IEEE Organizations related to Histograms

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Conferences related to Histograms

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2016 IEEE International Conference on Image Processing (ICIP)

Signal processing, image processing, biomedical imaging, multimedia, video, multidemensional.


2012 8th International Conference on Natural Computation (ICNC)

ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering and more.

  • 2011 Seventh International Conference on Natural Computation (ICNC)

    ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering, and more.

  • 2010 Sixth International Conference on Natural Computation (ICNC)

    ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering, and more.



Periodicals related to Histograms

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Medical Imaging, IEEE Transactions on

Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...


Signal Processing Letters, IEEE

Rapid dissemination of new results in signal processing world-wide.


Visualization and Computer Graphics, IEEE Transactions on

Specific topics include, but are not limited to: a) visualization techniques and methodologies; b) visualization systems and software; c) volume visulaization; d) flow visualization; e) information visualization; f) multivariate visualization; g) modeling and surfaces; h) rendering techniques and methodologies; i) graphics systems and software; j) animation and simulation; k) user interfaces; l) virtual reality; m) visual programming and program visualization; ...



Most published Xplore authors for Histograms

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Xplore Articles related to Histograms

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Histogram cloning and CuSum: An experimental comparison between different approaches to Anomaly Detection

Christian Callegari; Stefano Giordano; Michele Pagano 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 2015

Due to the proliferation of new threats from spammers, attackers, and criminal enterprises, Anomaly-based Intrusion Detection Systems have emerged as a key element in network security and different statistical approaches have been considered in the literature. To cope with scalability issues, random aggregation through the use of sketches seems to be a powerful prefiltering stage that can be applied to ...


A Novel Image Encryption Algorithm Based on Hyperchaotic System and Shuffling Scheme

Xiaoheng Deng; Chunlong Liao; Congxu Zhu; Zhigang Chen 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, 2013

We propose an improved image encryption algorithm based on hyper chaotic system and permutation-diffusion structure. In the permutation process, the algorithm introduces plain-text feedback mechanism that makes the permutation effect not only associated with the chaotic sequences, but also related to plain-text. And in the diffusion process, the algorithm introduces both cipher-text and plain-text feedback mechanisms to diffuse the permuted ...


A Parallel, Non-parametric, Non-iteratrve Clustering Algorithm With Application To Image Segmentation

A. Khotanzad; A. Bouarfa Twenty-Second Asilomar Conference on Signals, Systems and Computers, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754005.png)


Multi-feature fusion based re-ranking for person re-identification

Saeed ur Rehman; Zonghai Chen; Jamal Hussain Shah; Mudassar Raza 2016 International Conference on Audio, Language and Image Processing (ICALIP), 2016

In computer vision applications such as person re-identification the optimization of rank list is an important issue. In order to address this issue, a multi-feature fusion based re-ranking technique is proposed. In most of the conventional methods, a long feature vector is formulated from a single modality. Whereas, in the proposed approach, multiple features from the image are extracted and ...


Scale-Specific Similarity Measure for Analysis of Gene Expression Time Series

Li Ying 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Combined cross-correlation and multi-resolution of maximal overlap discrete wavelets, the scale-specific similarity measure for the analysis of gene expression time series is provided, which can capture the relationship of co- expression under time-delay and local time points. The scale-specific similarity measure have more possible to capture more biological knowledge than Pearson correlation and cross correlation.


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Educational Resources on Histograms

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eLearning

Histogram cloning and CuSum: An experimental comparison between different approaches to Anomaly Detection

Christian Callegari; Stefano Giordano; Michele Pagano 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 2015

Due to the proliferation of new threats from spammers, attackers, and criminal enterprises, Anomaly-based Intrusion Detection Systems have emerged as a key element in network security and different statistical approaches have been considered in the literature. To cope with scalability issues, random aggregation through the use of sketches seems to be a powerful prefiltering stage that can be applied to ...


A Novel Image Encryption Algorithm Based on Hyperchaotic System and Shuffling Scheme

Xiaoheng Deng; Chunlong Liao; Congxu Zhu; Zhigang Chen 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, 2013

We propose an improved image encryption algorithm based on hyper chaotic system and permutation-diffusion structure. In the permutation process, the algorithm introduces plain-text feedback mechanism that makes the permutation effect not only associated with the chaotic sequences, but also related to plain-text. And in the diffusion process, the algorithm introduces both cipher-text and plain-text feedback mechanisms to diffuse the permuted ...


A Parallel, Non-parametric, Non-iteratrve Clustering Algorithm With Application To Image Segmentation

A. Khotanzad; A. Bouarfa Twenty-Second Asilomar Conference on Signals, Systems and Computers, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754005.png)


Multi-feature fusion based re-ranking for person re-identification

Saeed ur Rehman; Zonghai Chen; Jamal Hussain Shah; Mudassar Raza 2016 International Conference on Audio, Language and Image Processing (ICALIP), 2016

In computer vision applications such as person re-identification the optimization of rank list is an important issue. In order to address this issue, a multi-feature fusion based re-ranking technique is proposed. In most of the conventional methods, a long feature vector is formulated from a single modality. Whereas, in the proposed approach, multiple features from the image are extracted and ...


Scale-Specific Similarity Measure for Analysis of Gene Expression Time Series

Li Ying 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Combined cross-correlation and multi-resolution of maximal overlap discrete wavelets, the scale-specific similarity measure for the analysis of gene expression time series is provided, which can capture the relationship of co- expression under time-delay and local time points. The scale-specific similarity measure have more possible to capture more biological knowledge than Pearson correlation and cross correlation.


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IEEE-USA E-Books

  • Kernels on Structured Objects Through Nested Histograms

    We propose a family of kernels for structured objects which is based on the bag-ofcomponents paradigm. However, rather than decomposing each complex object into the single histogram of its components, we use for each object a family of nested histograms, where each histogram in this hierarchy describes the object seen from an increasingly granular perspective. We use this hierarchy of histograms to define elementary kernels which can detect coarse and fine similarities between the objects. We compute through an efficient averaging trick a mixture of such specific kernels, to propose a final kernel value which weights efficiently local and global matches. We propose experimental results on an image retrieval experiment which show that this mixture is an effective template procedure to be used with kernels on histograms

  • Histogram Operations

    The implementation of histograms and histogram based processing are discussed in this chapter. Techniques of accumulating a histogram, and then extracting data from the histogram are described in detail. Histogram equalisation, threshold selection, and the use of clustering for colour segmentation and classification is also discussed. The chapter concludes with the use of features extracted from multidimensional histograms for texture analysis.

  • No title

    Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series- based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio- temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neura networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.

  • Beyond Parametrics

    This chapter contains sections titled: 13.1 Introduction, 13.2 CUP: Unions of Parametric Models, 13.3 Universal Codes Based on Histograms, 13.4 Nonparametric Redundancy, 13.5 Gaussian Process Regression, 13.6 Conclusion and Further Reading

  • Appendix D: Calculation of Shell Frequency Distribution

    This chapter includes the following topics: Partial Histograms Partial Histograms for General Cost Functions Frequencies of Shells

  • Fast Discriminative Visual Codebooks using Randomized Clustering Forests

    Some of the most effective recent methods for content-based image classification work by extracting dense or sparse local image descriptors, quantizing them according to a coding rule such as k-means vector quantization, accumulating histograms of the resulting "visual word" codes over the image, and classifying these with a conventional classifier such as an SVM. Large numbers of descriptors and large codebooks are needed for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests -- ensembles of randomly created clustering trees -- and show that these provide more accurate results, much faster training and testing and good resistance to background clutter in several state-of-the-art image classification tasks.

  • Histogram Processing

    This chapter contains sections titled: Image Histogram: Definition and Example Computing Image Histograms Interpreting Image Histograms Histogram Equalization Direct Histogram Specification Other Histogram Modification Techniques Tutorial 9.1: Image Histograms Tutorial 9.2: Histogram Equalization and Specification Tutorial 9.3: Other Histogram Modification Techniques Problems

  • No title

    <p>Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.</p><p>This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.</p><p>We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, w give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.</p><p>The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.</p>

  • Generate Quality Graphs

    This chapter covers the most effective ways to display data in various graphical forms. It also showcases the kinds of visuals that engineers, scientists, and technical experts use daily. Identifying the typical design traps that ensnare even the most well-intentioned professionals can help us avoid creating junk and, instead, propel us toward elegance and simplicity. Whether the presenter is creating a plot, a chart, or a table, there are a few aspects of design that can ensure success for the visual. These basic tenets can save embarrassment and audience frustration if applied well during the planning process. Establishing a baseline of best practices for pie charts enables avoiding many of the typical mistakes that can happen with this simple chart type. Bar charts and histograms appear frequently in engineering, scientific, and technical presentations and papers because they provide visual comparison that the user can digest quickly and easily.

  • Independence and Probability Density Functions

    This chapter contains sections titled: Introduction, Histograms, Histograms and Probability Density Functions, The Central Limit Theorem, Cumulative Density Functions, Moments: Mean, Variance, Skewness and Kurtosis, Independence and Correlation, Uncorrelated Pendulums, Summary



Standards related to Histograms

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No standards are currently tagged "Histograms"


Jobs related to Histograms

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