A Fast Tool Edge Detection Method Based on Zernike Moments Algorithm

This paper proposed a new subpixel detection method that detects subpixel edges directly, as opposed to the previous method, which requires crossing the entire image. It shows superior subpixel detection on the edge directly, which enhances subpixel edge detection speed significantly. In order to overcome the problem of noise reduction, this paper employs bilateral filtering. The method first performed coarse localization with the improved operator to determine the coordinates and gradient direction of the edge points. Then, the Zernike moment algorithm was used for subpixel repositioning of edge points. Finally, subpixel level edge positioning of the image is obtained. The detection algorithm is used to identify the edges of large‐size workpieces, and the results reveal that the approach has superior positioning accuracy, noise immunity, and fast detection speed.

This paper proposes a fast method for car-license-plate detection (CLPD) and presents three main contributions.The first contribution is that we propose a fast vertical edge detection algorithm (VEDA) based on the contrast between the grayscale values, which enhances the speed of the CLPD method.After binarizing the input image using adaptive thresholding (AT), an unwanted-line elimination algorithm (ULEA) is proposed to enhance the image, and then, the VEDA is applied.The second contribution is that our proposed CLPD method processes very-low-resolution images taken by a web camera.After the vertical edges have been detected by the VEDA, the desired plate details based on color information are highlighted.Then, the candidate region based on statistical and logical operations will be extracted.Finally, an LP is detected.The third contribution is that we compare the VEDA to the Sobel operator in terms of accuracy, algorithm complexity, and processing time.The results show accurate edge detection performance and faster processing than Sobel by five to nine times.In terms of complexity, a big-O-notation module is used and the following result is obtained: The VEDA has less complexity by K2 times, whereas K2 represents the mask size of Sobel.Results show that the computation time of the CLPD method is 47.7 ms, which meets the real-time requirements.

Aiming at the problem that the classical Sobel edge detection algorithm is sensitive to noise and has a large amount of computation, an adaptive fast edge detection algorithm is proposed. Firstly, some background information which we are not interested in is removed through adaptive window to reduce the computational complexity. Then, Sobel operator is used to detect the real-time edge of the image in the window, and then morphological optimization is carried out. The fast detection algorithm in this paper is implemented on the FPGA. The results show that this method can effectively detect image edges and make the image edges clearer. At the same time, it can greatly reduce the noise and computational load in the image and reduce the power consumption.

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

Accurate surface normal computation is one of the most basic and important tasks for 3d perception. While much progress has been made in speeding up normal estimation algorithms and improving their accuracy, a significant inaccuracy still remains even with modern implementations, which is the correct determination of surface normals close to non-differentiable surface edges. Current algorithms tend to amalgamate neighborhood points from independent surfaces yielding normals that neither fit well to the one nor the other surface. This paper introduces a fast and accurate 3d edge detection algorithm suitable to detect discontinuities both in depth and on surfaces with nearly 90% accuracy at rates beyond 30 Hz. Based on this method, we demonstrate how established normal estimation algorithms can be extended for edge-awareness. Additionally, a new edge-aware, fast, accurate, and robust normal estimation approach is described which exploits the data structures computed for 3d edge detection and estimates normals at 23 Hz. We assess the performance of all proposed methods and compare them with other state-ofthe-art approaches.

A two-stage method for fast edge detection

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.

For today’s Internet-of-Things (IoT) edge devices, there is an acute need for fast and power-efficient hardware for an image processing task. Traditional hardware solutions with sequential and/or pipelined architectures incur high latency and power. This motivates us to propose a novel near-memory computing architecture for rapid image processing. We propose a bit-sliced near-memory computing architecture for CMOS VLSI implementation for fast Sobel edge detection in IoT edge devices. To the best of our knowledge, this is the first work to propose near-memory computing based VLSI architecture for edge detection. The novelty of the proposed work is that one image can be processed in constant time irrespective of the image size. Binary images are used as input to the design. The Sobel operator equations are simplified by operator strength reduction, bit manipulation, and common term sharing across equations. The captured image is loaded into the design and all block-level operations are executed in parallel close to where the data resides. The architecture is highly modular and can be scaled for any image size. The block processing element (PE) is implemented at the gate-level with the Synopsys Design Compiler in 32 nm CMOS technology node using SAED 32/28 nm Process Design Kit. For processing one block frame (3x3 pixel block), the number of logic gates required is 22 with a worst-case delay of 1.5 fs and a total area of 111 n m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The average power dissipation is 2.27 μW at 1.05 V supply voltage.

T3D edge detection from a depth image is an important technique of 3D object recognition in preprocessing. There are three types of 3D edges in a depth image called jump, convex roof, and concave roof edges. Conventional 3D edge detection based on ring operators has been proposed. The conventional ring operator can detect three types of 3D edges by classifying the response of Fourier transforms. Since the conventional method needs to apply Fourier transforms to all pixels of a depth image, real-time processing cannot be done due to high computational cost. Therefore, this paper presents a fast and reliable method of detecting three types of 3D edges by using a decision tree. The decision tree is trained under supervised learning from numerous synthesized depth images and labels by capturing depth relations between candidate pixels and pixels on a ring operator to classify 3D edges. The experimental results revealed that the proposed method has 25 times faster than the conventional method. This paper also presents some examples of 3D line and 3D convex corner detection based on results obtained with the proposed method.

With conventional edge detection algorithms, the shortcomings of undetected and artificial information will happen. An improved method based on grey prediction model is proposed to improve the performance of image edge detection considerably, and the corresponding experiments is carried out. The results show that the algorithm can effectively detect the edge of images, while retaining more vivid image details, comparing to the traditional algorithm. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

An online visual measurement method for workpiece dimension based on deep learning

Halmahera Island is the result of the interaction between the Indo-Australian Plate, the Molucca Sea Plate, and the Philippine Plate, which gave rise to many active geological structures that are vulnerable to seismic activity around the island. The complexity of this geological structure makes Halmahera Island very interesting to study. This study aims to identify geological structures by integrating remote sensing and gravity satellite data. Surface lineament analysis using the remote sensing method was carried out based on Sentinel-1A imagery data. The gravity method uses GGMPlus satellite data to clarify the continuity of geological structures that cannot be clearly mapped on the surface. Lineament analysis on gravity data uses techniques such as fast sigmoid edge detection (FSED) and Euler deconvolution. The results of lineament interpretation based on integrating remote sensing and gravity satellite data show that the NE-SW structure controls the northern and northeastern arms of Halmahera. In contrast, the southern arm is dominated by the NW-SE structure. The Euler depth estimation shows that the Halmahera area contributes to having geological structures at various depths. Deep structures reach 4 km, while shallow structures are found at depths of up to 2 km. Earthquake hypocenter data strengthen the interpretation of this geological structure. This comprehensive study yields an excellent correlation between gravity and remote sensing techniques in describing the general structural framework of the area. The new finding is an NE-SW trending geological structure on the northern Halmahera arm, which may be caused by two different tectonics first, the subduction of the Molucca Sea Plate with the Halmahera Plate in the west. Second, the strike-slip movement is trending NE-SW, which cuts the northern and northeastern arms due to the rotational movement of the thrust fault with the Philippine Plate to the west.

Edge detection (for both open and closed edges) from real images is a challenging problem. Developing fast algorithms with good accuracy and stability for noisy images is difficult yet and in demand. In this work, we present a variational model which is related to the well-known Mumford-Shah functional and design fast numerical methods to solve this new model through a binary labeling processing.A pre-smoothing step is implemented for the model, which enhances the accuracy of detection. Ample numerical experiments on grey-scale as well as color images are provided. The efficiency and accuracy of the model and the proposed minimization algorithms are demonstrated through comparing it with some existing methodologies.

A novel hybrid technique of combining an improved morphological gradient operator and Zernike moments, named MGZ edge detection algorithm, is developed to meet the stringent requirements for measuring accuracies of charge coupled device metrology system. The mathematical morphological gradient and Zernike moment algorithm are discussed based on the pixels grey-scale and spatial structure information of the images. To improve the algorithm process, the image edge is oriented and extracted by using improved morphological gradient operator firstly; then, the calculation errors of the typical ideal step edge model are analysed and corrected by the proposed error compensation formula. The edge point is relocated with sub-pixel accuracy by means of Zernike moment operator based on the edge point vectors and the threshold value. Finally, the sub-pixel edge detecting of the image is attained. The extensive experimental results show that the measuring error of the edge point is compensated reliably and accurately, and the new algorithm has short operation time, more precise and stronger robustness to noise than the prior similar algorithms. Meanwhile, the proposed method can well meet the need of charge coupled device metrology system for sub-pixel edge detection.

Ant Colony Optimization (ACO) is a metaheuristic based on real behavior of ants to solve optimization problems. Edge detection plays an important role in image processing. It consists in detecting edges or contours in images which allows to extract information from them. An ACO algorithm to edge detection is proposed. Using heuristic and knowledge information and applying an improvement operator, binary images including edges detected are obtained. The algorithm was tested with several images (real and synthetic, with and without presence of noise). The results reached were competitive in terms of quality of images and required computational time.