Results 1  10
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1,267
Iterative decoding of binary block and convolutional codes
 IEEE TRANS. INFORM. THEORY
, 1996
"... Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the soft chann ..."
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Cited by 610 (43 self)
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Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the soft
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing
 IEEE TRANSACTIONS ON COMPUTERS
, 1987
"... Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or costsensitive applications. In some situations, designers are not willing to squander computing resources for the sake of pro ..."
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Cited by 598 (37 self)
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flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need
Graphcut textures: Image and video synthesis using graph cuts
 ACM Transactions on Graphics, SIGGRAPH 2003
, 2003
"... This banner was generated by merging the source images in Figure 6 using our interactive texture merging technique. In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output a ..."
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Cited by 490 (9 self)
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and then stitched together along optimal seams to generate a new (and typically larger) output. In contrast to other techniques, the size of the patch is not chosen apriori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture
Iterative (turbo) soft interference cancellation and decoding for coded CDMA
 IEEE Trans. Commun
, 1999
"... Abstract — The presence of both multipleaccess interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath codedivision multipleaccess (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuse ..."
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Cited by 456 (18 self)
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multiuser information data in a convolutionally coded asynchronous multipath DSCDMA system. The receiver performs two successive softoutput decisions, achieved by a softinput softoutput (SISO) multiuser detector and a bank of singleuser SISO channel decoders, through an iterative process. At each
A general framework for object detection
 Sixth International Conference on
, 1998
"... This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of ..."
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Cited by 395 (21 self)
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of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a suppori vector machine classifier. This representation overcomes both the problem of inclass variability and provides a low false detection rate
Recognition of visual activities and interactions by stochastic parsing
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... This paper describes a probabilistic syntactic approach to the detection and recognition of temporally extended activities and interactions between multiple agents. The fundamental idea is to divide the recognition problem into two levels. The lower level detections are performed using standard inde ..."
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Cited by 322 (8 self)
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independent probabilistic event detectors to propose candidate detections of lowlevel features. The outputs of these detectors provide the input stream for a stochastic contextfree grammar parsing mechanism. The grammar and parser provide longer range temporal constraints, disambiguate uncertain low
Minimum mean squared error equalization using a priori information
 IEEE TRANS. SIGNAL PROCESSING
, 2002
"... A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder e ..."
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Cited by 153 (12 self)
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complexity softinput/softoutput (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSEoptimal solution. All
Algorithms for the universal and a priori tsp
 Oper. Res. Lett
, 2008
"... We present two simple results for generalizations of the traveling salesman problem (TSP): For the universal TSP, we show that one can compute a tour that is universally optimal whenever the input is a tree metric. A (randomized) O(log n)approximation algorithm for the a priori TSP follows as a cor ..."
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Cited by 5 (0 self)
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We present two simple results for generalizations of the traveling salesman problem (TSP): For the universal TSP, we show that one can compute a tour that is universally optimal whenever the input is a tree metric. A (randomized) O(log n)approximation algorithm for the a priori TSP follows as a
Results 1  10
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1,267