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Search Results: 1 - 10 of 316681 matches for " Plataniotis K. N. "
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Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications
Wael Louis,K. N. Plataniotis
EURASIP Journal on Image and Video Processing , 2011, DOI: 10.1155/2011/745487
Abstract:
Multisensor estimation: New distributed algorithms
Plataniotis K. N.,Lainiotis D. G.
Mathematical Problems in Engineering , 1997,
Abstract: The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. The proposed distributed algorithms are theoretically interesting and computationally attractive.
Multisensor estimation: New distributed algorithms
K. N. Plataniotis,D. G. Lainiotis
Mathematical Problems in Engineering , 1996, DOI: 10.1155/s1024123x97000471
Abstract:
Hierarchical Fuzzy Feature Similarity Combination for Presentation Slide Retrieval
A. Kushki,M. Ajmal,K. N. Plataniotis
EURASIP Journal on Advances in Signal Processing , 2009, DOI: 10.1155/2008/547923
Abstract: This paper proposes a novel XML-based system for retrieval of presentation slides to address the growing data mining needs in presentation archives for educational and scholarly settings. In particular, contextual information, such as structural and formatting features, is extracted from the open format XML representation of presentation slides. In response to a textual user query, each extracted feature is used to compute a fuzzy relevance score for each slide in the database. The fuzzy scores from the various features are then combined through a hierarchical scheme to generate a single relevance score per slide. Various fusion operators and their properties are examined with respect to their effect on retrieval performance. Experimental results indicate a significant increase in retrieval performance measured in terms of precision-recall. The improvements are attributed to both the incorporation of the contextual features and the hierarchical feature combination scheme.
New filtering technique for the impulsive noise reduction in color images
B. Smolka,A. Chydzinski,K. N. Plataniotis,A. N. Venetsanopoulos
Mathematical Problems in Engineering , 2004, DOI: 10.1155/s1024123x04110016
Abstract: We present a novel approach to the problem of impulsive noise reduction for colorimages. The new image-filtering technique is based on the maximization of the similarities between pixels in the filtering window. Themethod is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Experimental results indicate that the new method is superior, in terms of performance, to algorithms commonly used for impulsive noise reduction.
Adaptive filters for color image processing
Papanikolaou V.,Plataniotis K. N.,Venetsanopoulos A. N.
Mathematical Problems in Engineering , 1998,
Abstract: The color filters that are used to attenuate noise are usually optimized to perform extremely well when dealing with certain noise distributions. Unfortunately it is often the case that the noise corrupting the image is not known. It is thus beneficial to know a priori the type of noise corrupting the image in order to select the optimal filter. A method of extracting and characterizing the noise within a digital color image using the generalized Gaussian probability density function (pdf) (B.D. Jeffs and W.H. Pun, IEEE Transactions on Image Processing, 4(10), 1451–1456, 1995 and Proceedings of the Int. Conference on Image Processing, 465–468, 1996), is presented. In this paper simulation results are included to demonstrate the effectiveness of the proposed methodology.
New filtering technique for the impulsive noise reduction in color images
Smolka B.,Chydzinski A.,Plataniotis K. N.,Venetsanopoulos A. N.
Mathematical Problems in Engineering , 2004,
Abstract: We present a novel approach to the problem of impulsive noise reduction for colorimages. The new image-filtering technique is based on the maximization of the similarities between pixels in the filtering window. Themethod is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Experimental results indicate that the new method is superior, in terms of performance, to algorithms commonly used for impulsive noise reduction.
Adaptive filters for color image processing
V. Papanikolaou,K. N. Plataniotis,A. N. Venetsanopoulos
Mathematical Problems in Engineering , 1999, DOI: 10.1155/s1024123x98000957
Abstract:
Boosting Discriminant Learners for Gait Recognition Using MPCA Features
Haiping Lu,K. N. Plataniotis,A. N. Venetsanopoulos
EURASIP Journal on Image and Video Processing , 2009, DOI: 10.1155/2009/713183
Abstract: This paper proposes a boosted linear discriminant analysis (LDA) solution on features extracted by the multilinear principal component analysis (MPCA) to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.
Discrete Riccati equation solutions: Distributed algorithms
D. G. Lainiotis,K. N. Plataniotis,Michail Papanikolaou,Paraskeuas Papaparaskeva
Mathematical Problems in Engineering , 1996, DOI: 10.1155/s1024123x96000373
Abstract:
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