is to identify the underlying density function of the original series and to estimate the parameters of this density function. The second step is to generate a series ..."
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Cited by 71 (0 self)
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, the probability distorted series provides asymptotically the same statistical properties as those of the original series, since both are under the same distribution. Unlike conventional point distortion, probability distortion is difficult to compromise by repeated queries, and provides a maximum exposure
DistortionLimited Vector Quantization
 in Proc.Data Compression Conf.  DCC ’96
, 1996
"... This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected thresho ..."
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Cited by 3 (1 self)
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This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected
Approximating Average Distortion of Embeddings into Line
, 2003
"... elative #. vs Relative Bounds Absolute Bounds Best guarantee about "worst case" distortion. Guarantee on distortion is independent of input metric. Relative Bound Given, as input, a finite metric, embed it into the host metric to (approximately) minimize distortion. [cf. Ravi's Talk ..."
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Cited by 3 (2 self)
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's Talk] Comparing against the best possible distortion for the given input metric. Note: Absolute ##Relative #. Bounds: ExistingWork [LLR95] minimizing maximum distortion of embedding arbitrary finite metrics via SemiDefinite Programming. #1approximation maximum distortion problem. [WLB 98
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