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16th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2019

作者： Xu, Shuning Hu, Menghan Yu, Wangyang Feng, Jianlin Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China

ISBN: (纸本)9789811533402

In medical diagnostic analysis, pathological microscopic image is often regarded as a gold standard, and hence the study of pathological microscopic image is of great necessity. High quality microscopic pathological images enable doctors to arrive at correct diagnosis. The pathological microscopic image is an important cornerstone for modernization and computerization of medical procedures. The quality of pathological microscopic images may be degraded due to a variety of reasons. It is difficult to acquire key information, so research for quality assessment of pathological microscopic image is quite necessary. In this paper, we perform a study on subjective quality assessment of pathological microscopic images and investigate whether the existing objective quality measures can be applied to the pathological microscopic images. Concretely, we establish a new pathological microscopic image quality database (PMIQD) which includes 425 pathological microscopic images with different quality degrees. The mean opinion scores rated by nonexperts and experts are calculated afterwards. Besides, we investigate the prediction performance of the existing popular image quality assessment (IQA) algorithms on PMIQD, including 8 no-reference (NR) methods. Experimental results demonstrate that the present objective models do not work well. IQA for pathological microscopic image needs to be developed for predicting the quality rated by nonexperts and experts. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Enhancement of images from Industrial X-Ray Computed Tomography systems by Hybrid Approach 19th

Enhancement of Images from Industrial X-Ray Computed Tomogra...

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19th International Symposium for Production Research, ISPR 2019

作者： Sokac, Mario Santosi, Zeljko Vukelic, Djordje Katic, Marko Durakbasa, M. Numan Budak, Igor Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia Vienna Austria

ISBN: (纸本)9789811509490

Application of the computed tomography (CT) within industry has been rising in recent years due to its non-destructive abilities and accuracy. Nevertheless, there are some challenges related to CT scanning, such as presence of artefacts. The aim of this research is to investigate to what extent the application of some advanced algorithms can influence the accuracy of the X-ray CT images. In this paper, after a brief overview of different existing methods used for reduction of different types of artefacts, preliminary research of a new approach for CT image enhancement is presented. It is based on a hybrid methodology using two different methods - Fuzzy Clustering and Region Growing - joined in order to exploit their advantages. Results show that the proposed methodology contributes to CT image enhancement, with borders of segmented objects on CT images more easily extracted. © 2020, Springer Nature Switzerland AG.

关键词： Artefacts Computed tomography image processing Industrial CT images X-ray CT

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Region-based seam optimization for image stitching

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JOURNAL OF ELECTRONIC IMAGING 2019年第4期28卷

作者： He, Chu Li, Yongchao Zhang, Yifan Liao, Mingsheng Wuhan Univ Elect Informat Sch Wuhan Hubei Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China Collaborat Innovat Ctr Geospatial Technol Wuhan Hubei Peoples R China

Parallax disposition is a challenging task in image stitching. Thus far, the most effective method for parallax processing is the seam-driven method. However, its ability to handle complex textures is poor. To apply the seam-driven method to more scenes, we have incorporated the idea of spatially varying warps. Before finding the optimal seam, we try to register the overlapping area in as projective a manner as possible. Meanwhile, we set some constraints for the registered image to preserve the original appearance as much as possible. Among the constraints, we introduce local features to further improve the stitched image. After registration, we use the maximum flow method to obtain the optimal seam and introduce a series of seam evaluation systems. On one hand, these systems evaluate whether the resulting seam is misaligned, and on the other hand, they provide feedback to the previous registration, increasing the weights of the feature points near the dislocation point to fine-tune the seam. Comparisons with existing image stitching algorithms demonstrate the effectiveness and universality Of Our approach. (C) 2019 SPIE and IS&T

关键词： parallax region features seam alignment weight

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Fractional Contrast Stretching for image Enhancement of Aerial and Satellite images 18

Fractional Contrast Stretching for Image Enhancement of Aeri...

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18th image processing: algorithms and systems Conference, IPAS 2020

作者： Trongtirakul, Thaweesak Chiracharit, Werapon Imberman, Susan Agaian, Sos Department of Electronic and Telecommunication Faculty of Engineering King Mongkut’s University of Technology Thonburi 126 Pracha Uthit Rd Bang Mot Thung Khru Bangkok10140 Thailand Department of Computer Science College of Staten Island City University of New York 2800 Victory Blvd. Staten IslandNY10314 United States Department of Computer Science City University of New York New York CityNY United States

Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. This is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details and local contrast at the same visualization level. They are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform brightness, halo effect, blur edges, and so on. This article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. The introduced approach contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. The proposed algorithm was tested on the orthorectified aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000–2016. The simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization quality. c Society for Imaging Science and Technology 2019

关键词： Antennas

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A method of forest-fire image recognition based on adaboost-BP algorithm

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International Journal of Circuits, systems and Signal processing 2019年 13卷 312-319页

作者： Ding, Dehong Zhu, Sisi College of Compter Hunan University of Arts and Science Changde415000 China College of Electrical and Information Engineering Hunan University Changsha410082 China Institute of Mechanical and Electrical Engineering Hezhou University HezhouGuanxi542899 China

In view of the shortcomings of the existing forest flame recognition technology, in order to further improve and perfect some problems of the flame image recognition technology, provide guidance and technical support for fire detection products. The research is focus on flame information of physical characteristics, which combines with image processing techniques and comes up to a recognition way of forest flame that character of highlight area based on image and a flame recognition method of BP neural network for forest fire based on AdaBoost. AdaBoost-BP method uses the BP neural network as the basic weak classifier, and then updates it through the specific algorithm, and constructs a combination of strong classifier. Those methods are mainly extracting Color, Area and Circularity of pretreatment image, then output AdaBoost-BP neural network learning to realize firework recognition. Using single algorithms and single characteristic to analyze, easily led to accuracy is not high. It not only can make up for that shortcoming but also eliminate interference interruption, and improves efficiency of image recognition for *** recognition rate is about 94%, and the training time is about 250 times to achieve convergence. The computation speed is only 0.01292 seconds. © 2019, North Atlantic University Union. All rights reserved.

关键词： Adaptive boosting

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Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

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INTERNATIONAL JOURNAL OF IMAGING systems AND TECHNOLOGY 2019年第4期29卷 483-490页

作者： Guo Qingrong Jia Zhenhong Yang Jie Kasabov, Nikola Xingjiang Univ Coll Informat Sci & Engn Sheng Li Rd 666 Urumqi 830046 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Patter Recognit Shanghai Peoples R China Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand

Noises and artifacts are introduced in medical images during the process of imaging and transmission, resulting in reduced definition and lack of detail. Therefore, a contrast enhancement method, based on fuzzy set theory and nonsubsampled shearlet transform (NSST), is proposed. First, the original image is decomposed into several high-frequency components and a low-frequency component by NSST. Then, the threshold method is used to remove noises in the high-frequency components. In addition, a linear stretch is used to improve the overall contrast in the low-frequency component. Then, the reconstruct image is reconstructed by applying the inverse NSST to the processed high-frequency and low-frequency components. Finally, the fuzzy contrast is used to improve the detail information and global contrast in the reconstruct image. Experimental results indicate that, relative to contrast algorithms, the peak signal-to-noise ratio of the proposed method is improved by approximately 18%, and the root mean square error (RMSE) is optimized to approximately 48%. The proposed method also improves the image definition and texture information. Moreover, when compared with the Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical image Enhancement, the processing time (time) of this proposed method optimizes about 86%, which can obviously improve the computational efficiency of this method.

关键词： fuzzy contrast medical image nonsubsampled shearlet transform threshold denoising

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Methods of spatial and temporal processing of images in optoelectronic control systems

Methods of spatial and temporal processing of images in opto...

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International Conference on Innovations and Prospects of Development of Mining Machinery and Electrical Engineering 2019, IPDME 2019

作者： Makhov, V.E. Shaldaev, S.E. A.F. Mozhaisky Military Space Academy 13 Zhdanovskaya Str. St. Petersburg197082 Russia

The data about the effect of image quality on accuracy of information parameters are presented. Studies on accuracy of measuring characteristics of objects in their images depending on image quality are described. Studies showed that accuracy of measurement algorithms improves with an increase in sample sizes of frames in the optoelectronic system (OES). image quality is improved by processing the frame samples with algorithms for increasing resolution and reducing noise levels, both in single-point and multi-position OESs based on digital cameras with matrix photodetectors. The maximum effect of an increase in information content of the image and accuracy of measuring algorithms is determined by variations in the pixel structure of each image in the region of brightness gradient boundaries. In the single-point OESs which ensure high quality of optical images without noise factors and variations of other external parameters, the optimal number of frames in the OES is four. © 2019 Published under licence by IOP Publishing Ltd.

关键词： image quality

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Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer

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EXPERT systems WITH APPLICATIONS 2019年 125卷 112-129页

作者： Abd Elaziz, Mohamed Oliva, Diego Ewees, Ahmed A. Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Univ Guadalajara Dept Ciencias Computac CUCEI Ave Revoluc 1500 Guadalajara Jalisco Mexico Damietta Univ Dept Comp Dumyat Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt

image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur's and Otsu's methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu's methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Multi-verse optimizer Multi-objective optimization image segmentation Multi-level thresholding

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An enhanced non local mean method for hole filling in the depth image-based rendering system 11

An enhanced non local mean method for hole filling in the de...

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11th International Conference on Digital image processing (ICDIP)

作者： Zhang, Zihao Wang, Yuanqing Zhan, Lingli Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Peoples R China Nanjing Univ Key Lab Intelligent Opt Sensing & Manipulat Minist Educ Nanjing 210046 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Depth image based rendering (DIBR) is the most widely used technology among synthesis algorithms. Hole filling is a challenge in producing desirable synthesized images. In this paper, we propose an enhanced non local mean based hole filling method. Color, gradient and depth information is combined to select the optimal candidate patches. The missing information from holes is then formed by aggregating multiple candidate patches. Furthermore, an efficient invalid pixel classification method based on their chararcteristics is proposed to divide invalid pixels into three types, and use different methods to fill them, and reduce the computational load of the hole filling unit. The results show that the proposed method has a better robustness and performance for hole filling in DIBR systems than other hole filling based on algorithms.

关键词： Depth image based rendering(DIBR) Hole filling Non local mean based hole filling method

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Channel based Threshold Segmentation of Multi-Class Cervical Cancer using Mean and Standard Deviation on Pap Smear images

Channel based Threshold Segmentation of Multi-Class Cervical...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： S. Jaya M. Latha Sri Sarada College for Women Salem India

ISBN: (数字)9781728141084

ISBN: (纸本)9781728141091

Cervical cancer is the second most dangerous metastatic tumor where grows in a woman's cervix. Pap smear is the simplest screening test for the detection of cancer at the starting stage. Cervical cancer has two types of Normality and abnormality cancer which contains the cell and cytoplasm in the same structure. It is difficult to identify a cancerous nucleus in the cell. In image processing, various algorithms are used to segment the nucleus alone in microscopic images. The primary scope of this paper is focusing on Pre-processing, Segmentation and Feature Extraction with six levels of Pap images. The performance evaluation has been calculated based on the segmentation results. In the first phase, pre-processing used mean filters to remove noise and enhanced with CLAHE. In the second phase, Segmentation used threshold value by taking the sum of three channels with the proposed methodology in mean and standard deviation. In the third phase, Properties of image regions, Shapes, Textures and some statistical features are extracted after segmentation. To evaluate performance measure used SSIM for each type of cancerous segmentation that is compared with K-means and Fuzzy C-means algorithm. Thus, the proposed work of separation and addition of RGB channel based segmentation gives the best results for nucleus segmentation in Pap smear images. The accuracy level 94.60 % has been obtained by using SVM and KNN classification for 182 Pap smear images with a class of six labels. Matlab R2016a is used as a programming tool.

关键词： image segmentation Feature extraction Cervical cancer Classification algorithms Clustering algorithms Standards

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