Sub-pixel Edge Detection of LED Probes Based On Partial Area Effect

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;

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

In this paper an accurate measurement method for optics system based on the lunar imaging is presented, and this method has the following steps. Firstly, the optical imaging system observes the lunar and acquires the image on the ground or in orbit, and records the position and the time simultaneously, with which the distance to the lunar can be computed. Secondly, the initial region of the lunar in the acquired image is decided by the gray value threshold, and the Canny edge detection method with parabola fitting is used to acquire the sub-pixel image edge points. Thirdly, the extracted edge points are used to preliminary fit the lunar disc, and the lunar ring is formed based on the fitted lunar disc expanded two pixels, then the initial coarse fitting disc is acquired according to the maximum number of edge points located in the lunar ring. Fourthly, the sub-pixel lunar disk can be obtained via the least squares fitting on the base of the initial coarse fitting disc. At last, the focal length of the optical imaging system can be computed with the position relationship between the optical imaging system and the lunar. Experiments show that this method has the ability to focal length measurement with high accuracy and frequency. By the means of imaging to the lunar, taking advantage of the long distance, sub-pixel edge detection and fitting for the lunar disc diameter, etc, whether in the full lunar and the waning lunar，the focal length could be measured accurately. It has a wide application prospects both in the developing and in orbit operating stage for optical imaging system.

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.

Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure’s edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.

To meet the demands of non-destructive testing and precision measurement in aerospace applications, this paper proposes a modified sub-pixel Canny edge detection method for ceramic matrix composite A500H11. Specifically: Direction-constrained bilateral filtering is employed to suppress pseudo-edges arising from fiber textures; The Scharr operator enhances gradient calculation accuracy; Sub-pixel non-maximum suppression elevates measurement precision to the sub-pixel level; The 2D-OTSU method significantly improves detection accuracy on reflective material surfaces. Experimental results demonstrate that the enhanced Canny operator achieves 9.29% improvement in precision compared to the traditional Canny method. Furthermore, recall and F1-score are increased by 170.9% and 77.8% respectively. This method effectively enables accurate identification of microscopic defects on the material surface.

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.

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

During space reconnaissance applications, edge detection from remote sensing imagery plays an important role in the target recognition processing. However, traditional edge detection methods usually only utilize the high-frequency information in one image. Since low-frequency elements may be aliasing with high-frequency parts, the edges extracted may be unconnected under complex topography, different objects and imaging conditions. This paper proposes a novel image edge detection method based on Non-Subsampled Contourlet Transform (NSCT) to keep the object boundary continuously. It transforms the image into Contourlet domain in both high-frequency and low-frequency sub-bands respectively. Depending on the feature of flexible directivity reservation of an image during NSCT, the further edge extraction consists of 3 steps: firstly, the elements of the high-frequency coefficient matrix in Contourlet domain are filtered with high values left using adaptive thresholds. Then the low-frequency edge information is extracted via Canny operator from the low-frequency sub-band information. Finally, to achieve a more consistent edge image, the low-frequency edge image is achieved according to the low-frequency matrix and adopted to compensate the high-frequency image with the isolated noise points eliminated as well. The numerical simulation and practical test results show the higher effectiveness and robustness of the proposed algorithm when comparing with the classical edge detectors, such as Sobel operator, Canny operator, Log operator and Prewitt operator, etc.

Cellular edge detection: Combining cellular automata and cellular learning automata

Deep learning-based super-resolution images for synchronous measurement of temperature and deformation at elevated temperature

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.

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.

Ultrasound image edge detection based on a novel multiplicative gradient and Canny operator.

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.