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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16439 条记录，以下是4841-4850 订阅

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Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm

Simple and Efficient Clustering Approach Based on Cuckoo Sea...

引用

International conference on Intelligent Computing in Data Sciences (ICDS)

作者： Lahbib KHRISSI Nabil EL AKKAD Hassan SATORI Khalid SATORI LISAC Faculty of Sciences Dhar-Mahraz (FSDM) Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory of Engineering Systems and Applications ENSA of Fez.

ISBN: (数字)9781728180847

ISBN: (纸本)9781728180854

image segmentation is the most important operation in the image processing system because it is located at the articulation between image processing and analysis. The advantage of segmentation is to partition an image into several homogeneous regions, within the meaning of a criterion fixed a priori. A multitude of segmentation methods are proposed in the literature, but there is no universal image segmentation technique to apply to all different types of images and in any given computer context. Because of these constraints, in this paper, we will propose a new image segmentation approach which is based on the hybridization of an unsupervised classification method which is fuzzy C-means (FCM) and a metaheuristic which is called Cuckoo Search Algorithm (CSA). In our proposed approach, the cuckoo search is used to find the optimal partitioning according to an objective function which is based on the indices of validity of the clusters. First, CSA is initialized with random cluster centers. The cluster centers are then updated using the CSA principles aimed at minimizing the objective function proposed. The performance of the proposed approach was measured on several images and compared to other existing FCM techniques such as standard FCM and FCM based on genetic algorithms (FCM-GA). The experimental results show that the proposed approach yields satisfactory results in terms of precision, simplicity and efficiency.

关键词： Clustering algorithms image segmentation Classification algorithms Partitioning algorithms Optimization Birds Linear programming

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Uncertainty-driven loss for single image super-resolution 21

Uncertainty-driven loss for single image super-resolution

引用

Proceedings of the 35th International conference on Neural Information processing systems

作者： Qian Ning Weisheng Dong xin Li Jinjian Wu Guangming Shi School of Artificial Intelligence Xidian University Xi'an China Lane Dep. of CSEE West Virginia University Morgantown WV

ISBN: (纸本)9781713845393

In low-level vision such as single image super-resolution (SISR), traditional MSE or L1 loss function treats every pixel equally with the assumption that the importance of all pixels is the same. However, it has been long recognized that texture and edge areas carry more important visual information than smooth areas in photographic images. How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty. Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously. Moreover, uncertainty estimation allows us to leverage conventional wisdom such as sparsity prior for regularizing SISR solutions. Ultimately, pixels with large certainty (e.g., texture and edge pixels) will be prioritized for SISR according to their importance to visual quality. For the first time, we demonstrate that such uncertainty-driven loss can achieve better results than MSE or L1 loss for a wide range of network architectures. Experimental results on three popular SISR networks show that our proposed uncertainty-driven loss has achieved better PSNR performance than traditional loss functions without any increased computation during testing.

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Astronomical image Restoration and Point Spread Function Estimation with Deep Neural Networks

Astronomical Image Restoration and Point Spread Function Est...

引用

conference on Advances in Optical Astronomical Instrumentation

作者： Jia, Peng Wu, Xuebo Yang, xiaoshan Huang, Yi Cai, Bojun Cai, Dongmei Taiyuan Univ Technol Coll Phys & Optoelect Taiyuan 030024 Peoples R China Univ Durham Dept Phys South Rd Durham DH1 3LE England Taiyuan Univ Technol Minist Educ & Shanxi Prov Key Lab Adv Transducers & Intelligent Control Sys Taiyuan 030024 Peoples R China

ISBN: (纸本)9781510631472

From ground-based extremely large telescopes to small telescope arrays used for time domain astronomy, point spread function plays an important role both for scientific data post-processing and instrument performance estimation. In this paper, we propose a new method which can restore astronomical images and obtain the point spread function of the whole optical system at the same time. Our method uses simulated high resolution astronomical images and real observed blurred images to train a deep neural network (Cycle-GAN). The Cycle-GAN contains a pair of generative adversarial neural networks and each generative adversarial neural network contains a generator and a discriminator. After training, one generator (PSF-Gen) can learn the point spread function and the other generator (Dec-Gen) can learn the deconvolution kernel. We test our method with real observation data from solar telescope and small aperture telescopes. We find that the Dec-Gen can give promising restoration results for solar images and can reduce the PSF spatial variation for images obtained by smaller telescopes. Besides, we also find that the PSF-Gen can provide a non-parametric PSF model for short exposure images, which would then be used as prior model for PSF reconstruction algorithms in adaptive optics systems.

关键词： Point spread function reconstruction Deep neural network Cyclegan image restoration

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Waste Classification System Using image processing and Convolutional Neural Networks 15th

Waste Classification System Using Image Processing and Convo...

引用

15th International Work-conference on Artificial Neural Networks (IWANN)

作者： Bobulski, Janusz Kubanek, Mariusz Czestochowa Tech Univ Inst Comp & Informat Sci Dabrowskiego St 69 PL-42201 Czestochowa Poland

ISBN: (纸本)9783030205188;9783030205171

image segmentation and classification is more and more being of interest for computer vision and machine learning researchers. Many systems on the rise need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. The use of convolutional neural networks for the image classification and recognition allows building systems that enable automation in many industries. This article presents a system for classifying plastic waste, using convolutional neural networks. The problem of segregation of renewable waste is a big challenge for many countries around the world. Apart from segregating waste using human hands, there are several methods for automatic segregation. The article proposes a system for classifying waste with the following classes: polyethylene terephthalate, high-density polyethylene, polypropylene and polystyrene. The obtained results show that automatic waste classification, using image processing and artificial intelligence methods, allows building effective systems that operate in the real world.

关键词： Deep learning Waste management image processing

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Data Flow Mapping onto DNN Accelerator Considering Hardware Cost

Data Flow Mapping onto DNN Accelerator Considering Hardware ...

引用

Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

作者： Baharealsadat Parchamdar Midia Reshadi Islamic Azad University Tehran Iran

ISBN: (数字)9781728185101

ISBN: (纸本)9781728185118

Deep Neural Networks (DNNs) has created outstanding results in computer vision and image processing recently. Computational algorithms complexity has led to widespread research in this field. Besides, using a DNN accelerator is an effective method in order to hasten the computation in those algorithms. The majority of DNN accelerator use parallelism processing Elements (PEs) in order to lessen hardware costs. Regarding high volume of input data in image processing, lessening computing runtime is considered as an open challenge in DNNs accelerator. All above-mentioned issues have created different data flow mapping method in DNNs accelerator. This paper presents the new data flow mapping on Resnet34, regarding stationaries concepts; moreover, evaluates computing runtime in each step of mapping. We also propose a new data flow mapping method based on weight stationary in Resnet34, which reduces computing runtime, by 50.6%.

关键词： Computer vision Runtime image processing Neural networks Parallel processing Hardware Proposals

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Unsupervised Machine Learning Algorithm for MRI Brain image processing 7th

Unsupervised Machine Learning Algorithm for MRI Brain Image ...

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International conference on Soft Computing for Problem Solving (SocProS)

作者： Rani, S. Saradha Rao, G. Sasibhushana Rao, B. Prabhakara GITAM Univ Dept Elect & Commun Engn Visakhapatnam Andhra Pradesh India AU Coll Engn Visakhapatnam Andhra Pradesh India JNTUK Kakinada 533003 Andhra Pradesh India

ISBN: (纸本)9789811315923;9789811315916

Denoising of an image is the first and primary pre-processing step in image processing. In this paper, an algorithm is implemented using machine learning in conjunction with wavelet-based denoising method. Most learning algorithms use activation function that is continuously differentiable. Since standard threshold functions are weakly differentiable, a new type of thresholding function was proposed. Stein's unbiased risk estimate (SURE)-based updating algorithm is used for estimation. The proposed method is compared with conventional filtering andwavelet-based denoising methods, using performance evaluators like PSNR and MSE. Results indicate there is a significant reduction in MSE and increase in PSNR for the proposed method.

关键词： image denoising SURE Thresholding Unsupervised learning

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An Opinion-unaware Blind Quality Assessment Algorithm for Multiply Distorted images 11

An Opinion-unaware Blind Quality Assessment Algorithm for Mu...

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11th International conference on Signal processing systems (ICSPS)

作者： Wang, Tongle Deng, Junchen East China Normal Univ Dept Comp Sci & Technol 3663 Zhongshan North Rd Shanghai Peoples R China

ISBN: (纸本)9781510635463

The blind image quality assessment algorithms produced every year are mostly "opinion-aware" (OA). It means that they require large numbers of subjective quality scores for regression model training. Subjective quality scores are not easily available, so people are eager to design an opinion-unaware (OU) algorithm which has free subjective quality scores. Besides, the OU algorithm has greater generalization capability than the OA algorithm. Therefore, we propose an OU algorithm based on a visual codebook for multiply distorted image quality assessment. Extensive experiments conducted on the three databases demonstrate that the proposed method is superior to the existing five OU methods in terms of the coherence with the human subjective rating.

关键词： image quality assessment human visual system multiply distorted blind/no-reference

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A New Evaluation Method for Medical image Information Hiding Techniques*

A New Evaluation Method for Medical Image Information Hiding...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Peter Eze Udaya Parampalli Robin Evans Dongxi Liu Computing and Information Systems University of Melbourne Melbourne Australia Distributed and Software Systems Data61 CSIRO Sydney Australia

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.

关键词： Watermarking Medical diagnostic imaging Heuristic algorithms Diseases Distortion

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Fused image Separation with Scatter Graphical Method 4th

引用

4th International conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Sharma, Mayank Satya Prakash Tomar, Ranjeet Singh Paliwal, Nikhil Shrivastava, Prashant Rustan Ji Inst Technol Tekanpur India ITM Univ Gwalior Gwalior India

ISBN: (纸本)9789811307614;9789811307607

image fusion and its separation is a frequently arising issue in image processing field. In this paper, we have described image fusion and its Separation using Scatter graphical method and Joint Probability Density Function. Fused image separation using Scatter Graphical Method depend on Joint Probability density function of fused image. This technique gives batter result of other technique based on Signal Interference ratio and peak signal-to-noise ratio.

关键词： Real image Scatter BSS PSNR SIR real mixture

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Improved fuzzy clustering with swarm intelligence for medical image analysis

Improved fuzzy clustering with swarm intelligence for medica...

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International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Fateme Gholami Faculty of Computer Engineering and Information Technology Sadjad University of Technology Mashhad Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

One of the challenges in the world today is the existence of a variety of diseases, some of which require the processing of medical images to diagnose and evaluate, such as images of brain tumors. One of the methods of analyzing and evaluating patients related to the brain is magnetic resonance imaging(MRI). Data mining methods such as clustering can be used to analyze magnetic resonance images. Clustering techniques can take the area of brain tumors from brain tissue and use it to diagnose disease. Various clustering methods have been proposed so far, one of which is the fuzzy clustering or FCM method, and it has a high accuracy for clustering and segmentation of brain tissues. Fuzzy clustering is less sensitive to the noise in these images and therefore its segmentation accuracy is somewhat desirable. To improve the performance of FCM clustering, in identifying the edges and borders of tumors, it is necessary to select the optimal clustering centers. The optimal selection of cluster centers increases its accuracy in learning and segmentation. Given that the optimal selection of cluster centers is an optimization method, metaheuristic algorithms can be used for this purpose. In this research, swarm intelligence algorithms have been used to optimally select cluster centers in FCM. The analysis of the proposed method on a set of images of brain magnetic resonance shows that the proposed algorithm has the specificity, sensitivity, and accuracy of 96.87%, 88.36%, and 91.32% in the diagnosis of brain tumors, respectively. The proposed method of hybrid methods, such as the fuzzy method, better detects brain tumors.

关键词： image segmentation Brain Magnetic resonance Clustering algorithms Signal processing algorithms Tumors Diseases

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