Markov random fields
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...
EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, 2012
This paper presents a new approach for change detection in SAR images based on clustering method. Classic change detection methods use a hard threshold to divide the difference map into two classes: change and unchanged, which has a disadvantage that some weak changed regions are often undetected. Unlike those methods, our proposed method use expectation maximization with graph cut optimization ...
Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005
In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile ...
2014 2nd International Conference on Electronic Design (ICED), 2014
Microarray is one of the most promising tools available for researchers in the life sciences to study gene expression profiles. Through microarray analysis, gene expression levels can be obtained, and the biological information of a disease can be identified. The gene expression information embedded in the microarray is extracted using image-processing techniques. Gridding is one of the important processes used ...
International Conference on Acoustics, Speech, and Signal Processing,, 1989
The authors present a novel image coding algorithm based on a class of image models known as doubly stochastic Gaussian models (DSGM). They exploit the nonhomogeneous nature of images by a space-variant autoregressive representation that switches in a set of linear predictive submodels. The switch is controlled by a 2-D Markov chain. The coder is a DPCM (differential pulse code ...
IEEE Signal Processing Letters, 2008
As a region-based approach, the Mumford-Shah (MS) model is a robust image segmentation technique. However, the solution of the MS model is not trivial. Although some alternative approaches have been presented, these methods are either inefficient or applicable only to some special cases. We present a new model which consists of two terms, the length of the segmentation curve and ...
Random Sparse Adaptation for Accurate Inference with Inaccurate RRAM Arrays - IEEE Rebooting Computing 2017
IEEE Medal of Honor Recipient (2007): Thomas Kailath
IEEE Medal of Honor Recipient (2009): Dr. Robert Dennard
Rebooting Computing: Randomness and Approximation
8-Element, 1-3GHz Direct Space-to-Information Converter - Matthew Bajor - RFIC Showcase 2018
Anticipating Human Activities for Reactive Robotic Response
Cross Entropy Benchmarking & Quantum Supremacy - Sergio Boixo - ICRC San Mateo, 2019
A Global Engineering Challenge
Mario Milicevic - IEEE Theodore W. Hissey Outstanding Young Professional Award, 2019 IEEE Honors Ceremony
Young Professionals at N3XT: Bringing Together Tech Fields
IEEE Photonics Conference 2017 Recap
Robotics History: Narratives and Networks Oral Histories: George Bekey
Changing the world: IEEE Women in Engineering (WIE)
EMBC '09 - Advances in Neuro-rehabilitation
ICRA 2020 Keynote - Can Deep Reinforcement Learning from pixels be made as efficient as from state?
Device versus Circuit Engineer
Magnetic Nanowires: Revolutionizing Hard Drives, RAM, and Cancer Treatment
What's New in Storage Devices - Jim Gathman from IBM
John G. Webster - IEEE James H. Mulligan, Jr. Education Medal, 2019 IEEE Honors Ceremony
This paper presents a new approach for change detection in SAR images based on clustering method. Classic change detection methods use a hard threshold to divide the difference map into two classes: change and unchanged, which has a disadvantage that some weak changed regions are often undetected. Unlike those methods, our proposed method use expectation maximization with graph cut optimization to cluster the difference map into three classes: strong changed areas, weak changed areas and unchanged areas. The experimental results on real SAR images show that our approach obtains a higher detection rate than the previous ones.
In this paper, we propose a statistical-structural scheme for Chinese character modeling based on Markov random fields (MRFs). We use 2-D Gabor filters to extract directional stroke segments from images of Chinese characters, where each stroke segment is associated with a state in Markov random field models. The structural information is described by neighborhood system and pair-state clique potentials; meanwhile the statistical information is represented by single-state probability density functions (pdfs). Extensive experiments on similar characters have been carried out on the database ETL9B. The experimental results confirm that Markov random field models are effective in modeling both statistical and structural information of Chinese characters, and works well for handwritten Chinese character recognition.
Microarray is one of the most promising tools available for researchers in the life sciences to study gene expression profiles. Through microarray analysis, gene expression levels can be obtained, and the biological information of a disease can be identified. The gene expression information embedded in the microarray is extracted using image-processing techniques. Gridding is one of the important processes used to extract features in DNA microarray, by assigning each spot in the microarray with individual coordinates for further data interpretation. This paper evaluates popular techniques of DNA microarray image gridding in the literature with an emphasis on gridding accuracy, speed, and the ability to remove noise. Based on our evaluation, the Otsu method can provide a better performance in terms of processing speed, accuracy, and ability to remove noise compared to other methods discussed in this paper.
The authors present a novel image coding algorithm based on a class of image models known as doubly stochastic Gaussian models (DSGM). They exploit the nonhomogeneous nature of images by a space-variant autoregressive representation that switches in a set of linear predictive submodels. The switch is controlled by a 2-D Markov chain. The coder is a DPCM (differential pulse code modulation) system that is given the submodel (predictor) coefficients. In order to obtain these predictors, a recursive state estimation of the underlying Markov chain is done. This combination provides the doubly recursive prediction nature of this algorithm. Two coding schemes based on this structure are introduced. The first is a backward adaptation DPCM coder that needs very few side information bits. The other transmits codes for both model indices and quantized prediction residuals. Experimental results for different bit rates are presented.<<ETX>>
As a region-based approach, the Mumford-Shah (MS) model is a robust image segmentation technique. However, the solution of the MS model is not trivial. Although some alternative approaches have been presented, these methods are either inefficient or applicable only to some special cases. We present a new model which consists of two terms, the length of the segmentation curve and the high-frequency component in the regions. Because only one variable needs to be solved, the method of solution is very efficient. Using the level set method, the approach can segment objects with complicated image intensity distribution without any approximations. In addition, the new model can segment both step and roof edges.
Bayesian approaches, or maximum a posteriori (MAP) methods, have been accepted as an effective solution to overcome the ill-posed problem of such image reconstructions as positron emission tomography (PET) image reconstruction. Based on Bayesian theory, prior information of the objective image is imposed on image reconstruction to suppress noise. Generally, the information in most of prior models is from a simply weighted differences between the pixel densities in a small local neighborhood, so it can only provide limit prior information for reconstruction. In this paper, a novel nonlocal Markov random fields (MRF) prior, which is able to exploit global information in image using large neighborhoods and a new weighting method, is proposed. Relevant experiments about the proposed prior's application in PET are illustrated. Results and comparisons with other priors proved the proposed nonlocal prior's good performance in both lowering noise effect and preserving edges
We develop multispectral random field image models for use in image processing applications. The simultaneous autoregressive and Markov random field (MRF) models have been widely used in modeling intensity images. In this work we extend these models to include the more general multispectral case where images are represented by multiple intensity planes. In particular, we focus on the obvious application to color texture modeling using the RGB color model. For each model type we present the model equations, develop methods for synthesizing images based on these models and procedures for estimating the model parameters. In addition, the conditions necessary to ensure model validity are identified. We also provide experimental results which, substantiate the validity of these results. Color images synthesized from these models are shown to have the statistical characteristics implied by the model equations and parameters estimated from these images are very close to the known values from which the images were generated. In further experiments color random field models were fitted to natural texture samples. Images synthesized from these models are observed to be visually similar to the original images.
Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.<<ETX>>
To get good reconstruction from the SPECT data heavily corrupted by noise, attenuation, scattering and blurring, it is essential to find the medium attenuation/scatter distribution of the study. In myocardial SPECT, this problem is more severe because of the complex structure of the chest. The authors present a Bayesian approach which estimate both the activity distribution and the medium distribution simultaneously. Their method consists of three models for the different aspects of SPECT. It is concluded that the deformable template used to model the medium distribution, together with the Markov random field, used to model the activity distribution, provides a way to impose global and local regularizations on the highly ill-posed problem of reconstructing activity distribution from the projection data alone. The Bayesian approach yields good results for reconstructions in myocardial SPECT.
A probabilistic graphical model is proposed for the complex video foreground and background discrimination. The model learns the temporal and the spatial correlation from the video input data. The inference of the graphical model is achieved with the generalized belief propagation algorithm. Experiments have shown that the proposed method is able to model the dynamic backgrounds containing swaying trees, bushes and moving ocean waves. The final segmentation results are very promising.
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