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86
APPLICATION OF ADVANCED MORPHOLOGICAL FILTERS INTO IMAGE SEGMENTATION
, 1997
"... This paper is devoted to a segmentation method using advanced morphological filtering by reconstruction followed by clustering by k-means algorithm. Advanced morphological filtering bases on morphological reconstruction and two filters are applied: opening by reconstruction and closing by reconstruc ..."
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This paper is devoted to a segmentation method using advanced morphological filtering by reconstruction followed by clustering by k-means algorithm. Advanced morphological filtering bases on morphological reconstruction and two filters are applied: opening by reconstruction and closing
ANY-TIME FUZZY CONTROLLER
, 2006
"... Fuzzy logic has been successfully applied in various fields. However, as fuzzy controllers increase in size and complexity, the number of control rules increases exponentially and real-time behavior becomes more difficult. This thesis introduces an any-time fuzzy controller. Much work has been done ..."
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of fuzzy control. Popular aggregation and defuzzification methods (max-min, sum-product, MOM and COG) are first shown to satisfy these constraints, and then three linearization methods are presented. Linearization methods are used to reorder fuzzy rules base such that a reordered rule base would result
Spatial Fuzzy Clustering using EM and Markov Random Fields
- InternationalJournal of System Research and Information Science
, 1998
"... Methods are investigated in order to partition in k groups a set of n multivariate observation vectors located at neighboring geographic sites; applications include image segmentation, ecological or soil data cartography. In this perspective, the deterministic variant of the EM procedure described ..."
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Cited by 4 (0 self)
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\Theta k) posterior probabilities that the n observations belong to the K groups, computed by an efficient iterative method based on the mean field approximation principle. The resulting algorithm may be viewed as an extension of the k-means algorithm to fuzzy clustering and spatial data. Keywords
Iris Data Indexing Method Using Biometric Features 1
"... Abstract A biometric system provides identification of an individual based on a unique feature or characteristic possessed by the individual. Among the available biometric identification system, Iris recognition is regarded as the most reliable and accurate one. Demands are increasing to deal with ..."
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with large scale databases in these applications. The Segmentation in boundary detection, edge Mapping, circular Hough Transform, extracting Region of interest (Eyelash and noise removal), circle detection. In a module of Person Identification system using Iris Recognition. The iris recognition system
Active Mask Framework for Segmentation of Fluorescence Microscope Images
"... m]]l]]s¶D]]¿÷mB]iv]b]oD]m¶¨]iv]§]iv]r]j]t¿rv]]irj]]t]]m] / | ap]]r¿]ÎNy]s¶D]]mb¶r]ix} Û]Ix]]rd]mb]} p—N]t]o%ism] in]ty]m] / || Û]Is]¡uÎc]rN]]riv]nd]p]*N]m]st¶ I always bow to Śri ̄ Śāradāmbā, the limitless ocean of the nectar of compassion, who bears a rosary, a vessel of nectar, the symbol of ..."
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facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together
RICE UNIVERSITY Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing
, 2011
"... The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
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. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find
for Sparse Translation-Invariant Signals
"... Document Version Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA): ..."
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Document Version Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA):
PROOF COPY 012212JOE PROOF COPY 012212JOE Fuzzy block truncation coding
"... Abstract. Block truncation coding (BTC) is a well known lossy compres-sion scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to ..."
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, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algo-rithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental re
1 Partially Linear Estimation with Application to Sparse Signal Recovery From Measurement Pairs
"... ar ..."
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