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Image Segmentation
Published in Vipin Tyagi, Understanding Digital Image Processing, 2018
In the Roberts edge detection technique, only four pixels’ values are processed in order to get the value at an output pixel and the mathematical operations are limited to addition and subtraction. Therefore, this is very quick technique. The disadvantage of this technique is its noise sensitivity which is limited due to the small kernel. If the edges are not very sharp, the output from the Roberts edge detection is unsatisfactory. The Prewitt edge detector: The Prewitt operator is also a gradient-based edge detector, it consists of two 3 × 3 masks (Fig. 7.10). Basically, the Prewitt operator detects horizontal and vertical edges. The x component (denoted by Gx) of the Prewitt operator detects horizontal edges, while the y component (denoted by Gy) detects vertical edges. The results of the Prewitt edge detection are shown in Fig. 7.12c. It is computationally less expensive and a quicker method for edge detection but this technique does not work well with noisy and low contrast images.The Sobel edge detector: The Sobel operator is also based on gradient, and very similar to the Prewitt operator. The difference between the two is that the Sobel operator has ‘2’ and ‘–2’ values in the center of the first and third columns of the horizontal mask and first and third rows of the vertical mask (Fig. 7.11).
Fusion of Texture and Shape-Based Statistical Features for MRI Image Retrieval System
Published in D. P. Acharjya, V. Santhi, Bio-Inspired Computing for Image and Video Processing, 2018
Prewitt operator is a method of edge detection that estimates the maximum response of a set of convolution kernels to locate the local edge orientation for every pixel element. Normally it is applied to calculate the approximate gradient magnitude and orientation of an edge within the gray scale input image. The Prewitt operator is a discrete derivative operator by means of calculating the gradient components of the image intensity function. The Prewitt edge detector is composed of a pair of 2D convolution masks or kernels of 3x3, one to compute the gradient components in the vertical direction, and another one to compute the gradient components in the horizontal direction. These 2D convolution masks (Px&Py) $ (P_x \& P_y) $ are shown in Figure 11.5.
Demonstration Of Image Sensor Communication
Published in Amir Hussain, Mirjana Ivanovic, Electronics, Communications and Networks IV, 2015
Lan Lv, Rongzhao Wu, Jiang Liu, Peng Liu, Song Liu
In order to locate the LED, edge algorithms are used. The simulation results of edge algorithms are shown as follows. The original image is shown as Figure 2(a). Robert's operator adopts partial difference operator to find the edges. The simulation result of Roberts is shown as Figure 2(b). The key of Prewitt operator is the convolution of each pixel in an image with the two convolution kernels by moving the two templates of a small region in the image. And the final output of the edge magnitude can be used for image edge detection. The simulation result of Prewitt is shown as Figure 2(c). There exist two Sobel operators, one is the horizontally detected edges, and the other is the vertically detected edges. Its mask templates are 3×3 extremely similar to Prewitt operator. The simulation result of Sobel is shown as Figure 2(d). The operator of Laplace of Gaussian (LoG) is a second-order differential edge detection method, which detects the edges by finding out the zero-crossing point value from the second derivative of gray level of the image. The operator is sensitive to noise. The simulation result of LoG is shown as Figure 2(e). The essence of Canny is to make a smooth operation using a standard Gaussian function, and then to locate the maxima of the first-order differential with the direction. The simulation result of Canny is shown as Figure 2(f)
Effective detection by fusing visible and infrared images of targets for Unmanned Surface Vehicles
Published in Automatika, 2018
Xuefeng Dai, Jianqi Zhao, Dahui Li
Sobel operator-based image edge detection method is realized by a neighbourhood convolution on one of two direction templates and the image, respectively, in the image space. The roles of the two direction templates are that one template detects the vertical edge and the other detects the horizontal edge. Then larger value of the two convolution results is assigned to the corresponding pixels on the image in the template, as a new pixel grey values. The Prewitt operator-based approach utilizes two mask convolutions as an edge detector. Usually larger value is taken as the output, which makes the algorithm sensitive to the edge changing trend. If their square mean is adopted, the more consistent performance of the full range of response can be obtained. The result is close to the true gradient. In addition, the operator can extend to eight directions, namely edge template operator, which is obtained by offline edge sub-images. The edge templates are used to detect the image in turn, and the template most similar to the detected area with the maximum, which is used as the output of the algorithm. So, the edge can be detected.
Content-based image retrieval using block truncation coding based on edge quantization
Published in Connection Science, 2020
Yan-Hong Chen, Ching-Chun Chang, Cheng-Yi Hsu
Edge detection techniques are significant and required in image processing and computer vision applications (El-tawel & Helmy, 2015). Many researchers have developed several edge detectors, including Robert operator, Sobel operator, and Prewitt operator. One of the most important operators is the Canny operator. The Canny edge detection algorithm can be decomposed into four different steps (Gu et al., 2015): (1) Smooth the image in order to remove the noise by Gaussian filter; (2) Calculate the gradient magnitude and direction by the first-order partial derivative; (3) Conduct non-maxima suppression of gradient magnitude. (4) Detect and connect the edge with the two thresholds.
Parallel Processing Algorithms of Ultrasound Images
Published in IETE Journal of Research, 2021
Digital image processing technology contains a very rich content. It is applied in different fields and different application requirements. It is necessary to adopt different digital image processing methods to achieve the expected goals. It requires specific analysis of specific issues; digital image processing technology is a comprehensive edge Discipline, the method adopted is a synthesis of the more advanced results of various disciplines, algorithm principles and special hardware are the main considerations of computer image processing [13,14]. Image recognition establishes a description of the target image through the detection and analysis of certain features of the target image [15,16]. The essence of image recognition is the process from the target image to the numerical value and symbol describing the characteristics of the target image [17,18]; Image segmentation is one of the main steps in this direction. Image segmentation is to decompose the image into several parts according to the characteristics of the image, analyze each part, and extract the part that is of interest to the system. When the conditions are met, the system will not go to the next execution means that the segmentation step stops; edge detection is one of the more important methods of segmentation classification. This article focuses on the gradient-based edge detection operators, such as Sobel operator and Prewitt operator. As shown in picture 2.Image understanding refers to the use of artificial intelligence and cognitive theory, combined with image analysis and recognition technology, to study the characteristics of the part of interest in the target image and the differences and connections between them to understand the target image and the real scene of the target image Explanation (Figure 2).