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Verification of Characteristics of Gaussian filter Series for Surface Roughness in ISO and Proposal of filter Selection Guidelines

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Nanomanufacturing and Metrology 2021年第2期4卷 97-108页

作者： Yuki Kondo Ichiro Yoshida Dan Nakaya Munetoshi Numada Hiroyasu Koshimizu HOSEI University 3-7-2 Kajino-choKoganei-shiTokyo184-8584Japan Chukyo University 101-2 Yagotohonmati Showa-kuNagoyaAichi466-8666Japan YYC Solution 3-102-3F Kamiyashiro Meito-kuNagoyaAichi465-0025Japan

In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection.

关键词： Surface roughness Gaussian filter Robust filter M-estimation method Fast M-estimation method

3D Profile filter Algorithm Based on Parallel Generalized B-spline Approximating gaussian

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Chinese Journal of Mechanical Engineering 2015年第1期28卷 148-154页

作者： REN Zhiying GAO Chenghui SHEN Ding School of Mechanical Engineering and Automation Fuzhou University Tribology Research Institute Fuzhou University Fujian Institute of Metrology

Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are used for three-dimensional filtering, it is found that a rounding error and quantization error would be passed to the next in every part. In this paper, a new and high-precision implementation approach for Gaussian filter is described, which is suitable for three-dimensional reference filtering. Based on the theory of generalized B-spline function and the variational principle, the transmission characteristics of a digital filter can be changed through the sensitivity of the parameters （t1, t2）, and which can also reduce the rounding error and quantization error by the filter in a parallel form instead of the cascade form, Finally, the approximation filter of Gaussian filter is obtained. In order to verify the feasibility of the new algorithm, the reference extraction of the conventional methods are also used and compared. The experiments are conducted on the measured optical surface, and the results show that the total calculation by the new algorithm only requires 0.07 s for 480×480 data points; the amplitude deviation between the reference of the parallel form filter and the Gaussian filter is smaller; the new method is closer to the characteristic of the Gaussian filter through the analysis of three-dimensional roughness parameters, comparing with the cascade generalized B-spline approximating gaussian. So the new algorithm is also efficient and accurate for the implementation of Gaussian filter in the application of surface roughness measurement.

关键词： generalized B-spline Gaussian filter three-dimensional reference cascade characteristic parallel characteristic

Improved adaptive genetic algorithm based RFID positioning

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Journal of Systems Engineering and Electronics 2022年第2期33卷 305-311页

作者： LI Yu WU Honglan SUN Youchao Civil Aviation College Nanjing University of Aeronautics and AstronauticsNanjing 211100China

The existing active tag-based radio frequency identi-fication(RFID)localization techniques show low accuracy in practical applications.To address such problems,we propose a chaotic adaptive genetic algorithm to align the passive tag ar-rays.We use chaotic sequences to generate the intersection points,the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem.Meanwhile,to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage,we use adaptive rate of change to improve the optimiza-tion efficiency.In addition,to remove signal noise and outliers,we preprocess the data using Gaussian filtering.Experimental results demonstrate that the proposed algorithm achieves high-er localization accuracy and improves the convergence speed.

关键词： radio frequency identification(RFID)positioning im-proved genetic algorithm Gaussian filter passive tags

Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

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Computers, Materials & Continua 2022年第11期73卷 2275-2291页

作者： Prasanalakshmi Balaji B.Sri Revathi Praveetha Gobinathan Shermin Shamsudheen Thavavel Vaiyapuri Department of Computer Science King Khalid UniversitySaudi Arabia School of Electrical Engineering Vellore Institute of TechnologyIndia Department of Computer Science Jazan UniversitySaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performanc

关键词： Deep belief neural network mayfly optimization Gaussian filter contrast normalization grey level variance local binary pattern discrete wavelet transform

Micro Calcification Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm

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Computer Systems Science & Engineering 2023年第5期45卷 1887-1899页

作者： P.Gomathi C.Muniraj P.S.Periasamy Selvam College of Technology Namakkal637003Tamil NaduIndia Knowledge Institute of Technology Kakapalayam637504Tamil NaduIndia KSR College of Engineering Tiruchengode637215NamakkalIndia

The mortality rate decreases as the early detection of Breast Cancer(BC)methods are emerging very fast,and when the starting stage of BC is detected,it is curable.The early detection of the disease depends on the image processing techniques,and it is used to identify the disease easily and accurately,especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger,and cancer cells are about 0.03 mm,which is crucial for identifying in the BC area.To achieve this micro calcification in the BC images,it is necessary to focus on the four main steps presented in this work.There are three significant stages of the process assigned to find the BC using a thermal image;the image processing procedures are described below.In the first stage of the process,the Gaussian filter technique is implemented to magnify the screening image.During the second stage,BC detection is separated from the pre-processed image.The Proposed Versatile K-means clustering(VKC)algorithm with segmentation is used to identify the BC detection form of the screening image.The centroids are then recalculated using proposed VKC,which takes the mean of all data points allocated to that centroid’s cluster,lowering the overall intracluster variance in comparison to the prior phase.The“means”in K-means refers to the process of averaging the data and determining a new centroid.This process eliminates unnecessary areas of interest.First,the mammogram screening image information is taken from the patient and begins with the Contiguous Convolutional Neural Network(CCNN)method.The proposed CCNN is used to classify the Micro calcification in the BC spot using the feature values is the fourth stage of the process.The assess the presence of high-definition digital infrared thermography technology and knowledge base and suggests that future diagnostic and treatment services in breast cancer imaging will be developed.The use of sophisticated CCNN techniques in thermography is being developed to attain a greater level of consistency.The im

关键词： Contiguous Convolutional Neural Network(CCNN) Gaussian filter Versatile K-Means Clustering(VKC)algorithm mammogram cancer detection

Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

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Intelligent Automation & Soft Computing 2023年第7期37卷 707-726页

作者： V.Swetha G.Vadivu Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur CampusChennai603203India

Every year,the number of women affected by breast tumors is increasing worldwide.Hence,detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer.The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy.The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks.The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries.These drawbacks are resolved by the proposed cancer region detection methods stated in this paper.The mammogram images are clas-sified into normal,benign,and malignant types using the Adaptive Neuro-Fuzzy Inference System(ANFIS)approach in this paper.This mammogram classification process consists of a noise filtering module,spatial-frequency transformation module,feature computation module,and classification mod-ule.The Gaussian filtering Algorithm(GFA)is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module.The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this paper.Finally,Probability Histogram Segmentation Algo-rithm(PHSA)is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images.This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets.From the extensive analysis of the proposed tumor detection methods stated in this work with other works,the proposed work significantly achieves a higher performance.The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

关键词： Mammogram cancer Gaussian filter ridgelet classification

PRE-Processing for Video Coduing with Rate-Distortion Optimization Decision

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The Journal of China Universities of Posts and Telecommunications 2006年第2期13卷 79-83页

作者： QI Yi HUANG Yong-gui QI Hong-gang College of Computer Science and Engineering Guizhou University Guiyang 550003 P.R. China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 P.R. China

This paper proposes an adaptive video pre-processing algorithm for video coding. This algorithm works on the original image before intraor inter-prediction. It adopts Gaussian filter to remove noise and insignificant features existing in images of video. Detection and restoration of edges are followed to restore the edges which are excessively filtered out in filtered images. Rate-Distortion Optimization （RDO） is employed to decide adaptively whether a processed block or a unprocessed block is coded into bit-streams doe more efficient coding. Our experiment results show that the algorithm achieves good coding performances on both subjective and objective aspects. In addition, the proposed pre-processing algorithm is transparent to decoder, and thus can be compliant with any video coding standards without modifying the decoder.

关键词： video pre-processing Gaussian filter detection and restoration of edge rate-distortion optimization

Adaptive image decomposition method based on credible data fitting with local total variation

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Computer Aided Drafting,Design and Manufacturing 2012年第4期22卷 11-15页

作者： CHEN Ya GUO Qiang ZHOU Yuanfeng LI Xuemei ZHANG Caiming Department of Computer Science and Technology Shandong University Shandong Provincial Key Laboratory of Digital Media Technology Shandong University of Finance and Economics

In this paper we present a novel image decomposition method via credible data fitting with local total variation filter. The oscillation rate is used to measure the image complexity and characteristics. The filter parameter can be determined by a fitting curve which is reconstructed by oscillation rate. In addition, the approximate gaussian algorithm and integral image are used to reduce the algorithm computation and the sensitivity of the filter window selection. Experiments show the new method is better than the exist- ing methods.

关键词： image decomposition adaptive filter integral image Gaussian filter

gaussian PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

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Journal of Electronics(China) 2011年第4期28卷 539-547页

作者： Li Xiangping Wang Xiaotian Shi Guangming ＂ China Xidian University Xi＇ an 710071 China

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

关键词： Image denoising NonLocal Means(NLM) Gaussian filter Principle Component Analysis(PCA)