Design and simulation of various edge detection techniques using Matlab Simulink

Sahoo, Tamanna; Pine, Sandipan

doi:10.1109/scopes.2016.7955636

Cited by 24 publications

(12 citation statements)

References 3 publications

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“…The Sobel edge detectors use the masks in Figure 2 to approximate digitally the first derivatives Gx and Gy. In another word, the gradient at the center point in neighborhoods is computed as follows (1) by the Sobel detectors [18,19]. Then, we saw that a pixel at location (x, y) is an edge pixel if g ≥ T at that location.…”

Section: Edge Detectionmentioning

confidence: 99%

“…The edge detection technique involves a variety of mathematical methods that all aim to identify dots on digital images where the brightness of the image changes rapidly-the dots in the area where the image brightness changes drastically are usually arranged into a set of line curve segments called edges. Edge detection is an essential tool in image processing, even in the f ield of feature detection and extraction [18,19].…”

Section: Figure 11 Block Diagram Of the Skin Disease Detection System Through The Imagementioning

confidence: 99%

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Image investigation using higher moment statistics and edge detection for recognizing abnormal skin

Afdhal¹,

Zulfikar²,

Zulhelmi³

et al. 2020

Bulletin EEI

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This paper discusses the recognition of skin abnormalities by investigating the images using high-level statistics and edge detection. Six images of skin diseases were analyzed using several statistical parameters, including high-level moments such as skewness and kurtosis. In comparison, some images from other categories such as animal, architecture, art, vehicle, food, people, and scenery have been analyzed as well. The results were compared to skin disease images. It is expected that the general pattern of statistical parameters can distinguish skin images against images from other categories. MatLab is used as a medium to calculate the values of statistical parameters. The mean and median of the skin disease image are much larger. Meanwhile, the standard deviation is the smallest compared to other categorical images. Almost all the analyzed images close to symmetry. Nearly all images category are distributed more leaning to the left, except for the images of the art category, which is slightly more leaning to the right. Moreover, the edge detection process has been done using the Sobel algorithm. The result, however, cannot clearly distinguish a skin's abnormality. This difficulty is because of the lack of accuracy in selection the intensity.

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Section: Edge Detectionmentioning

confidence: 99%

Section: Figure 11 Block Diagram Of the Skin Disease Detection System Through The Imagementioning

confidence: 99%

Image investigation using higher moment statistics and edge detection for recognizing abnormal skin

Afdhal¹,

Zulfikar²,

Zulhelmi³

et al. 2020

Bulletin EEI

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show abstract

“…Figure 4b shows the layered map extracted from the OctoMap corresponding to the maps in Figure 4a and the generated traversable map. The edge on the layer can be detected by the edge detection method [39], where only the regular edges are considered in this illustrative example. In addition, the unknown regions are not taken into consideration in determining the terrain types.…”

Section: Environment Representationmentioning

confidence: 99%

Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments

Wang

et al. 2019

Sensors

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Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

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“…As a very common first order edge detection, Sobel and Prewitt algorithms compute the gradient of the image intensity approximately [20] [23] proposed a new design model for Sobel edge detection algorithm to be coded using MATLAB Simulink. The experimental results presented by Adlakha in [20], shows that the used technique in Prewitt edge detection algorithm is better than the Sobel edge detection technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Edge Detection Using Sobel, Homogeneity and Prewitt Algorithms

Baareh¹,

Al-Jarrah²,

Smadi³

et al. 2018

JSEA

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Edge detection considered as very important and fundamental tool in image processing. An image edge is a very sensitive place where the image information and details mostly placed on it. Different filters were used to detect and enhance these edges to improve the sharpness and raising the image clarity. The significance of this paper comes from the study, compare and evaluate the effects of three well-known edge detection techniques in a spatial domain, where this evaluation was performed using both subjective and objective manner to find out the best edge detection algorithm. The Sobel, Homogeneity and Prewitt algorithms were used on 2D gray-scale synthesis and real images in Jordan using C# programming language. According to the comparative results obtained using the three techniques, it was clearly found that Prewitt and Homogeneity algorithms performance were better than Sobel algorithm. Therefore, Prewitt and Homogeneity algorithms can be recommended as useful detection tools in edge detection.

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Design and simulation of various edge detection techniques using Matlab Simulink

Cited by 24 publications

References 3 publications

Image investigation using higher moment statistics and edge detection for recognizing abnormal skin

Image investigation using higher moment statistics and edge detection for recognizing abnormal skin

Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments

Performance Evaluation of Edge Detection Using Sobel, Homogeneity and Prewitt Algorithms

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