VS + OpenCV based binocular vision for A4 paper length measurement

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

Abstract: Abstract: this study evaluates and compares the performance of two edge detection methods Sobel method and Canny method on batik image. Batik images have unique characteristics and complex patterns, making it difficult to analyze the edges. This study presents a comparison of the results using sobel and canny edge detection methods on batik kawung images both from peak signal-to-noise reatio and from mean squared error. The results showed that canny edge detection was better than sobel method. This can be seen from the results of PSNR and MSE that is 100%. This analysis is determined by considering factors such as the accuracy of edge detection, sensitivity to noise, and the ability to handle the complexity of batik drawing patterns. The results of this study provide a detailed description of the advantages and disadvantages of each method in the image of batik kawung. The conclusions that can be drawn from this study can provide valuable guidance for choosing the optimal edge detection method in image analysis of batik kawung and others. Keywords: batik kawung; canny; MSE; PSNR; sobel Abstrak: Penelitian ini mengevaluasi dan membandingkan kinerja dua metode deteksi tepi metode Sobel dan metode Canny pada citra batik. Gambar batik mempunyai ciri-ciri yang unik dan pola yang kompleks, sehingga menyulitkan analisis tepian. Penelitian ini menyajikan perbandingan hasil menggunakan metode deteksi tepi sobel dan canny pada citra batik kawung baik dari peak signal-to-noise reatio maupun dari mean squared error. Hasil penelitian ini menunjukkan bahwa deteksi tepi canny lebih baik dibandingkan dari metode sobel. Hal ini dapat dilihat dari hasil PSNR dan MSE yang dihasilkan yaitu 100%. Analisis ini ditentukan dengan mempertimbangkan faktor-faktor seperti keakuratan deteksi tepi, kepekaan terhadap noise, dan kemampuan menangani kompleksitas pola gambar batik. Hasil penelitian ini memberikan gambaran secara detail mengenai kelebihan dan kekurangan masing-masing metode pada citra batik kawung. Kesimpulan yang dapat diambil dari penelitian ini dapat memberikan panduan berharga untuk memilih metode deteksi tepi yang optimal dalam analisis citra batik kawung dan yang lainnya. Kata kunci: batik kawung; canny; MSE; PSNR; sobel

The iris is a part of the human anatomy that can be used as a biometric identifier. Data obtained from the iris can be converted into information through iris image processing, and in order to obtain accurate iris pixel results, an edge detection operator is required that can provide detailed and good image quality effects. In this research, a comparative analysis of the Canny, Sobel, Prewitt and Roberts edge detection operators was carried out on iris images. The purpose of performing a comparative analysis of edge detection methods is to compare the detection results of each edge detection operator on iris recognition detected by each operator. The results of the comparison of edge detection methods using precision tables can be analyzed to show that the Canny edge detection operator provides better, smoother and sharper edge results in actual edge point detection, namely 0.357867, while Sobel =, 0.210212, Prewitt = 0.212452 and Roberts = 0.279196. From these results it can be concluded that the edge detection results provided by Sobel and Prewitt are less sharp and sensitive to noise, and the comparison results can vary depending on the intensity of the image and the image object being compared.

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;

Feature extraction in Copy-Move Forgery Detection (CMFD) is crucial to facilitate image forgery analysis. Edge detection is one of the processes to extract specific information from Copy-Move Forgery (CMF) Images. It sensitizes the amount of information in the image and filters out useless ones while preserving the important structural properties in the image. This paper compares five edge detection methods: Robert, Sobel, Prewitt (first Derivative), Laplacian, and Canny edge detectors (second Derivatives). CMFD evaluation datasets images (MICC-F220) are tested with both methods to facilitate comparison. The edge detection operators were implemented with their respective convolution masks. Robert with a 2x2 mask, The Prewitt and Sobel with a 3x3 mask, while Laplacian and canny used adjustable masks. These masks determine the quality of the detected edges. Edges reflect a great-intensity contrast that is either darker or brighter.

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

One of the first steps of feature extraction is edge detection. There are various methods for edge detection such as sobel operator, log method and canny operator but with disadvantages (create noise, image smoothing and discontinuous edge detected). We present an improved method for edge detection. In this method, edge detected by morphology’s operator and their combination and with the use of various structure elements of images in satellite and remote sensing. Keywords: Satellite, Image processing, Edge detection, Multi spectral images

As an important part of the automation industrial system, mechanical arm robot plays an important role in service, exploration, automation engineering and other fields, and has a broad application prospect. The traditional mechanical arm and Sobel edge detection methods have some shortcomings: 1. The PID method is separated from the ontology model and only carries out simple and multiple calculations according to the code of the steering engine, which is not stable enough and will cause more time consumption; 2. In some complex environments, while Sobel edge detection is detecting the shape of objects, the separation between subject and background is not clear, which makes it impossible to obtain accurate object information. Therefore, this paper proposes a new PID method and Canny edge detection algorithm: 1. PID method combined with three-dimensional coordinate system modeling, to achieve a more stable, faster and more robust calculation formula, to complete the mechanical arm grasping function; 2. Using Canny edge detection and three-dimensional coordinate system to analyze the object image to obtain the accurate information of the target object.

Edge is the basic feature of image. Edges form the outline of an object. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. So edge detection is one of the most commonly used operations in image analysis and there are probably more algorithm for detecting edges. In this paper various edge detectors like Canny, Sobel, Roberts and Prewitt are compared. These operators are more susceptible to noise and do not give satisfactory result for face outline. For overcoming this disadvantage morphological method is studied and the result of edge detection using morphological method is compared with Canny edge detector, Sobel edge detector, Roberts edge detector and Prewitt edge detector.Wood and Glass Images are taken up as a special conditions for wider number of applications.

In this paper, a hybrid edge detection method based on fuzzy sets and cellular learning automata is proposed. At first, existing methods of edge detection and their problems are discussed and then a high performance method for edge detection, that can extract edges more precisely by using only fuzzy sets than by other edge detection methods, is suggested. After that the edges improve incredibly by using cellular learning automata. In the end, we compare it with popular edge detection methods such as Sobel and Canny. The proposed method does not need parameter settings as Canny edge detector does, and it can detect edges more smoothly in a shorter amount of time while other edge detectors cannot.

Introduction:High-speed and high-efficient machining is the inevitable development direction of machining technology. The tool edge preparation can improve the life, cutting performance, and surface quality of a tool and help to achieve high-speed and efficient machining. Therefore, precise modeling and detection of the micron-level contour of a tool edge are crucial for edge preparation. The aim of this study is to provide the model and detect method of the prepared tool edge radius.Methods:The mathematical model of the milling tool trajectory is established through the Matlab. The material removal model by single abrasive particle is established based on the energy conservation principle and energy absorption theory. The material removal model by multiple abrasive grains on the cutting tool edge is constructed using the statistical methods. The mathematical model of the edge radius is established through the geometrical relationship. The milling edge preparation contour detection system is setup based on the machine vision principle through LabVIEW software. Finally, the edge radius at different process parameters is determined by the mathematical model and detection system, and the results are compared with the results of the scanning electron microscopic measurement (SEM).Results:Through the Comparison and analysis of the edge radius measured by the SEM and calculated by the proposed model. The maximum error between the analytical results and SEM measurements is 11.18 μm, while the minimum error is 0.07 μm. Through the comparison and analysis of the edge radius measured by the SEM and the edge detection system. The maximum difference between the two methods is 2.71 μm, and the minimum difference is 0.31 μm. The maximum difference in percentage is 9.2%, and the minimum difference in percentage is 1.2%.Discussions:The edge preparation mechanisms of a single particle and multiple particles on the tool edge are explained. A mathematical model of the edge radius is established, which provides a basis for a deeper understanding of the edge preparation effect. Based on the machine vision principle, the prepared tool micron-level edge detection method is proposed. The histogram specification method, median filtering, multi-threshold segmentation method, and Canny edge detection operator are adopted to obtain the edge contour. The comparison result shows that the mathematical model of the edge radius is accurate, and the proposed tool edge detection method is feasible, which lays the foundation for edge preparation and realization of high-speed and high-efficient machining.

Wavelet space entropy is the extension of wavelet time entropy in the two-dimensional space, which can make use of the wavelet to detect sudden change in the signal. It's a good method to eliminate the effect of noise, but at the same time, the edge it detects is coarse and not integrated. Canny operator is a classical edge detection operator that has a good localization performance for the edge. It works well for the signal mixed with little noise, but when applied to image with lots of noise, the edge it detects is often submerged by the noise. In this paper, the wavelet space entropy algorithm is improved. Furthermore, the two methods of edge detection are combined in the paper to make full use of the advantage of both methods. Simulation has proved the integrated method to be a better one for edge detection when the image is polluted by noise.

As autonomous driving technology becomes more and more popular, its safety is also attracting attention. Regarding the automatic driving of vehicles, the detection of road markings is particularly important. This paper improves the lane edge detection part of the Hough transform lane line detection method. Because the traditional Canny operator edge detection method is good for image processing, but the detection time is long, this paper replaces the Canny algorithm with the Robert operator edge detection method. The sub-edge detection method can improve the detection speed of lane line extraction. In MATLAB, by using multiple edge detection operators to perform edge detection on the same image 100 times, and taking the average of the detection time, it is found that the Robert operator takes a shorter time in the detection process than the Canny operator; Then the Robert operator and Canny operator are respectively fused into the Hough transform lane line detection. After 100 times, the same image is detected, and the running time is statistically averaged for comparison and analysis. The Robert operator is better than the Canny operator. The time taken is reduced by 0.15191 s. The simulation results show that the integration of Robert operator in Hough transform lane line detection improves the real-time performance of lane detection.

Batik is the work of the Indonesian nation which is a combination of art and technology by the ancestors of the Indonesian people. UNESCO designated batik as a Humanitarian Heritage for Masterpieces of the Oral and Intangible Heritage of Humanity, followed by a presidential decree on 2 October 2009 which was designated as Indonesia's National Batik Day. In maintaining the existence of batik in the era of technological advances, they have utilized Artificial Intelligence and Machine Learning technologies. Even though research on batik motifs has become a common topic, there are still many mistakes found during the process of identifying motifs. As is well known, Indonesia has various types of traditional batik which have very diverse colors, patterns and motifs. This is the main problem in the identification of batik motifs, where it is found that many batik motifs adopt or have similarities in either pattern or color to other batik motifs, giving rise to thin predictions when identified with other motifs. This study uses the Convolutional Neural Networks (CNN) method using Edge Detection to identify batik motifs. Using the main dataset of 1106 images divided into 4 classes, namely Kawung, Megamendung, Merak Ngibing, and Parang motifs. The research is focused on comparing the results of prediction effectiveness on the CNN model with Canny edge detection (CNN-Canny) and the CNN model with Sobel edge detection (CNN-Sobel). The training process for each model is applied the same configuration to the dataset with a ratio of 8:2. Then in the model augmentation the functions of random flip, random zoom, and random invert are applied. The results obtained from learning the machine learning model at the testing and validation stage on the CNN-Sobel model obtained an accuracy of 91.2% in the training process and 91.8% in the validation process. Meanwhile, CNN-Canny got 90.5% accuracy in the training process and 86.2% in the validation process. The results of the comparison of the performance of the two models which are mapped to the confusion matrix table on 16 data testing in the form of images of batik motifs that have never been studied by each model show that the CNN-Sobel model can work more optimally than the CNN-Canny model with an accuracy comparison percentage of 94%. compared to 76% in the process of identifying batik motifs.

Edge Detection is a very essential part in digital image processing. In case of applications which require object information or feature extraction in an image, edge detection can play a vital role. Edge occurs where there is discontinuity or a sharp change in the intensity function. Now there are many operators for edge detection, but the challenge is to get better results from the existing system. This paper represents a hybrid approach which combines the Sobel and Canny edge detectors. It also uses a median filter to remove the salt and pepper noise from the image which consequently smoothen the image and edges can be detected easily. A comparison has also been shown between the hybrid approach with median filter and without median filter. The comparative study shows that using median filter shows enhanced result. As it filters the noise from the image, the accuracy of edge detection improved and achieved ideal effect.