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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

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An Experiment with statistical Region Merging and Seeded Region Growing image Segmentation Techniques 2nd

An Experiment with Statistical Region Merging and Seeded Reg...

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2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Deshpande, Anuja Dahikar, Pradeep Agrawal, Pankaj Department of Electronics Kamla Nehru Mahavidyalaya Sakkardara NagpurMaharashtra440024 India NagpurMaharashtra440028 India

ISBN: (纸本)9789811391804

This paper puts forth our observations from the experiments conducted on interactive segmentation techniques - statistical Region Merging and Seeded Region Growing, both based on Region Growing methods, using Matlab software on selected natural images. Both these techniques meant to solve segmentation challenges posed by medical images and were effective as well;however, in this experiment, we have subjected natural images to these techniques to assess the effectiveness. We have chosen the images such that those have complexities and pose segmentation challenges. The objective of this experiment is to understand and assess the effectiveness of these techniques on select natural images, which have complex image composition in terms of intensity, color mix, indistinct object boundary, low contrast, etc. We have used Jaccard Index, Dice Coefficient and Hausdorff Distance as measures to assess the accuracy, besides visual assessment. We have compared the segmented images with ground truth using these accuracy measures. While both the techniques failed to give acceptable results on most of the images, relatively, we have found statistical Region Merging to be slightly better than Seeded Region Growing method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

Unsupervised Feedforward Feature (UFF) Learning for Point Cl...

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IEEE Visual Communications and image processing (VCIP)

作者： Min Zhang Pranav Kadam Shan Liu C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles CA USA Tencent Media Lab Tencent America Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification and segmentation of 3D point clouds is proposed in this work. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture. It learns global shape features through the encoder and local point features through the concatenated encoder-decoder architecture. The extracted features of an input point cloud are fed to classifiers f or shape classification and part segmentation. Experiments are conducted to evaluate the performance of the UFF method. For shape classification, the UFF is superior to existing unsupervised methods and on par with state-of-the-art DNNs. For part segmentation, the UFF outperforms semi-supervised methods and performs slightly worse than DNNs.

关键词： image segmentation Three-dimensional displays Correlation Shape Visual communication Feature extraction Feedforward neural networks

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Markovian Segmentation Of Textured Color images

Markovian Segmentation Of Textured Color Images

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Intelligent Systems and Computer Vision (ISCV)

作者： Meryem Ameur Cherki Daoui Najlae Idrissi Laboratory of Information Processing and Decision Support Faculty of Sciences and Techniques Sultan Moulay Slimane University BeniMellal Morrocco

ISBN: (数字)9781728180410

ISBN: (纸本)9781728180427

This paper presents an application of textured color images segmentation using hidden Markov chain model. We propose two comparative studies, The first one is between EM(Expectation-Maximization) algorithm,SEM (stochastic Expectation-Maximization) algorithm and ICE (Iterative Conditional Estimation) algorithm, these estimators are used to estimate the parameters of Hidden Markov Chain with Independent Noise. The second one is between MAP(Maximum a Posteriori) and MPM(Maximum Marginal Posteriori) algorithms. These strategies are used to estimate the resulted images of segmentation. The obtained results show that EM algorithm, SEM, and ICE algorithm give the same results of segmentation under MAP and MPM algorithms. But, MPM provides better segmentation results than MAP.

关键词： Hidden Markov models image segmentation Ice image color analysis Markov processes Bayes methods

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A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface

A 3D Non-Stationary Channel Model for 6G Wireless Systems Em...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Yingzhuo Sun Cheng-Xiang Wang Jie Huang Jun Wang National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

As one of the key technologies for the sixth generation (6G) mobile communications, intelligent reflecting surface (IRS) has the advantages of low power consumption, low cost, and simple design methods. But channel modeling is still an open issue in this field currently. In this paper, we propose a three dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing IRS. The model supports the movements of the transmitter, the receiver, and clusters. The evolution of clusters on the linear array and planar array is also considered in the proposed model. In addition, the generation of reflecting coefficient is incorporated into the model and the path loss of the sub-channel assisted by IRS is also proposed. The steering vector is set up at the base station for the cooperation with IRS. Through studying statistical properties such as the temporal autocorrelation function and space correlation function, the nonstationary properties are verified. The good agreement between the simulation results and the analytical results illustrates the correctness of the proposed channel model.

关键词： Indexes Wireless communication Signal processing conferences

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Deep learning based image classification and abnormalities analysis of MRI brain images 1

Deep learning based image classification and abnormalities a...

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1st International conference on Microwave Integrated Circuits, Photonics and Wireless Networks, IMICPW 2019

作者： Muthu Krishnammal, P. Selvakumar Raja, S. Sathyabama Institute of Science and Technology Faculty of Electronics Tamil Nadu India Khakathiya Institute of Science and Technology for Women Nizamabad India

ISBN: (纸本)9781728110349

Intensive researches are being carried out to study the abnormalities present in the brain structures and to detect the type of tumors based on the statistical and textural features extracted from the medical images. In MRI imaging, the images may be clear but the clinicians have to quantify the size and location of the tumors for further treatment planning. The image processing methodologies and the deep learning methods aid the different stages of treatment such as pre and postsurgical procedures. The images captured by Magnetic Resonance Imaging systems are processed by the different software based algorithms in order to segregate the malicious tumor regions from the non-tumor regions. The proposed method includes three phases: feature extraction, image classification and segmentation of tumors. The important features of the images are extracted with Wavelet transform, a multi-resolution technique. The Convolutional Neural Network, which is a very popular deep learning method is used in image classification stage which is helpful in disease or lesion detection, image classification and then the segmentation methods are used to segregate the infected regions from the rest. The proposed method provides better accuracy in classification and segmentation stages. © 2019 IEEE.

关键词： Tumors

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SAR image Specle Reduction based on a Generative Adversarial Network

SAR Image Specle Reduction based on a Generative Adversarial...

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International Joint conference on Neural Networks (IJCNN)

作者： Ruijiao Liu Yangyang Li Licheng Jiao School of Artificial Intelligence Xidian University Xian China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Synthetic aperture radar (SAR) image despeckling is recognized as the basis for SAR image processing and interpretation. Over the past decades, many impressed speckle reduction methods have been developed and achieved good performance under certain circumstances. However, how to suppress speckle noise in a homogeneous region while more effectively protecting details and avoid distortion of data features caused by homomorphic transformation is still an urgent problem. In this paper, a novel speckle reduction algorithm based on generative adversarial network (GAN) is proposed, which contains a generator and a discriminator. For the generator that is used directly for subsequent noise reduction, a total variation (TV) loss function is added. Meanwhile, we directly learn the mapping between the input image and the ground truth rather than the logarithmic transformation. Indeed, the improved lightweight discriminative network will also provide learning guidance for the generator. Experiments on simulatedSAR images and real SAR images demonstrate the improvement in visual and statistical performance comparing to the state-of-the-art despeckling algorithms.

关键词： Speckle Generators Generative adversarial networks Gallium nitride Synthetic aperture radar TV Transforms

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On Finding Gray Pixels 32

On Finding Gray Pixels

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32nd IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qian, Yanlin Kamarainen, Joni-Kristian Nikkanen, Jarno Matas, Jiri Tampere Univ Comp Sci Tampere Finland Intel Finland Espoo Finland Czech Tech Univ Ctr Machine Percept Prague Czech Republic

ISBN: (纸本)9781728132938

We propose a novel grayness index forfinding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illuminationsources in color-biasedimages. On standardsingleillumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statisticalmethods and many recent deep methods. GI is simple andfast, written in afew dozen lines of code, processing a 1080p image in - 0.4 seconds with a non-optimized Matlab code.

关键词： Computational Photography Low-level Vision statistical Learning

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Why is the Winner the Best?

Why is the Winner the Best?

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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PADDIT: Probabilistic Augmentation of Data using Diffeomorphic image Transformation

PADDIT: Probabilistic Augmentation of Data using Diffeomorph...

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conference on Medical Imaging: image processing

作者： Orbes-Arteaga, Mauricio Sorensen, Lauge Cardoso, Jorge Modat, Marc Ourselin, Sebastien Sommer, Stefan Nielsen, Mads Igel, Christian Pai, Akshay Univ Copenhagen DIKU Copenhagen Denmark Biomediq AS Copenhagen Denmark Cerebriu AS Copenhagen Denmark Kings Coll London London England UCL London England

ISBN: (纸本)9781510625464

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic image Transformation (PADDIT) - a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. The main advantage of PADDIT is the ability to produce transformations that capture the morphological variability in the training data. To this end, PADDIT constructs a mean template which represents the main shape tendency of the training data. A Hamiltonian Monte Carlo(HMC) scheme is used to sample transformations which warp the training images to the generated mean template. Augmented images are created by warping the training images using the sampled transformations. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation (0.2 and 0.15 higher dice accuracy respectively) in segmenting white matter hyperintensities from T1 and FLAIR brain MRI scans.

关键词： image segmentation Brain Convolutional neural networks Neuroimaging Magnetic resonance imaging Medical imaging Monte Carlo methods statistical analysis Data modeling image registration

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Simultaneous Denoising and Deblurring by Non-Convex Regularization: Alternating Minimization Framework

Simultaneous Denoising and Deblurring by Non-Convex Regulari...

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International conference on Communications and Signal processing

作者： H N Latha Rajiv R. Sahay H N Poornima Department of Electronics and communication Engineering BMS college of Engineering Bangalore India Department of Electrical Engineering IIT Kharagpur computer science Department Global Academy Bangalore

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

In this paper, we have addressed the problem associated with simultaneous denoising and deblurring of a degraded image. We have implemented Discontinuity Adaptive Markov Random Field (DAMRF) algorithm along with a nonlocal based filtering technique. The image under consideration is degraded by Additive White Gaussian Noise (AWGN) and blur. We implemented a space-invariant blind deconvolution to deblur the image using DAMRF regularization frame work. Latent focused image along with the blur map sigma of the space-invariant point spread function (PSF) is estimated by regularization based optimization techniques. The problem is critically ill-posed as well as inverse hence it has several solutions. We exploit a recently proposed method with a MAP-DAMRF energy minimization to estimate latent image followed by a blur map sigma. statistical analyses and derivations are carried out on effective priors, by utilizing the methods of non-local neighborhood pixels information (for denoising) and local neighbor-hood pixels information (for deblurring). Objective of our proposed work about alternating energy minimization is successfully optimized by implementing the gradient descent method. We showed that the performance metric of DAMRF prior is better than Gauss-Markov random field (GMRF) prior. Edges, fine details and structure information of 2D image generated by the DAMRF regularizer is better than few state-of-the-art work. Comparability of both PSNR and SSIM evaluation of our work with presented.

关键词： Noise measurement Estimation Noise reduction Minimization image restoration Two dimensional displays Imaging

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