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K.B.: MultiInterval Discretization of ContinuousValued Attributes for Classication Learning. In:
 IJCAI.
, 1993
"... Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuousvalued a ..."
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Cited by 832 (7 self)
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Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous
Snakes, Shapes, and Gradient Vector Flow
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1998
"... Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new extern ..."
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Cited by 755 (16 self)
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Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new
Unsupervised Learning by Probabilistic Latent Semantic Analysis
 Machine Learning
, 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of cooccurren ..."
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Cited by 618 (4 self)
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Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co
Generating Binary Processes with allPole Spectra
 in Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
, 2007
"... This paper presents an algorithm to generate autoregressive random binary processes with predefined mean and predefined allpole power spectrum, subject to specific constraints on the parameters of the allpole spectrum. The process is generated recursively using a linear combination of the previous ..."
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Cited by 2 (0 self)
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This paper presents an algorithm to generate autoregressive random binary processes with predefined mean and predefined allpole power spectrum, subject to specific constraints on the parameters of the allpole spectrum. The process is generated recursively using a linear combination
GLR Control Charts for Monitoring Correlated Binary Processes
, 2013
"... When monitoring a binary process proportion!!, it is usually assumed that the binary observations are independent. However, it is very common that the observations are correlated with! being the correlation between two successive observations. The first part of this research investigates the problem ..."
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When monitoring a binary process proportion!!, it is usually assumed that the binary observations are independent. However, it is very common that the observations are correlated with! being the correlation between two successive observations. The first part of this research investigates
Stereo matching using belief propagation
, 2003
"... In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, ..."
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Cited by 350 (4 self)
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, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other lowlevel visual cues (e.g., image segmentation
Development of a binary Process Quality Control methodology
"... Process Quality Control (PQC) methodologies aim to improve quality and productivity by identifying and eliminating rootcauses of qualityrelated problems for manufacturing processes. ..."
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Process Quality Control (PQC) methodologies aim to improve quality and productivity by identifying and eliminating rootcauses of qualityrelated problems for manufacturing processes.
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2007
"... Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also ..."
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Cited by 291 (35 self)
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are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co
Performance analysis of kary ncube interconnection networks
 IEEE Transactions on Computers
, 1990
"... AbstmctVLSI communication networks are wirelimited. The cost of a network is not a function of the number of switches required, but rather a function of the wiring density required to construct the network. This paper analyzes communication networks of varying dimension under the assumption of co ..."
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Cited by 357 (18 self)
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spot throughput than highdimensional networks (e.g., binary ncubes) with the same bisection width. Index Terms Communication networks, concurrent computing, interconnection networks, messagepassing multiprocessors, parallel processing, VLSI. I.
Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms
 IEEE Transactions on Image Processing
, 1993
"... Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked " by a (binary) image J contained in I. This transformation can be ext ..."
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Cited by 336 (3 self)
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Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked " by a (binary) image J contained in I. This transformation can
Results 1  10
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