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international Conference on image Information processing

作者： Ashish Phophalia Suman K. Mitra Ajit K. Rajwade Dhirubhai Ambani Institute of Information & Communication Technology Gandhinagar Gujarat India Department of Computer Sci. & Engineering Indian Institute of Technology Bombay Maharashtra India

Current state-of-the-art research on denoising involves patch similarity. The similar patches are obtained either from image itself or from dictionary of patches. This paper proposes a new way to find similar patches from a given image using Rough Set Theory (RST). Search for similar patches is usually restricted locally. However, a global search could fetch patches which are more similar. The current RST based approach is enabling such search global and hence satisfying the non-local principal which is the basis for patch based denoising. Like a few other denoising techniques, the framework of nonlocal means and principal component analysis both are then utilized to denoise medical images. The main essence of the current work reflects true sense of non-locality of similar patches. Exhaustive experiments clearly indicate comparability of the current proposal to the state-of-the-art methods in the light of several evaluation measures.

关键词： Noise level Noise reduction approximation methods Noise measurement PSNR Set theory

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Segmentation-based illumination normalization for face detection

Segmentation-based illumination normalization for face detec...

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IEEE international workshop on Computational Intelligence & Applications (IWCIA)

作者： Min Yao Kota Aoki Hiroshi Nagahashi Department of Information Processing Interdisciplinary Graduate School of Science andEngineering Tokyo Institute of Technology Yokohama Japan Imaging Science and Engineering Laboratory Tokyo Institute of Technology Yokohama Japan

Face detection is an important research topic in the field of computer vision. Illumination problem is one of the most important aspects impeding the effectiveness of face detection. The well known Haar-like face detector developed by Viola and Jones is also largely weakened under adverse lighting conditions such as backlighting or uneven lighting. In this paper, a novel segmentation-based illumination normalization method is presented for the purpose of compensating non-uniform illuminations and increasing the robustness of Haar-like face detector. First Otsu method is employed to segment the input image. Then the proposed illumination normalization method called Half Histogram Truncation and Stretching (HHTS) is applied to locally attenuate the illumination and enhance the visibility of local patterns (facial structures). Finally Haar-like face detector is executed to locate faces. Experimental results show that it can remove non-uniform illuminations efficiently and significantly increase the performance of the original Haar-like face detector.

关键词： Lighting Face Detectors Face detection image segmentation Histograms Learning systems

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Multi-scale Gaussian representation and outline-learning based cell image segmentation

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BMC BIOINFORMATICS 2013年第10-sup期14卷 1-14页

作者： Farhan, Muhammad Ruusuvuori, Pekka Emmenlauer, Mario Raemoe, Pauli Dehio, Christoph Yli-Harja, Olli Tampere Univ Technol Dept Signal Proc Tampere 33720 Finland Univ Basel Biozentrum CH-4056 Basel Switzerland

Background: High-throughput genome-wide screening to study gene-specific functions, e. g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods: We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions: We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

关键词： local Binary Pattern Cytoplasmic Region Intensity Inhomogeneity Sparse Model Cell Segmentation

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localLY LINEAR EMBEDDING BASED TEXTURE SYNTHESIS FOR image PREDICTION AND ERROR CONCEALMENT

LOCALLY LINEAR EMBEDDING BASED TEXTURE SYNTHESIS FOR IMAGE P...

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19th IEEE international Conference on image processing (ICIP)

作者： Turkan, Mehmet Guillemot, Christine INRIA Campus Univ Beaulieu F-35042 Rennes France

ISBN: (纸本)9781467325332;9781467325349

The template matching algorithm is a simple extension to exemplar-based texture synthesis. Average of template matching predictors or non-local means based approaches can be seen as heuristic extensions to template matching. These methods which linearly combine several texture patches have been shown to be more robust in synthesis and to give better results when compared to simple template matching. However, they do not search to minimize an approximation error on the known pixel values in the template. They are rather heuristic methods for calculating the linear weighting coefficients. This paper proposes a neighbor embedding based texture synthesis method by formulating the problem as a least-squares optimization using locally linear embedding. By this means, one calculates the linear weighting coefficients by solving a constrained optimization for approximating the template. The proposed texture synthesis framework has first been applied to the image prediction (predictive coding) problem. It has then been extended to a loss concealment application for transmission errors. Experimental results demonstrate the effectiveness of the proposed method for both image compression and error concealment.

关键词： Template matching average template matching non-local means locally linear embedding image prediction error concealment

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Study on non-rigid Medical image Registration Based on Optical Flow Model

Study on Non-rigid Medical Image Registration Based on Optic...

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international workshop on image processing and Optical Engineering (IPOE)/international Conference on Information, Computing, and Telecommunications (ICICT)

作者： Lu Xiaoqi Zhao Yongjie Zhang Baohua Ma Hongli Inner Mongolia Univ Sci & Technol Sch Informat Engn Baotou 014010 Peoples R China

ISBN: (纸本)9780819489920

A new method for image registration based on step-by-step registration is proposed. First, SIFT feature algorithm is used to image to get rough registration then optical flow algorithm is used to achieve the final accurate registration. image feature point extraction is the basis of medical image registration and its accuracy impacts on the matching results directly. SIFT feature algorithm is based on local image features and has a good feature of scale, rotation, illumination invariant. Optical flow algorithm no need to do feature extraction and this method calculation is quick and simple and it uses image intensity information directly. The two steps are complementary that the former prepare for the latter to improve the convergence rate while the latter allows a more accurate registration result. The experimental results show that this algorithm can improve the efficiency of non-rigid medical image registration accuracy and increase convergence speed thus it has certain superiority in terms of image registration.

关键词： non-rigid registration medical image step-by-step registration SIFT feature algorithm optical flow algorithm

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Hybrid Differential Evolution using Low-Discrepancy Sequences for image Segmentation

Hybrid Differential Evolution using Low-Discrepancy Sequence...

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26th IEEE international Parallel and Distributed processing Symposium (IPDPS) / workshop on High Performance Data Intensive Computing

作者： Nakib, A. Daachi, B. Siarry, P. Univ Paris Est Creteil Lab Images Signaux & Syst Intelligent LiSSi EA3956 F-94010 Creteil France

ISBN: (纸本)9780769546766

The image thresholding problem can be seen as a problem of optimization of an objective function. Many thresholding techniques have been proposed in the literature and the approximation of normalized histogram of an image by a mixture of Gaussian distributions is one of them. Typically, finding the parameters of Gaussian distributions leads to a nonlinear optimization problem, of which solution is computationally expensive and time-consuming. In this paper, an enhanced version of the classical differential evolution algorithm using low-discrepancy sequences and a local search, called LDE, is used to compute these parameters. Experimental results demonstrate the ability of the algorithm in finding optimal thresholds in case of multilevel thresholding.

关键词： Low discrepancy sequence Simplex Gaussian curve fitting image segmentation differential evolution

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non-rigid image registration using Gaussian mixture models

Non-rigid image registration using Gaussian mixture models

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5th international workshop on Biomedical image Registration, WBIR 2012

作者： Somayajula, Sangeetha Joshi, Anand A. Leahy, Richard M. Dept. of Informatics IT Merck Research Laboratories Boston MA United States Signal and Image Processing Institute University of Southern California Los Angeles CA United States

ISBN: (纸本)9783642313394

non-rigid mutual information (MI) based image registration is prone to converge to local optima due to Parzen or histogram based density estimation used in conjunction with estimation of a high dimensional deformation field. We describe an approach for non-rigid registration that uses the log-likelihood of the target image given the deformed template as a similarity metric, wherein the distribution is modeled using a Gaussian mixture model (GMM). Using GMMs reduces the density estimation step to that of estimating the parameters of the GMM, thus being more computationally efficient and requiring fewer number of samples for accurate estimation. We compare the performance of our approach (GMM-Cond) with that of MI with Parzen density estimation (Parzen-MI), on inter-subject and inter-modality (CT to MR) mouse images. Mouse image registration is challenging because of the presence of a rigid skeleton within non-rigid soft tissue, and due to major shape and posture variability in inter-subject registration. The results show that GMM-Cond has higher registration accuracy than Parzen-MI in terms of sum of squared difference in intensity and dice coefficients of overall and skeletal overlap. The GMM-Cond approach is a general approach that can be considered a semi-parametric approximation to MI based registration, and can be used an alternative to MI for high dimensional non-rigid registration. © 2012 Springer-Verlag.

关键词： image registration

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local stereo matching by joining shiftable window and non-parametric transform

Local stereo matching by joining shiftable window and non-pa...

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6th Multi-Disciplinary international workshop on Artificial Intelligence, MIWAI 2012

作者： Nguyen, Hong Phuc Tran, Thi Dinh Dinh, Quang Vinh University of Science Ho Chi Minh City Viet Nam University of Agriculture and Forestry Ho Chi Minh City Viet Nam Sungkyunkwan University Suwon City Korea Republic of

ISBN: (纸本)9783642354540

In this paper, we propose a blocking-matching approach to the problem of correspondence in stereo matching. In blocking-matching methods, the correspondence or difference between pixels of a stereo pair is measured by a local window. Despite some area-based stereo matching algorithms have been developed and work well in a number of kinds of regions such as textureless or object boundary regions, their performance can debase once working in some sorts of radiometric conditions. Our proposed algorithm, in which non-parametric transform is used in the pre-processing step and an edge-preserving filter is used in the post-processing step, is an improved method of a shiftable window method and can work robustly in various radiometric conditions. Input images first are pre-processed by the census transform that makes our method robustly when the image pair is captured in various light sources or camera uncovering conditions. The window cost in our approach is calculated from the transformed images using in the Hamming distance, and the similarity is finally selected by a Winner-Takes-All strategy. The experimental results for the Middleburry images show that our proposed algorithm outperforms test local stereo algorithms in radiometrically dissimilarity images. © 2012 Springer-Verlag.

关键词： Hamming distance

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Efficient Gaussian filtering using Cascaded Prefix Sums

Efficient Gaussian filtering using Cascaded Prefix Sums

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IEEE international Conference on image processing

作者： John A Robinson Department of Electronics University of York Heslington York UK

This paper introduces a method for multidimensional Gaussian filtering using an efficient one-pass cascade of overlapping local-average windows driven by prefix sums. Each local-average filter is implemented in n dimensions, with non-integer lengths, allowing accurate approximation of Gaussians of any variance. In axis-oriented form the method has a scan-rate hardware realization and fast software implementation using minimal extra memory. In this latter case the new method consistently outperforms the fastest alternative Gaussian filtering method both in accuracy and speed.

关键词： approximation methods Equations Convolution Gabor filters Accuracy Memory management

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On the generalization ability of distributed online learners

On the generalization ability of distributed online learners

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IEEE workshop on Machine Learning for Signal processing

作者： Zaid J. Towfic Jianshu Chen Ali H. Sayed Electrical Engineering Department University of California Los Angeles USA

We propose a fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques. We show that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is the iteration index. In this way, the distributed diffusion strategy, which relies only on local interactions, is able to achieve the same convergence rate as centralized strategies that have access to all data from the nodes at every iteration. We also show that every learner is able to improve its excess-risk in comparison to the non-cooperative mode of operation where each learner would operate independently of the other learners.

关键词： Convergence approximation methods Optimization Vectors Noise approximation algorithms Nickel

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