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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Techniques of image processing and Segmentation in Predicting Hydrocephalus using Magnetic Resonance image 7

Techniques of Image Processing and Segmentation in Predictin...

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7th international conference on advanced Computing and Communication Systems, ICACCS 2021

作者： Rajiga, S.V. Gunasekaran, M. Government Arts College Autonomous Pg and Research Department of Computer Science Salem -7 India Government Arts College Pg and Research Department of Computer Science Dharmapuri India

ISBN: (纸本)9781665405201

Children are most commonly affected by many neurological disorders now a days. One of the common disease is hydrocephalus occurring 1/1000 in infantile age group and also in adults as a result of congenital, acquired, tumors, spina bifida, bleeding or infection. Hydrocephalus may cause disability and even death when it is left untreated. Hydrocephalus occurs when, due to previous causes in the brain, excessive cerebrospinal fluid builds up in the brain. When diagnosing tumours and hydrocephalus, image processing plays a vital role. The gap between the visual representation of data captured by MRI and the information relevant to the individual is a major challenge in the medical field. The latest image processing and data mining technologies are used to classify images with high precision. This research paper suggest a definition of image processing and segmentation algorithms for the evaluation of hydrocephalus in children and the determination of its MRI volume. Earlier research work are intended for identifying Hydrocephalus from CT brain images. But in the proposed work MRI images are used for diagnosis. This paper presents techniques and algorithms of image processing and Segmentation which will be very useful in diagnosing Hydrocephalus. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Multi-channel image Fusion in Ultraviolet Dynamic Detection on Electric Railways 13

Multi-channel Image Fusion in Ultraviolet Dynamic Detection ...

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13th international conference on Graphics and image processing (ICGIP)

作者： Wang, Yan-guo Fang, Jun Qi, Liusheng Xie, Dapeng Li, Liandong Gu, Zichen Liu, Guoyue Tao, Kai Sheng, Liang China Acad Railway Sci Corp Ltd Inst Infrastruct Inspect Beijing Peoples R China China Railway Wuhan Grp Corp Ltd Beijing Peoples R China INMAI Railway Technol Corp Ltd Beijing Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Catenary is an important infrastructure along electric railways, which should be periodically checked to guarantee the normal working status of power supply for the trains. Technologies of ultraviolet detection has been introduced into catenary inspection, and it can be used to detect the electrical state for insulators that are the isolation components between high voltage and low voltage in catenary. However, due to the dynamic detection condition with the running of catenary inspection car, the performance of multi-channel image fusion becomes a key issue in the application on electric railways. In this paper, we proposed a multi-channel image fusion method for the ultraviolet and visible image in dynamic detection on electric railways. The ultraviolet detection module is designed with dual optical coupling alignment, which guarantees the consistency of multi-channel in spatial domain. The post detection is adopted and used as an external trigger signal for both the ultraviolet and visible cameras, which are working in triggered acquisition mode with good time synchronization. Finally, insulator localization algorithms based on deep learning are utilized in the automatic detection of insulator area in visible image, and the ultraviolet signals are successfully extracted from the image fusion results. The performance of multi-channel image fusion method proposed in the paper has been validated, in both lab test and dynamic test on electric railways.

关键词： Visual inspection ultraviolet detection sensor applications and deployments image and video acquisition

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Machine Learning Techniques for Medical image processing 9

Machine Learning Techniques for Medical Image Processing

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9th IEEE international conference on e-Health and Bioengineering (EHB)

作者： Rashed, Baidaa Mutasher Popescu, Nirvana Univ Politech Bucharest Comp Sci Dept Bucharest Romania Univ Thi Qar Nasiriyah Iraq

ISBN: (纸本)9781665440004

In last decade, machine learning (ML) techniques increased the capability to automatically learn the experience without being explicitly programmed. Different machine learning methods are used for a number of tasks such as image processing, predictive analytics, data mining, and are used for classification, regression, clustering, and dimensionality reduction. ML is widely utilized in medical imaging research field. This paper introduces a survey on ML used in medical image processing and it focuses on two main types (supervised and unsupervised learning) to importance them in medical image processing with explains the foremost important algorithms of machine learning, discussing the most important advantages and drawbacks of applying Machine Learning techniques in medical imaging. In addition, some common algorithms were applied like k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Logistic Regression and Random Forest on medical dataset in order to check the efficiency of algorithms.

关键词： Medical images Machine learning techniques Accuracies

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SEMI-SUPERVISED MULTIMODAL image TRANSLATION FOR MISSING MODALITY IMPUTATION

SEMI-SUPERVISED MULTIMODAL IMAGE TRANSLATION FOR MISSING MOD...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Sun, Wangbin Ma, Fei Li, Yang Huang, Shao-Lun Ni, Shiguang Zhang, Lin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781728176055

Missing data is a common problem in multimodal and multi-view learning. It raises a critical challenge for most multimodal algorithms, which are unable to deal with incomplete datasets. Rather than discarding entries with missing modalities, this paper aims to reconstruct the complete image-based multimodal data by imputing missing modalities. We solve the imputation problem as an image translation task, which transforms images in one domain to other domains. Existing image translation techniques either can not fully utilize the information contained in partially complete entries or are limited to the bimodal situation. We propose a semi-supervised algorithm for multimodal learning with missing data, namely Cyclic Autoencoder (CycAE). Specifically, a novel cyclical structure, as well as the correlation among modalities, is integrated to leverage information from complete entries to incomplete ones. Experiments on two multimodal datasets show that our model outperforms state-of-the-art models. Downstream tasks can also benefit from the completed datasets.

关键词： Missing data Multimodal representation learning image translation

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Research on Haze Traffic signal image processing Based on Retinex Algorithm 2

Research on Haze Traffic Signal Image Processing Based on Re...

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2021 2nd international conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Qin, Lijuan He, Xinyu Liu, Tao School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9783800757398

The signal lamp is affected by the haze weather conditions, and the color of the signal lamp image taken will be disturbed to a certain extent. Before color threshold segmentation, it is necessary to restore and enhance the color of the signal lamp to a certain extent, so as to ensure the accuracy and real-time performance of color segmentation and subsequent recognition. Retinex algorithm is an image enhancement theory based on human vision system. In recent years, it has been widely studied and applied. In this paper, SSR, MSR and MSRCR algorithms are introduced in detail, and these three methods are applied to haze traffic signal image enhancement. The experimental results of image processing are given, and the images have been enhanced to different degrees, and the experimental results are good. Traffic signal processing and recognition is one of the key technologies and difficulties of intelligent vehicle unmanned driving, so the research of this paper has important practical application value. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： image enhancement

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SELF-SUPERVISED DISENTANGLED EMBEDDING FOR ROBUST image CLASSIFICATION

SELF-SUPERVISED DISENTANGLED EMBEDDING FOR ROBUST IMAGE CLAS...

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IEEE international conference on image processing (ICIP)

作者： Liu, Lanqing Duan, Zhenyu Xu, Guozheng Xu, Yi Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665441155

Recently, the security of deep learning algorithms against adversarial samples has been widely recognized. Most of the existing defense methods only consider the attack influence on image level, while the effect of correlation among feature components has not been investigated. In fact, when one feature component is successfully attacked, its correlated components can be attacked with higher probability. In this paper, a self-supervised disentanglement based defense framework is proposed, providing a general tool to disentangle features by greatly reducing correlation among feature components, thus significantly improving the robustness of the classification network. The proposed framework reveals the important role of disentangled embedding in defending adversarial samples. Extensive experiments on several benchmark datasets validate that the proposed defense framework consistently presents its robustness against extensive adversarial attacks. Also, the proposed model can be applied to any typical defense method as a good promotion strategy.

关键词： Disentanglement Adversarial Examples Robustness

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HVS-BASED PERCEPTUAL COLOR COMPRESSION OF image DATA

HVS-BASED PERCEPTUAL COLOR COMPRESSION OF IMAGE DATA

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Prangnell, Lee Sanchez, Victor Univ Warwick Dept Comp Sci Coventry W Midlands England

ISBN: (纸本)9781728176055

In perceptual image coding applications, the main objective is to decrease, as much as possible, Bits Per Pixel (BPP) while avoiding noticeable distortions in the reconstructed image. In this paper, we propose a novel perceptual image coding technique, named Perceptual Color Compression (PCC). PCC is based on a novel model related to Human Visual System (HVS) spectral sensitivity and CIELAB Just Noticeable Color Difference (JNCD). We utilize this modeling to capitalize on the inability of the HVS to perceptually differentiate photons in very similar wavelength bands (e.g., distinguishing very similar shades of a particular color or different colors that look similar). The proposed PCC technique can be used with RGB (4:4:4) image data of various bit depths and spatial resolutions. In the evaluations, we compare the proposed PCC technique with a set of reference methods including Versatile Video Coding (VVC) and High Efficiency Video Coding (HEVC) in addition to two other recently proposed algorithms. Our PCC method attains considerable BPP reductions compared with all four reference techniques including, on average, 52.6% BPP reductions compared with VVC (VVC in All Intra still image coding mode). Regarding image perceptual reconstruction quality, PCC achieves a score of SSIM >= 0.99 in all tests in addition to a score of MS-SSIM >= 0.99 in all but one test. Moreover, MOS = 5 is attained in 75% of subjective evaluation assessments conducted.

关键词： Perceptual image Coding Visually Lossless Compression Human Visual System HEVC VVC

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Batch Wafer-Identification Recognition Based on Machine Learning 6

Batch Wafer-Identification Recognition Based on Machine Lear...

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6th international conference on Communication, image and signal processings, CCISP 2021

作者： Cao, Fang Tian, Zengguo Jiang, Baozhu Zhang, Hongshuai Chen, Heng Zhu, Xuguang School of Physics and Microelectronics Zhengzhou University Zhengzhou China School of Mechanical and Power Engineering Zhengzhou University Zhengzhou China Luoyang Dejing Intelligent Technology Co. Ltd Luoyang China

ISBN: (纸本)9781665432795

Wafer identification (ID) is essential for wafer fabrication. Wafer ID can be used to associate production-process data in a manufacture-execution system, which helps product-quality tracing. The related work mainly focuses on single wafer-ID recognition, which requires the wafer to be taken out and placed on a specific platform, resulting in low efficiency. To speed up recognition, we propose a method of batch wafer-ID recognition with a customized optical scheme, which can capture the whole image of a cassette of wafers at once. The proposed scheme does not need to take out the wafers from cassette and avoids the risk of contamination. Based on this scheme, a batch ID recognition algorithm is presented for image preprocessing, undistortion, segmentation, and recognition. We evaluate various image features and classification algorithms for wafer-ID recognition. The experimental results show that a learning-based method like K-Nearest Neighbor using grid feature can achieve high accuracy with high speed, greatly improving the efficiency of wafer-ID recognition. © 2021 IEEE.

关键词： image segmentation

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ACCURATE COMPENSATION MAKES THE WORLD MORE CLEAR FOR THE VISUALLY IMPAIRED

ACCURATE COMPENSATION MAKES THE WORLD MORE CLEAR FOR THE VIS...

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IEEE international conference on image processing (ICIP)

作者： Wu, Sijing Duan, Huiyu Min, Xiongkuo Tu, Danyang Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665441155

Visual impairment is one of the most serious social and public health problems in the world, therefore, it is of great theoretical and practical significance to study the image enhancement algorithms for the visually impaired, which is the basis for the development of assistive devices. In this paper, a general deep learning based image enhancement framework for the visually impaired is proposed, which can be used to enhance images to compensate for any visually impaired symptom that can be modeled. Take central vision loss as an example, we first model the central vision loss based on the contrast sensitivity function (CSF) specified by clinical indicator Pelli-Robson score and logMAR visual acuity, and then use the proposed framework to generate an image enhancement method aiming at compensating for the central vision loss. Both the simulation experiment and the patient experiment show the superiority of the proposed image enhancement method designed for the central vision loss, which also validates the effectiveness of the proposed framework.

关键词： Visual Impairment Assistance image Enhancement Perceptual image Simulation Deep Learning Convolutional Neural Network

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Development of Wavelet Transform Based image Fusion Technique With Improved PSNR for CT and PET images in Comparison With Discrete Cosine Transform Based image Fusion Technique 1

Development of Wavelet Transform Based Image Fusion Techniqu...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Luminasree, B. Nanmaran, R. Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9781607685395

The image fusion process is characterised as gathering all relevant data from multiple images and combining it into a smaller number of images, typically only *** aim of image fusion is to merge data from multiple images of the same scene into a single image that, in theory, contains all of the original images' important features. As a result, the resulting fused image would be better suited to human and machine perception as well as image processing tasks. In this work, wavelet transform based image fusion technique was proposed and developed to improve the quality of *** proposed work is compared with Discrete cosine transform based image fusion technique using peak signal to noise ratio(PSNR) and universal image quality index (UIQI) values and sample size for each group is *** threshold value is set to 0.05 and the confidence interval as 95%. The performance of image fusion of both algorithms are measured by using parametres Peak signal to noise ratio(PSNR) and universal index quality measure(UIQI).High values of PSNR and UIQI indicates the better image *** wavelet transform provides mean value of PSNR (15.8460 dB),mean value of UIQI (0.1966%) and discrete cosine transform provides the mean value of PSNR (15.8783 dB),mean value of UIQI (0.0802%).The insignificant values of PSNR 1 (P=0.894), PSNR 2(P=0.643),UIQI(P=0.057). Based on the experimental results,it is observed that the discrete wavelet transform based image fusion method performs better discrete cosine transform. © The Electrochemical Society

关键词： Artificial intelligence Discrete cosine transform Discrete wavelet transform Innovative image fusion Peak signal to noise ratio Universal image quality index

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