Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

Cao, Jianfang; Chen, Lichao; Wang, Min; Tian, Yun

doi:10.1155/2018/3598284

Cited by 73 publications

(41 citation statements)

References 24 publications

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“…Otsu"s thresholding is a method can detect the threshold of each image automatically [22,23]. It is assumed that there are two classes in the image: foreground and background [24,25]. Foreground represents the area of the flood, while the background stands for the other objects like mountain, plants, and riverbank.…”

Section: Methodsmentioning

confidence: 99%

A performance analysis for real-time flood monitoring using image-based processing

Zhang

Jindapetch

Duangsoithong

et al. 2020

IJEECS

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Nowadays, various image-based methods have been used in the area of monitoring. Whereas the precision of detection objects and real-time processing are the key issues for many applications. Considering the limitation of the working environment, the higher correctness and faster operating time can guarantee the work efficiency. In this paper, the imagebased methods have been studied to monitoring the state of the flood in the real-time system. The performance of each image processing technique has been evaluated based on accuracy and processing time. In the flood monitoring system, the variation of important parameters can cause the change of performance and the effect of the variable parameters has been demonstrated from the experiment results. After comparing to the other image-based techniques, canny edge detection presents the best one, which also has better repeatability with the source image from different locations. Consequently, the improved canny edge detection method has been proved that can work very well on the real hardware in the outdoor environment.

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Section: Methodsmentioning

confidence: 99%

A performance analysis for real-time flood monitoring using image-based processing

Zhang

Jindapetch

Duangsoithong

et al. 2020

IJEECS

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“…The first-order differential operators mainly include a Roberts operator [ 27 ], Sobel operator [ 28 ], Prewitt operator [ 29 ], and Canny operator [ 30 ]. The Roberts operator has high edge positioning accuracy but is sensitive to noise.…”

Section: Methodsmentioning

confidence: 99%

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Chen

Liu

et al. 2020

Sensors

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Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

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“…The routine uses one of the most popular and reliable edge detection algorithms of 2D image processing, Canny Edge Detection [43]. This is since its results are much useful for the 2D contour calculation.…”

Section: Feature Extractionmentioning

confidence: 99%

Linear feature extraction from point cloud using color information

Alshawabkeh

2020

Herit Sci

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LiDAR is considered as an effective technology for digitizing the real scene at a very high-resolution and in a short time. However, the resolution of the LiDAR is not sufficient to identify and evaluate the façade surface features like edges and cracks. Generally, photographs provide a better interpretation of the linear characteristics. The complementary benefits of each allow exploring valuable spatial information with different surface detail levels. The paper introduces a flexible image-based approach for linear feature extraction from LiDAR point cloud. Initially, the algorithm converts the point clouds into a structured depth image to reduce the complexity and computation time. Using transformation matrix and camera calibration parameters, the visible point clouds are perceptively projected into color image space using co-linearity equations. The result depth channel is sampled with the interpolation process and added to the color channels to compute (RGBD) layers. The edges and linear features of the surface are initially extracted using the optical 2D imagery and subsequently, each pixel of the linear features can be projected directly into 3D space. Due to the various acquisition positions of the laser and color images, the issue of occlusion is resolved using the visibility algorithm. Applying the methodology, experimental results from the Treasury Monument of Jordan's ancient Petra City, indicate that the developed approach provides adequate contour points for better interpretation and quantification of weathering processes and dangerous cracking. 3D Modeling these features can also reduce data size, facilitating surface inspection and analysis with simpler models.

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Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

Cited by 73 publications

References 24 publications

A performance analysis for real-time flood monitoring using image-based processing

A performance analysis for real-time flood monitoring using image-based processing

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Linear feature extraction from point cloud using color information

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