IEEE Organizations related to Histograms

Back to Top


Conferences related to Histograms

Back to Top

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

Back to Top

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; ...




Xplore Articles related to Histograms

Back to Top

Detection of centroblasts in H&E stained images of follicular lymphoma

Emmanouil Michail; Evgenios N. Kornaropoulos; Kosmas Dimitropoulos; Nikos Grammalidis; Triantafyllia Koletsa; Ioannis Kostopoulos 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014

This paper presents a complete framework for automatic detection of malignant cells in microscopic images acquired from tissue biopsies of follicular lymphoma. After pre-processing to remove noise and suppress small details, images are segmented by using intensity thresholding, in order to detect the cell nuclei. Subsequently, touching cells are being separated using Expectation Maximization algorithm. Candidate centroblasts are then selected ...


A fuzzy approach to real-time digital color reproduction of clothing with 3D camera

Finn Wong; Donghai Dai 2015 IEEE International Conference on Image Processing (ICIP), 2015

Digital color reproduction[1][2] is a technique that can change the color appearance of objects shown in an image or video. The technique is utilized in various aspects and scenarios including projection-based appearances editing[3], medical imaging[4] and discrimination improvement for dichromats or anomalous trichromats[5][6]. However, traditional object color reproduction has high computational complexity due to color space transform, temporal color consistency ...


Multiple object tracking based on sparse generative appearance modeling

Dorra Riahi; Guillaume-Alexandre Bilodeau 2015 IEEE International Conference on Image Processing (ICIP), 2015

This paper addresses multiple object tracking which still remains a challenging problem because of factors like frequent occlusions, unknown number of targets and similarity in objects' appearance. We propose a novel approach for multiple object tracking using a multiple feature framework. The main focus of the proposed method is to build a robust appearance model. The appearance model of an ...


New multi-view image color correction method with karhunen- loeve transform

Feng Shao; Gangyi Jiang; Zhidi Jiang; Mei Yu; Weiyue Liu; Xiexiong Chen 2006 8th international Conference on Signal Processing, 2006

Inconsistent color between different viewpoints is a serious problem in multi- view image processing. A multi-view image color correction method is proposed. We first estimate whether global correction satisfied for the target image and source image. If no global correction is satisfied, image segmentation and Karhunen-Loeve (K-L) transform are performed. Then, local mapping relations between target and source images will ...


Cloud detection in all sky ConCam images by Gaussian fitting and valley detection in histogram

Tushar Jadhav; Aditi Kotibhaskar 2015 Eighth International Conference on Contemporary Computing (IC3), 2015

Clouds are the complex part to classify in the all sky Continuous Camera (ConCam) image due to their intensity similar to other objects and their diverse nature. The paper presents a histogram based technique to extract clouds in ConCam images. Small scale features are extracted by subtracting Gaussian fitted histogram from original histogram of an image. Valleys are then detected ...


More Xplore Articles

Educational Resources on Histograms

Back to Top

eLearning

Detection of centroblasts in H&E stained images of follicular lymphoma

Emmanouil Michail; Evgenios N. Kornaropoulos; Kosmas Dimitropoulos; Nikos Grammalidis; Triantafyllia Koletsa; Ioannis Kostopoulos 2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014

This paper presents a complete framework for automatic detection of malignant cells in microscopic images acquired from tissue biopsies of follicular lymphoma. After pre-processing to remove noise and suppress small details, images are segmented by using intensity thresholding, in order to detect the cell nuclei. Subsequently, touching cells are being separated using Expectation Maximization algorithm. Candidate centroblasts are then selected ...


A fuzzy approach to real-time digital color reproduction of clothing with 3D camera

Finn Wong; Donghai Dai 2015 IEEE International Conference on Image Processing (ICIP), 2015

Digital color reproduction[1][2] is a technique that can change the color appearance of objects shown in an image or video. The technique is utilized in various aspects and scenarios including projection-based appearances editing[3], medical imaging[4] and discrimination improvement for dichromats or anomalous trichromats[5][6]. However, traditional object color reproduction has high computational complexity due to color space transform, temporal color consistency ...


Multiple object tracking based on sparse generative appearance modeling

Dorra Riahi; Guillaume-Alexandre Bilodeau 2015 IEEE International Conference on Image Processing (ICIP), 2015

This paper addresses multiple object tracking which still remains a challenging problem because of factors like frequent occlusions, unknown number of targets and similarity in objects' appearance. We propose a novel approach for multiple object tracking using a multiple feature framework. The main focus of the proposed method is to build a robust appearance model. The appearance model of an ...


New multi-view image color correction method with karhunen- loeve transform

Feng Shao; Gangyi Jiang; Zhidi Jiang; Mei Yu; Weiyue Liu; Xiexiong Chen 2006 8th international Conference on Signal Processing, 2006

Inconsistent color between different viewpoints is a serious problem in multi- view image processing. A multi-view image color correction method is proposed. We first estimate whether global correction satisfied for the target image and source image. If no global correction is satisfied, image segmentation and Karhunen-Loeve (K-L) transform are performed. Then, local mapping relations between target and source images will ...


Cloud detection in all sky ConCam images by Gaussian fitting and valley detection in histogram

Tushar Jadhav; Aditi Kotibhaskar 2015 Eighth International Conference on Contemporary Computing (IC3), 2015

Clouds are the complex part to classify in the all sky Continuous Camera (ConCam) image due to their intensity similar to other objects and their diverse nature. The paper presents a histogram based technique to extract clouds in ConCam images. Small scale features are extracted by subtracting Gaussian fitted histogram from original histogram of an image. Valleys are then detected ...


More eLearning Resources

IEEE-USA E-Books

  • Appendix D: Calculation of Shell Frequency Distribution

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

  • Inserting Illustrations into Reports

    Illustrations appear mostly in longer, more formal reports, such as analyses, feasibility studies, proposals, and investigation or evaluation reports. For selecting and designing the most effective illustration for a given situation, three issues need to be clarified: which the kind of illustration (tables, graphs, bar charts, histograms, surface charts, pie charts, flow charts, and photographs) can best illustrate the particular feature or characteristic for the readers to comprehend; whether the illustration is simply used by the readers to gain a visual impression of an aspect being discussed, or can it be used to extract information; and is the illustration referred to only once to explain a point, or is it referred to several times in the report. Computer software can be used, with care, for illustration preparation. This chapter describes various ways to present information graphically, and explains what type of information is best suited for each.

  • 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

  • Beyond 2001: The Linguistic Spatial Odyssey

    This chapter contains sections titled: Introduction Force Histograms and Linguistic Scene Description Scene Matching Human-Robot Dialog Sketched Route Map Understanding The Future This chapter contains sections titled: Acknowledgments References

  • 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

  • BER Statistical Measurements

    This chapter contains sections titled: Introduction Sampling and Statistical Histograms Statistical Sampling for BER Estimating BER, Q-Factor, and SNR The BER Circuit Performance of the BER Circuit Other Performance Metrics References Standards

  • 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.

  • 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.

  • 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

  • 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.



Standards related to Histograms

Back to Top

No standards are currently tagged "Histograms"


Jobs related to Histograms

Back to Top