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Journal articles on the topic 'Mean square Canny error'

Author: Grafiati

Published: 6 September 2023

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1

Chhabra, Mohit, and Rajneesh Kumar. "Comparison of Different Edge Detection Techniques to Improve Quality of Medical Images." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2496–507. http://dx.doi.org/10.1166/jctn.2020.8921.

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In modern era the major challenge will betodetect diseases from Medical Images. To curb this challenge, different efficient image feature extraction techniques was required in medical fields. Today Medical field industry today deals with millions of images of different disease of brain heart, lungs. So in this paper, we had presented a comparison among different feature extraction technique like Canny, Laplacian of Gaussian, Sobel, Prewit on large number of images of lung disease. The objective of our research work was to find best extraction techniques based on various image quality parameters such as Mean absolute error (MAE), Root mean square error (RMSE), mean square error (MSE), Signal to noise ratio (SNR).

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Fendriani, Yoza, Regita Kharisma, and Mardian Peslinof. "ANALISIS PERBANDINGAN VARIASI FILTER PADA DETEKSI TEPI MENGGUNAKAN METODE CANNY TERHADAP CITRA CT-SCAN KANKER PARU-PARU." JOURNAL ONLINE OF PHYSICS 8, no. 2 (April 1, 2023): 77–81. http://dx.doi.org/10.22437/jop.v8i2.24451.

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Telah dilakukan penelitian deteksi tepi citra CT-Scan kanker paru-paru metode Canny menggunakan beberapa filter yaitu mean, median, dan Gaussian filter. Deteksi tepi ini bertujuan untuk mengetahui batasan dari kanker serta mengetahui filter terbaik dalam deteksi tepi metode Canny berdasarkan nilai Mean Square Error (MSE) dan Peak Signal Noise to Ratio (PSNR). Berdasarkan beberapa penelitian metode Canny adalah metode yang sangat baik dalam deteksi tepi citra karena menghasilkan bentuk tepi citra yang terlihat jelas, tetapi namun kualitas citra masih kurang baik karena banyaknya noise. Noise adalah citra atau pixel yang mengganggu kualitas citra maka dilakukan proses smooting dengan menggunakan filter. Terdapat enam citra pasien kanker paru-paru yang diuji dengan tahapan pendeteksian tepi pada metode Canny tanpa filter dan menggunakan filter. Dari hasil penelitian, filter median mendapatkan hasil yang lebih baik dari filter mean dan Gaussian. Berdasarkan nilai MSE dan PSNR filter median memiliki nilai yang lebih baik dari yang lainnya. Nilai MSE dari filter median yaitu 47 dan nilai PSNR filter median yaitu 31dB.

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Kieu, S. T. H., A. Bade, and M. H. A. Hijazi. "Modified canny edge detection technique for identifying endpoints." Journal of Physics: Conference Series 2314, no. 1 (August 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2314/1/012023.

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Abstract Edge detection is an image processing technique that retains the edges of an object in an image while discarding other features. The Canny edge detection technique is regarded as one of the most successful edge detection algorithms because of the good edge detection effect. However, one of its problems is the discontinued edges. In this paper, we present an endpoint identification algorithm that can pinpoint the position of the discontinued edges. After the endpoints are identified, they are paired together based on distance, and the broken gaps are filled by connecting the endpoints. Results have shown that, visually, our method has fewer discontinued edges when compared to Canny. Also, the mean square error of our method is lower than traditional Canny, indicating that our technique produces edge images that are more accurate than the traditional Canny.

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Sugandi, Budi, and Yuniatmi Syamsudin. "Deteksi Tepi Canny dan RMSE untuk Identifikasi Kerusakan pada Kemasan Minuman." JURNAL INTEGRASI 14, no. 2 (October 31, 2022): 110–13. http://dx.doi.org/10.30871/ji.v14i2.4420.

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Salah satu minuman kemasan yang banyak dipakai adalah kemasan kaleng. Kekurangan kemasan minuman kaleng adalah sifatnya yang mudah rusak akibat benturan dengan benda lain maupun terjatuh. Kemasan yang rusak mengakibatkan produk menjadi tidak sempurna. Sehingga proses identifikasi kerusakan kemasan kaleng menjadi sangat penting sebagai proses penjamin kualitas produk. Penelitian ini ditujukan sebagai salah satu solusi untuk mengidentifikasi kerusakan pada kemasan minuman kaleng. Metode deteksi yang diusulkan berdasarkan pada deteksi tepi Canny dan RMSE (Root Mean Square Error). Proses awal deteksi dimulai dengan pengkapturan citra kaleng oleh kamera. Citra asli RGB ini akan dikonversi ke citra biner untuk kemudian dilakukan deteksi tepi Canny. Pada penelitian ini, digunakan nilai high threshold 20 dan low threshold 10 pada proses deteksi tepi Canny. Citra hasil deteksi tepi Canny akan dibandingkan dengan citra deteksi Canny yang menjadi referensi menggunakan nilai RMSE. Nilai RMSE yang digunakan untuk kategori OK dan NG dibatasi pada nilai 70. Hasil pengujian menunjukan nilai RMSE untuk kategori OK berada pada rentang 70.72 dan 85.24 sedangkan kategori NG berada pada rentang 47.99 dan 69.93. Pengujian dilakukan menggunakan citra kaleng bagian atas dan tengah.

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Wang, Zhenyu, Mingshun Yang, Leijie Ren, Jiali Han, Yirou Liu, Xingbai Zhao, and Yangang Feng. "An Improved BM3D-Canny-Zernike Algorithm for Micro-Size Detection of Electronic Connectors." Traitement du Signal 39, no. 3 (June 30, 2022): 899–906. http://dx.doi.org/10.18280/ts.390315.

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To detect the micro-size injection molded parts of electronic connectors, this paper establishes a complete size detection system based on machine vision, and measures the size through image acquisition and processing, according to the features of the injection molded parts. The proposed system is called the improved BM3D-Canny-Zernike algorithm. Specifically, the traditional block matching and three-dimensional filtering (BM3D) image denoising algorithm was improved to optimize the peak signal-to-noise ratio (PSNR) and reduce the mean squared error (MSE). Then, the Canny algorithm was improved for pixel-level edge detection, and the Zernike moment is improved for detecting edges on the subpixel-level more effectively and reducing the calculation amount. Finally, the least squares method was employed to fit the edge to be measured. The exact pixel length was obtained by solving the function of different edges, thereby realizing size measurement. Experimental results show that the mean error percentage of our algorithm was 8.73%, which meets the needs of industrial detection.

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Kosukegawa, Hiroyuki, Yuta Kiso, Mitsuo Hashimoto, Tetsuya Uchimoto, and Toshiyuki Takagi. "Evaluation of detectability of differential type probe using directional eddy current for fibre waviness in CFRP." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2182 (September 14, 2020): 20190587. http://dx.doi.org/10.1098/rsta.2019.0587.

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This paper describes the detectability of eddy current testing (ECT) using directional eddy current for detection of in-plane fibre waviness in unidirectional carbon fibre reinforced plastic (CFRP) laminate. Three different types of probes, such as circular driving, symmetrical driving and uniform driving probe, were proposed, and the waviness angle was extracted from the contour map of the ECT signal by applying a Canny filter and a Hough transform. By comparing both the waviness angle estimated by ECT and that obtained by an X-ray CT image, the standard deviation (precision) and root mean square error (accuracy) were evaluated to discuss the detectability of these probes. The directional uniform driving probe shows the best detectability and can detect fibre waviness with a waviness angle of more than 2° in unidirectional CFRP. The probe shows a root mean square error of 1.90° and a standard deviation of 4.49° between the actual waviness angle and the angle estimated by ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

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Lu, Ting, Beibei Zhang, Yunpeng Hu, and Jianyong Chen. "Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction." Scientific Programming 2021 (October 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/5499351.

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This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm ( P < 0.05 ). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher ( P < 0.05 ). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C ( P < 0.05 ). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.

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Liu, Zhaoyang, Huajie Hong, Zihao Gan, Jianhua Wang, and Yaping Chen. "An Improved Method for Evaluating Image Sharpness Based on Edge Information." Applied Sciences 12, no. 13 (July 2, 2022): 6712. http://dx.doi.org/10.3390/app12136712.

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In order to improve the subjective and objective consistency of image sharpness evaluation while meeting the requirement of image content irrelevance, this paper proposes an improved sharpness evaluation method without a reference image. First, the positions of the edge points are obtained by a Canny edge detection algorithm based on the activation mechanism. Then, the edge direction detection algorithm based on the grayscale information of the eight neighboring pixels is used to acquire the edge direction of each edge point. Further, the edge width is solved to establish the histogram of edge width. Finally, according to the performance of three distance factors based on the histogram information, the type 3 distance factor is introduced into the weighted average edge width solving model to obtain the sharpness evaluation index. The image sharpness evaluation method proposed in this paper was tested on the LIVE database. The test results were as follows: the Pearson linear correlation coefficient (CC) was 0.9346, the root mean square error (RMSE) was 5.78, the mean absolute error (MAE) was 4.9383, the Spearman rank-order correlation coefficient (ROCC) was 0.9373, and the outlier rate (OR) as 0. In addition, through a comparative analysis with two other methods and a real shooting experiment, the superiority and effectiveness of the proposed method in performance were verified.

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Ledalla, Sukanya, Vijendar Reddy Gurram, Gopala Krishna P, Saiteja Vodnala, Maroof Md, and Raviteja Reddy Annapuredddy. "Density based smart traffic control system using canny edge detection algorithm along with object detection." E3S Web of Conferences 391 (2023): 01061. http://dx.doi.org/10.1051/e3sconf/202339101061.

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It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recognition method is required. The astute edge finder outperforms other processes in terms of accuracy, entropy, PSNR (peak signal to noise ratio), MSE (mean square error), and execution time. There are a number of possible edge recognition calculations. In terms of reaction time, vehicle the board, mechanization, dependability, and overall productivity, this framework performs significantly better than previous models. Utilizing a few model images of various traffic scenarios, appropriate schematics are also provided for a comprehensive approach that includes image collection, edge distinguishing evidence, and green sign classification. Also recommended is a system with object identification and priority for ambulances stuck in traffic.

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Yin, Hua, Jingling Xu, Yinglong Wang, Dianming Hu, and Wenlong Yi. "A Novel Method of Situ Measurement Algorithm for Oudemansiella raphanipies Caps Based on YOLO v4 and Distance Filtering." Agronomy 13, no. 1 (December 30, 2022): 134. http://dx.doi.org/10.3390/agronomy13010134.

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Oudemansiella raphanipies has gradually gained more and more popularity in the market for its delicious taste, while enhancing human immunity and regulating human body functions as well. To achieve the high-throughput and automatic monitoring of the phenotypes of Oudemansiella raphanipies, a novel method, based on YOLO v4 and Distance Filter (DF), was proposed for high-precision diameter estimation of Oudemansiella raphanipies caps. To begin with, a dataset of Oudemansiella raphanipies was established by the laboratory cultivation and collection of factory samples. The improved YOLO v4 target detection model with added CBAM modules to each convolution block in the backbone was trained to locate the caps and, thus, obtain an approximate bounding box. Secondly, the approximate contour of the cap was gained through the H component, canny edge detection operators, and distance filtering to conduct the noise elimination. Finally, the center of the fitted circle and its accurate contour of the cap could be obtained by the constrained least square method, and the diameter of the fitted circle was estimated by the calibration data. The results of practical tests showed that this method achieved an accuracy of 95.36% in recognizing Oudemansiella raphanipies caps in the growing bed, and the fitting effect of caps was superior to Circle Hough Transform (CHT), the least square method (LS), and Ransac, with no manual adjustment on parameters. Compared with the manual measurement, the mean absolute error (MAE) of this method was 0.77 mm, the coefficient of determination (R2) was 0.95, and the root mean square error (RMSE) was 0.96 mm. Therefore, the model had high-cost performance and could meet the needs of continuous and long-term tracking of the cap shape of Oudemansiella raphanipies, providing the basis for future high-throughput breeding and machine picking.

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Liu, Chang, Sara Shirowzhan, Samad M. E. Sepasgozar, and Ali Kaboli. "Evaluation of Classical Operators and Fuzzy Logic Algorithms for Edge Detection of Panels at Exterior Cladding of Buildings." Buildings 9, no. 2 (February 6, 2019): 40. http://dx.doi.org/10.3390/buildings9020040.

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The automated process of construction defect detection using non-contact methods provides vital information for quality control and updating building information modelling. The external cladding in modular construction should be regularly controlled in terms of the quality of panels and proper installation because its appearance is very important for clients. However, there are limited computational methods for examining the installation issues of external cladding remotely in an automated manner. These issues could be the incorrect sitting of a panel, unequal joints in an elevation, scratches or cracks on the face of a panel or dimensions of different elements of external cladding. This paper aims to present seven algorithms to detect panel edges and statistically compare their performance through application on two scenarios of buildings in construction sites. Two different scenarios are selected, where the building façades are available to the public, and a sample of 100 images is taken using a state-of-the-art 3D camera for edge detection analysis. The experimentation results are validated by using a series of computational error and accuracy analyses and statistical methods including Mean Square Error, Peak Signal to Noise Ratio and Structural Similarity Index. The performance of an image processing algorithm depends on the quality of images and the algorithm utilised. The results show better performance of the fuzzy logic algorithm because it detects clear edges for installed panels. The applications of classical operators including Sobel, Canny, LoG, Prewitt and Roberts algorithms give similar results and show similarities in terms of the average of errors and accuracy. In addition, the results show that the minor difference of the average of the error and accuracy indices for Sobel, Canny, LoG, Prewitt and Roberts methods between both scenarios are not statistically significant, while the difference in the average of the error and accuracy indices for RGB-Sobel and Fuzzy methods between both scenarios are statistically significant. The accuracy of the algorithms can be improved by removing unwanted items such as vegetation and clouds in the sky. The evaluated algorithms assist practitioners to analyse their images collected day to day from construction sites, and to update building information modelling and the project digital drawings. Future work may need to focus on the combination of the evaluated algorithms using new data sets including colour edge detection for automatic defect identification using RGB and 360-degree images.

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Doering, Dionísio, and Adalberto Schuck Junior. "A Novel Method for Generating Scale Space Kernels Based on Wavelet Theory." Revista de Informática Teórica e Aplicada 15, no. 2 (December 12, 2008): 121–38. http://dx.doi.org/10.22456/2175-2745.7024.

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The linear scale-space kernel is a Gaussian or Poisson function. These functions were chosen based on several axioms. This representation creates a good base for visualization when there is no information (in advanced) about which scales are more important. These kernels have some deficiencies, as an example, its support region goes from minus to plus infinite. In order to solve these issues several others scale-space kernels have been proposed. In this paper we present a novel method to create scale-space kernels from one-dimensional wavelet functions. In order to do so, we show the scale-space and wavelet fundamental equations and then the relationship between them. We also describe three different methods to generate two-dimensional functions from one-dimensional functions. Then we show results got from scale-space blob detector using the original and two new scale-space bases (Haar and Bi-ortogonal 4.4), and a comparison between the edges detected using the Gaussian kernel and Haar kernel for a noisy image. Finally we show a comparison between the scale space Haar edge detector and the Canny edge detector for an image with one known square in it, for that case we show the Mean Square Error (MSE) of the edges detected with both algorithms.

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Kumari, Rajshree, and Divyanshu Chandra. "Real-time Comparison of Performance Analysis of Various Edge Detection Techniques Based on Imagery Data." Current Journal of Applied Science and Technology 42, no. 24 (August 12, 2023): 22–31. http://dx.doi.org/10.9734/cjast/2023/v42i244178.

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Edge Detection is one of the most essential steps for image processing to identify and detect discontinuity in intensity variation. It is an effective and an efficient tool to recognize different properties of an image such as shape, contrast, color, scene analysis, image segmentation etc. The technique is very important to recognize all the edges accurately. It helps in object recognition, pattern recognition, medical image processing, motion analysis etc. There are many edge detection operators available in image processing. This paper illustrates the performance analysis of the most commonly used edge detection techniques including Canny, Sobel and Prewitt, highlighting their advantages and disadvantages with respect to different types of datasets. After analyzing various parameters like Accuracy, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Edge Detection Processing Time and Qualitative Human Visual Perception on two diverse type of datasets, varied results are found with respect to the techniques used. Among them, the most accurate and fast computed edge detection technique which gives better results on both type of datasets is concluded. Although the Sobel edge detection technique gives relatively poor result and weak performance of detection of edges, however it can be modified and further improved with respect to future work. The entire analyzing process was done under Scilab software.

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Li, Le. "Dance Art Scene Classification Based on Convolutional Neural Networks." Scientific Programming 2022 (July 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/6355959.

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Digital multimedia resources have become an important part of people’s daily cultural life. Automatic scene classification of a large number of dance art videos is the basis for scene semantic based video content retrieval. In order to improve the accuracy of scene classification, the videos are identified using a deep convolutional neural network based on differential evolution for dance art videos. First, the Canny operator is used in YCbCr colour space to detect the human silhouette in the key frames of the video. Then, the AdaBoost algorithm based on cascade structure is used to implement human target tracking and labelling, and the construction and updating of weak classifiers are analysed. Next, a differential evolution algorithm is used to optimise the structural parameters of the convolutional neural network, and an adaptive strategy is adopted for the scaling factor of the differential evolution algorithm to improve the optimisation solution accuracy. Finally, the improved deep convolutional neural network is used to train the classification of the labelled videos in order to obtain stable scene classification results. The experimental results show that by reasonably setting the crossover rate of differential evolution and the convolutional kernel size of the convolutional neural network, high scene classification performance can be obtained. The high accuracy and low root-mean-square error validate the applicability of the proposed method in dance art scene classification.

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Wang, Hongjun, Xiujin Xu, Yuping Liu, Deda Lu, Bingqiang Liang, and Yunchao Tang. "Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms." Applied Sciences 13, no. 12 (June 7, 2023): 6898. http://dx.doi.org/10.3390/app13126898.

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Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

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Li, Zhongyi, and Xi Ji. "Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries." Contrast Media & Molecular Imaging 2021 (October 1, 2021): 1–11. http://dx.doi.org/10.1155/2021/6891120.

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Background. Wrist joint injury refers to the injury of the wrist joint caused by excessive stretching of the ligaments and joint capsules around the joint caused by indirect violence. The tissue structure of the wrist joint is complex, and the clinical diagnosis effect is poor. Methods. The purpose of this study was to improve the diagnostic accuracy of wrist joint injuries and provide evidence for imaging analysis and automatic diagnosis of lesions in patients with wrist joint injuries. The Canny algorithm was adopted to extract the edge features of the patient’s magnetic resonance imaging (MRI) image, and the particle swarm optimization-support vector machine (PSO-SVM) algorithm was applied to segment the lesion. The image processing effect of the algorithm was evaluated by taking peak signal to noise ratio (PSNR), mean square error (MSE), figure of merit (FOM), and structural similarity (SSIM) as indicators. The accuracy, sensitivity, specificity, and Dice similarity coefficient of the algorithm were analyzed to evaluate the diagnostic accuracy in WJI. Results. Compared with the Gradient Vector Flo (GVF) algorithm and the Elastic Automatic Region Growing (ERG) algorithm, the edge stability of the PSO-SVM algorithm was stable above 0.9. After the quality of images processed using different algorithms was analyzed, it was found that the PSNR of the PSO-SVM algorithm was 26.891 ± 5.331 dB, the MSE was 0.0014 ± 0.0003, the FOM was 0.8832 ± 0.0957, and the SSIM was 0.9032 ± 0.0807. The four indicators were all much better than those of the GVF algorithm and the EARG algorithm, showing statistically obvious differences ( P < 0.05). Analysis on diagnostic accuracy of different algorithms for WJI suggested that the diagnostic accuracy of the PSO-SVM algorithm was 0.9413, the sensitivity was 0.9129, the specificity was 0.9088, and the Dice similarity coefficient was 0.8715. The four indicators all showed statistically great difference compared with those of the GVF algorithm and the EARG algorithm ( P < 0.05). Conclusions. The PSO-SVM algorithm showed excellent edge detection performance and higher accuracy in the diagnosis of WJI, which can assist clinicians in the clinical auxiliary diagnosis of WJI.

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da Silva, Yago M. R., Fabio A. A. Andrade, Lucas Sousa, Gabriel G. R. de Castro, João T. Dias, Guido Berger, José Lima, and Milena F. Pinto. "Computer Vision Based Path Following for Autonomous Unammed Aerial Systems in Unburied Pipeline Onshore Inspection." Drones 6, no. 12 (December 14, 2022): 410. http://dx.doi.org/10.3390/drones6120410.

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Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

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Suharto, Edi, Muhammad Yasin Simargolang, Muhammad Noor Hasan Siregar, and Agus Perdana Windarto. "Identifikasi Objek Menggunakan Proses Deteksi Tepi Metode Laplacian of Gaussian Dan Canny Terhadap Citra Sidik Jari." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (January 25, 2022): 294. http://dx.doi.org/10.30865/mib.v6i1.3459.

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Identification is the identification or determination of an object based on evidence as a clue. The objective of the research was to identify biometric images using edge detection of LoG (Laplacian of Gaussian), Canny, and LoG+Canny with different shapes and dimensions. It is expected that the object can still be identified with different shapes and dimensions. The sample of data used was 20 fingerprint images. This fingerprint image was tested using the methods LoG, Canny and LoG+Canny. The process begins with the image reading, and then the image is converted to grayscale, edge detection and image segmentation. The final result is the identification of the image. The results show that the average accuracy is 89.9 per cent for the LoG method, while 81.8 per cent for the Canny method and 90.7 per cent for the LoG + Canny method. From 10 fingerprint image tests, 8 fingerprint images can be identified by both methods. While the LoG + Canny method is capable of identifying 9 fingerprint images. The LoG method can detect images of 2, 4, 5, 6, 7, 8, 9, 10; while the Canny method can detect images of 2, 3, 4, 6, 7, 8, 9, 10; and the LoG + Canny method can detect images of 1, 2, 3, 4, 6, 7, 8, 9, 10. The minimum and maximum pixel values for the LoG method are 11 pixels for the test image and 25327 pixels for the database image. While the minimum and maximum pixel values for the Canny method are 148 pixels for the test image and 42323 pixels for the database image. In the meantime, the minimum and maximum pixel values for the LoG + Canny method are 806 pixels for the test image and 57972 pixels for the database image. The LoG + Canny method can outperform other methods for the identification of fingerprint images from the results of the tests carried out. In addition to the higher accuracy value, the resulting error value is also much smaller. The object images in the LoG method that have not been identified are numbers 1 and 3 with an error of 27.27 percent and 58.33. While the Canny method that has not been identified is number 1 and 5 with an error of 98.31 per cent and 59.92 per cent. The LoG + Canny method that cannot be identified is number 5 with an error of 61.69 per cent. The mean error values for the three methods were 10.1%, 18.2% and 9.3% (LoG, Canny, LoG + Canny).

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Piegorsch, Walter W., and A. John Bailer. "Minimum mean-square error quadrature." Journal of Statistical Computation and Simulation 46, no. 3-4 (May 1993): 217–34. http://dx.doi.org/10.1080/00949659308811504.

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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.

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Chatfield, Chris. "Apples, oranges and mean square error." International Journal of Forecasting 4, no. 4 (January 1988): 515–18. http://dx.doi.org/10.1016/0169-2070(88)90127-6.

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Beheshti, Soosan, Masoud Hashemi, Ervin Sejdic, and Tom Chau. "Mean Square Error Estimation in Thresholding." IEEE Signal Processing Letters 18, no. 2 (February 2011): 103–6. http://dx.doi.org/10.1109/lsp.2010.2097590.

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23

Schmidt, David A., Michael Joham, and Wolfgang Utschick. "Minimum mean square error vector precoding." European Transactions on Telecommunications 19, no. 3 (2008): 219–31. http://dx.doi.org/10.1002/ett.1192.

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24

Hou, Yun. "Global Mean Square Error Separation Loss." Journal of Physics: Conference Series 2363, no. 1 (November 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2363/1/012007.

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Class imbalance problem greatly affects the accuracy of deep-learning-based object detectors. To weaken its influence, this paper proposes a global mean square error separation loss (GMSES Loss). GMSES Loss assigns a factor to each example, and then enhances the learning of hard-to-learn examples by reducing the factors of easy-to-learn examples. Meanwhile, the global mean square error separation method is introduced to enhance the learning of foreground classes. To verify the effectiveness of our algorithm, experiments are conducted with YOLOv4 and RetinaNet as the baselines, respectively. The results have shown that our loss function can improve the performance of one-stage object detectors without adding any extra hyperparameters.

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TATEISHI, Ryutaro, and Chengang WEN. "Relationshio between Root Mean Square Error and Probable Error." Journal of the Japan society of photogrammetry and remote sensing 33, no. 4 (1994): 15–22. http://dx.doi.org/10.4287/jsprs.33.4_15.

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Karunasingha, Dulakshi Santhusitha Kumari. "Root mean square error or mean absolute error? Use their ratio as well." Information Sciences 585 (March 2022): 609–29. http://dx.doi.org/10.1016/j.ins.2021.11.036.

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Yonghe, Deng. "Perfectively Deducing Bessel Mean Square Error Formula." Open Civil Engineering Journal 9, no. 1 (July 31, 2015): 423–25. http://dx.doi.org/10.2174/1874149501509010423.

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In the survey teaching materials of China, deducing Bessel mean square error formula is all based on survey values with same mathematical expectation. These methods aren’t perfect. So, based on survey values without same mathematics expectation to prove Bessel mean square error formula is very necessary. Therefore, considering different mathematical expectation, it is meaningful that this paper has perfectively deduced Bessel mean square error formula.

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28

Abel, J. S. "A bound on mean-square-estimate error." IEEE Transactions on Information Theory 39, no. 5 (1993): 1675–80. http://dx.doi.org/10.1109/18.259655.

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KOBAYASHI, Kazuo. "Two Dissimilar Semantics of Mean Square Error." Journal of the Japan society of photogrammetry and remote sensing 30, no. 3 (1991): 42–48. http://dx.doi.org/10.4287/jsprs.30.3_42.

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Petra, Nicola, Davide De Caro, Valeria Garofalo, Ettore Napoli, and Antonio G. M. Strollo. "Truncated squarer with minimum mean-square error." Microelectronics Journal 45, no. 6 (June 2014): 799–804. http://dx.doi.org/10.1016/j.mejo.2014.02.018.

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Lee, Ming Ha, and Michael B. C. Khoo. "The Synthetic Mean Square Error Control Chart." Communications in Statistics - Simulation and Computation 43, no. 6 (December 2, 2013): 1523–42. http://dx.doi.org/10.1080/03610918.2012.735321.

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Salman, Mohammad Shukri, Osman Kukrer, and Aykut Hocanin. "Recursive inverse algorithm: Mean-square-error analysis." Digital Signal Processing 66 (July 2017): 10–17. http://dx.doi.org/10.1016/j.dsp.2017.04.001.

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33

Ljung, Lennart, and Pierre Priouret. "Remarks on the mean square tracking error." International Journal of Adaptive Control and Signal Processing 5, no. 6 (November 1991): 395–403. http://dx.doi.org/10.1002/acs.4480050605.

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34

Yuan, Sichen. "Review of Root-Mean-Square Error Calculation Methods for Large Deployable Mesh Reflectors." International Journal of Aerospace Engineering 2022 (August 4, 2022): 1–18. http://dx.doi.org/10.1155/2022/5352146.

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In the design of a large deployable mesh reflector, high surface accuracy is one of ultimate goals since it directly determines overall performance of the reflector. Therefore, evaluation of surface accuracy is needed in many cases of design and analysis of large deployable mesh reflectors. The surface accuracy is usually specified as root-mean-square error, which measures deviation of a mesh geometry from a desired working surface. In this paper, methods of root-mean-square error calculation for large deployable mesh reflectors are reviewed. Concept of reflector gain, which describes reflector performance, and its relationship with the root-mean-square error is presented. Approaches to prediction or estimation of root-mean-square error in preliminary design of a large deployable mesh reflector are shown. Three methods of root-mean-square error calculation for large deployable mesh reflectors, namely, the nodal deviation root-mean-square error, the best-fit surface root-mean-square error, and the direct root-mean-square error, are presented. Concept of effective region is introduced. An adjusted calculation of root-mean-square error is suggested when the concept of effective region is involved. Finally, these reviewed methods of root-mean-square error calculation are applied to surface accuracy evaluation of a two-facet mesh geometry, a center-feed mesh reflector, and an offset-feed mesh reflector for demonstration and comparison.

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Syafawi, Ahmad Ahsanudin. "PREDIKSI LUAS BANGUN BERBASIS IMAGE PROSESSING ALGORITMA CANNY." Jurnal Qua Teknika 8, no. 2 (September 29, 2018): 31–42. http://dx.doi.org/10.35457/quateknika.v8i2.471.

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Dalam menentukan luas objek persegi, persegi panjang, dan lingkaran diperlukanlah sebuah penggaris untuk mendapatkan nilai luasannya, agar lebih mudah dan praktis dapat dibantu dengan sebuah web camera dengan cara mengcapture gambar sampel objek yang ingin diketahui luasnya. Image Prosessing adalah suatu proses yang digunakan untuk mengolah citra atau gambar untuk mendapatkan citra yang lebih bagus mengunakan perangkat sistem komputer. Untuk mendapatkan perolehan panjang (X,Y) dari gambar dapat diukur setelah melewati beberapa tahapan di image prosessing yaitu dengan konversi citra dari RGB, HSV dan deteksi tepi canny, lalu terdapatlah nilai luasan dari hasil pengukuran objek. Metode Canny sendiri merupakan deteksi tepi paling baik ketika digunakan untuk mendeteksi tepi objek,Â sehingga hasil deteksi tepi tersebut dapat diambil informasi yang berguna dari citra tersebut. Dengan pengukuran luas secara manual dan secara otomatis terdapat presentase error kurang lebih 5%, hasil luas objek tersebut sudah cukup akurat namun terdapat masalah jika dalam pembuatan objek kurang presisi, peletakan objek yang miring/kurang tegap dan pencahayaan yang kurang mengakibatkan kurangnya tingkat akurasi.In determining the area of a square, rectangle, and circle object a ruler is needed to get the area value, so that it can be easier and more practical to be assisted by a web camera by capturing the image of the object sample that you want to know the area. Image Prosessing is a process used to process images or images to get better images using computer system devices. To get the long gain (X, Y) from the image can be measured after passing through several stages in image processing that is by image conversion from RGB, HSV and canny edge detection, then there is an area value from the object measurement results. The Canny method itself is the best edge detection when used to detect the edge of an object, so that the useful information of the edge detection can be retrieved from the image. With the area measurement manually and automatically there is a percentage error of approximately 5%, the object's width results are quite accurate but there is a problem if the object is less precise in making objects, sloping / less robust object laying and less lighting result in a lack of accuracy.

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36

Syafawi, Ahmad Ahsanudin. "PREDIKSI LUAS BANGUN BERBASIS IMAGE PROSESSING ALGORITMA CANNY." JURNAL QUA TEKNIKA 8, no. 2 (September 29, 2018): 31–42. http://dx.doi.org/10.30957/quateknika.v8i2.471.

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Dalam menentukan luas objek persegi, persegi panjang, dan lingkaran diperlukanlah sebuah penggaris untuk mendapatkan nilai luasannya, agar lebih mudah dan praktis dapat dibantu dengan sebuah web camera dengan cara mengcapture gambar sampel objek yang ingin diketahui luasnya. Image Prosessing adalah suatu proses yang digunakan untuk mengolah citra atau gambar untuk mendapatkan citra yang lebih bagus mengunakan perangkat sistem komputer. Untuk mendapatkan perolehan panjang (X,Y) dari gambar dapat diukur setelah melewati beberapa tahapan di image prosessing yaitu dengan konversi citra dari RGB, HSV dan deteksi tepi canny, lalu terdapatlah nilai luasan dari hasil pengukuran objek. Metode Canny sendiri merupakan deteksi tepi paling baik ketika digunakan untuk mendeteksi tepi objek, sehingga hasil deteksi tepi tersebut dapat diambil informasi yang berguna dari citra tersebut. Dengan pengukuran luas secara manual dan secara otomatis terdapat presentase error kurang lebih 5%, hasil luas objek tersebut sudah cukup akurat namun terdapat masalah jika dalam pembuatan objek kurang presisi, peletakan objek yang miring/kurang tegap dan pencahayaan yang kurang mengakibatkan kurangnya tingkat akurasi.In determining the area of a square, rectangle, and circle object a ruler is needed to get the area value, so that it can be easier and more practical to be assisted by a web camera by capturing the image of the object sample that you want to know the area. Image Prosessing is a process used to process images or images to get better images using computer system devices. To get the long gain (X, Y) from the image can be measured after passing through several stages in image processing that is by image conversion from RGB, HSV and canny edge detection, then there is an area value from the object measurement results. The Canny method itself is the best edge detection when used to detect the edge of an object, so that the useful information of the edge detection can be retrieved from the image. With the area measurement manually and automatically there is a percentage error of approximately 5%, the object's width results are quite accurate but there is a problem if the object is less precise in making objects, sloping / less robust object laying and less lighting result in a lack of accuracy.

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37

Ismail, Muhammad, Muhammad Qaiser Shahbaz, Saman Shahbaz, Muhammad Rashad, and Muhammad Hanif. "On Computing Mean Square Error of Ratio Estimator." Research Journal of Applied Sciences, Engineering and Technology 7, no. 9 (March 5, 2014): 1896–99. http://dx.doi.org/10.19026/rjaset.7.479.

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38

Chong, Edwin K. P. "Well-Conditioned Linear Minimum Mean Square Error Estimation." IEEE Control Systems Letters 6 (2022): 2431–36. http://dx.doi.org/10.1109/lcsys.2022.3162404.

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39

Adhikary, Arun Kumar. "Mean square error estimation in randomized response surveys." Model Assisted Statistics and Applications 10, no. 4 (November 16, 2015): 397–409. http://dx.doi.org/10.3233/mas-150342.

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40

Redinbo, G. "Optimum mean-square error use of convolutional codes." IEEE Transactions on Information Theory 31, no. 1 (January 1985): 18–33. http://dx.doi.org/10.1109/tit.1985.1057005.

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41

Ogasawara, Haruhiko. "Bias Adjustment Minimizing the Asymptotic Mean Square Error." Communications in Statistics - Theory and Methods 44, no. 16 (April 25, 2013): 3503–22. http://dx.doi.org/10.1080/03610926.2013.786788.

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42

Weinstein, E., and A. J. Weiss. "Lower bounds on the mean square estimation error." Proceedings of the IEEE 73, no. 9 (1985): 1433–34. http://dx.doi.org/10.1109/proc.1985.13307.

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43

Wen, Chao-Kai, Jung-Chieh Chen, and Pangan Ting. "A Shrinkage Linear Minimum Mean Square Error Estimator." IEEE Signal Processing Letters 20, no. 12 (December 2013): 1179–82. http://dx.doi.org/10.1109/lsp.2013.2283725.

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44

Kullaa, Jyrki. "Sensor validation using minimum mean square error estimation." Mechanical Systems and Signal Processing 24, no. 5 (July 2010): 1444–57. http://dx.doi.org/10.1016/j.ymssp.2009.12.001.

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45

Liski, Erkki P., Helge Toutenburg, and Götz Trenkler. "Minimum mean square error estimation in linear regression." Journal of Statistical Planning and Inference 37, no. 2 (November 1993): 203–14. http://dx.doi.org/10.1016/0378-3758(93)90089-o.

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46

Köksoy, Onur. "Multiresponse robust design: Mean square error (MSE) criterion." Applied Mathematics and Computation 175, no. 2 (April 2006): 1716–29. http://dx.doi.org/10.1016/j.amc.2005.09.016.

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47

Purczyński, Jan. "Unbiased estimator versus minimum mean square error estimator." Studia i Prace WNEiZ 45 (2016): 61–70. http://dx.doi.org/10.18276/sip.2016.45/2-05.

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48

Chaudhuri, Arijit, Arun Kumar Adhikary, and Shankar Dihidar. "Mean square error estimation in multi-stage sampling." Metrika 52, no. 2 (December 31, 2000): 115–31. http://dx.doi.org/10.1007/pl00003979.

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49

Shah, M. C., R. Parmar, and V. P. Gupta. "Some techniques of minimum mean square error estimation." Microelectronics Reliability 28, no. 5 (January 1988): 689–91. http://dx.doi.org/10.1016/0026-2714(88)90004-2.

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50

Bisaglia, Luisa, and Silvano Bordignon. "Mean square prediction error for long-memory processes." Statistical Papers 43, no. 2 (April 2002): 161–75. http://dx.doi.org/10.1007/s00362-002-0095-x.

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