A two-stage method for fast edge detection

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

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;

This study explores the application of digital image processing techniques to improve the diagnostic accuracy of coronary artery disease through enhanced analysis of angiographic images. A curated dataset of 40 grayscale, single-vessel angiograms was used to evaluate the effectiveness of various preprocessing and edge detection methods. Preprocessing steps included noise reduction, histogram equalization, and morphological operations aimed at improving image clarity and highlighting vascular structures. Three edge detection algorithms, Otsu, Canny, and Roberts, were applied, and their outputs were further combined using the Dempster-Shafer fusion theory. Performance was assessed using edge-based structural similarity (ESSIM) and the percentage error in vessel diameter measurements. The Canny algorithm demonstrated the highest individual accuracy, while the fusion of Canny and Roberts yielded superior results, achieving the lowest error and the highest similarity index.

due to the fast and high-precision edge detection requirements of tools in projection measurement, a fast sub-pixel edge detection algorithm for tool images is proposed. The algorithm first uses the Ostu threshold to perform the background segmentation of the tool image, then uses boundary tracking to obtain the boundary points of the image, and designs the corresponding correction operator to obtain the edge information of the tool; then according to the edge gray model, the Zernike moments are used to calculate the gradient direction of the edge points, and the two-dimensional model of the edge gray is converted into the one-dimensional model, and the sub-pixel edge of the tool image can be accurately obtained by calculating the gray distribution by spline interpolation. Experimental results show that this method has better noise immunity and faster detection speed.

Budaya aksara Lampung pada saat ini sudah jarang digunakan sehingga terancam punah. Untuk melestarikan budaya tersebut, maka dilakukan penelitian mengenai huruf aksara Lampung secara digital dengan metode pengolahan citra dan pengenalan pola. Pada penelitian ini, terbagi menjadi dua tahap yaitu tahap pelatihan dan tahap pengujian. Pada tahap pelatihan dilakukan pengolahan citra dan pelatihan jaringan saraf tiruan (JST) backpropagation. Salah satu tahapan dalam pengolahan citra adalah deteksi tepi. Dalam menganalisis karakter huruf aksara Lampung menggunakan metode deteksi tepi untuk melihat pola garis huruf tersebut. Metode pengolahan citra yang digunakan dalam mendeteksi tepi citra adalah metode deteksi tepi Roberts dan metode deteksi tepi Sobel. Hasil pengolahan citra akan masuk ke tahap pelatihan JST. Bobot yang didapat dari pelatihan kemudian akan digunakan pada tahap pengujian. Metode Sobel dapat mengenali karakter aksara Lampung lebih baik, dimana hasil dari pengujian karakter aksara Lampung memberikan nilai presentase error sebesar 28.5% dengan metode deteksi Roberts dan 14.5% dengan metode deteksi Sobel.

Edge detection is one of the most important aspects of image processing. In the paper, a method of edge parameters extraction based on the vertical template in the Sobel operator is studied, combining the inter-frames correlation of the target and background in image series. This method can decrease the superfluous pixels of background and increase the processing speed of edge detection. An experiment is illustrated, which shows the effectiveness of this method and the high speed of the processing. The technology can be applied to some real-time systems such as movement detection and automatic vehicle identification.

In this paper, an improved method for detecting edge points is presented. The principle of Zernike moments and the method of sub pixel edge detection based on Zernike moments are analyzed. With deducing of the rotated Zernike moments, a new way to acquire the perpendicular distance from the center of the circular to the edge(l) is introduced in the criterion for edge points detection. Experimental results show that the improved sub pixel edge detection algorithm can detect the edge effectively. It has higher precision, integrality of detection and stronger robustness to noise.

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

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

In this paper, a fast multi-scale edge detection approach is proposed based on the theories of image diffusion and curve evolution. In comparison with the previous edge detection approaches, edge detection is performed in two steps: beginning by detecting the initial contours at a courser scale that is performed by using a linear diffusion coupling with the denoising effect and gray transformation, then the obtained contour curves are mapped and further refined up to finer scales using the fast Hermes algorithm. By this way, the experimental results show that the proposed edge detection approach are more promising than the existing methods for object detection on general images, especially on medical images.

Usually, there is a distinguishable sea-level line in the infrared sea image, where many possible objects can be found. While relative to varies kinds of objects, the sea-level line can be more easily detected, which makes the sea-level line detection a important step in object detection and recognition in infrared sea images. This paper proposed a fast sea-level line detection method, which estimated pixels of sea-level line through the gray characteristic of neighborhood of them, performed a preliminary sea-level line positioning by line fitting, and verified the results by the linear feature of sea surface edges. Based on the results of sea-level line detection, a fast object candidate detection method was introduced. Experimental results proved that the existing and position of sea-level line can be determined and preliminary object detection can be performed by the proposed method.

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.

Edge detection is an important work for object recognition and is also an essential pre-processing step in image segmentation and visual analysis. This paper deeply studies the edge detection methods of the noisy images and concentrates on the mathematical morphology methods. By analyzing and studying the theory of mathematical morphology, the paper constructs edge detection operations and analyzes the specialties of the operator and the structure elements. Based on this, the paper proposes a new method of edge detection using mathematical morphology and image fusion. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations, the proposed method is more effective on noise restraining and retaining the image details.

Analysis and processing of decayed log CT image based on multifractal theory

Significant edges in the case of non-stationary Gaussian noise