Implementation of different image edge detection algorithms on a real embedded ADAS platform

2021 ◽  
Author(s):  
Dario Coric ◽  
Ivan Kastelan ◽  
Marijan Herceg ◽  
Nebojsa Pjevalica
Keyword(s):  
Edge Detection ◽  
Detection Algorithms ◽  
Image Edge Detection ◽  
Image Edge

Research of Image Edge Detection Algorithms Based on Android Platform

2014 ◽  
Vol 644-650 ◽  
pp. 1154-1157
Author(s):  
Yi He ◽  
Tian Li Li ◽  
Ying Qian Zhang
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Mobile Platform ◽  
Detection Algorithms ◽  
Image Edge Detection ◽  
Android Platform ◽  
Image Edge ◽  
Platform Development

With mobile platform development there are more and more Android-based image processing applications. The principles of four kinds of edge detection algorithms are analyzed in this paper and such algorithms are realized by adopting JNI technology based on android platform. At last the effect and efficiency of such algorithms are also compared and summarized.

Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

2020 ◽  
Vol 11 (2) ◽  
pp. 80-94
Author(s):  
Tapan Sharma ◽  
Vinod Shokeen ◽  
Sunil Mathur
Keyword(s):  
Edge Detection ◽  
Artificial Bee Colony ◽  
Satellite Image ◽  
Detection Algorithms ◽  
Distributed Approach ◽  
Bee Colony ◽  
Image Edge Detection ◽  
Nature Inspired Algorithm ◽  
Canny Edge ◽  
Image Edge

The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

A Novel Approach for Detectioning, Segmenting and Classifying Shadow Image Based on Rough Sets

2014 ◽  
Vol 513-517 ◽  
pp. 4175-4179
Author(s):  
Tie Min Chen ◽  
Hong Song He ◽  
Xin Cong Jiang
Keyword(s):  
Edge Detection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Detection Algorithm ◽  
Shadow Image ◽  
Detection Algorithms ◽  
Image Edge Detection ◽  
Novel Approach ◽  
Image Edge

The disadvantages of domestic and oversea shadow image edge detection algorithms are analyzed. A novel shadow detection algorithm based on edge growing and rough set theory and subsequent solution is proposed. We describe how to detect image edge using condition attribution of rough set in this paper. Also, the method of thinning and connection for shadow edge using edge growing from the edge nodes is proposed. As can be seen from the experimental analysis, the method we proposed has better performance in edge detection and image segmentation.

Medical Images Edge Detection Based on Mathematical Morphology

2013 ◽  
pp. 7-11
Author(s):  
J. Mehena
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Images ◽  
Detection Algorithm ◽  
Medical Image Segmentation ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Image Edge Detection ◽  
Image Edge

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and reconstruction. Conventionally, edge is detected according to gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of medical images with salt-and-pepper noise. The simulation results shows that the novel mathematical morphological edge detection algorithm is more efficient for image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms. It has been observed that the proposed morphological edge detection algorithm performs better than sobel, prewitt, roberts and canny’s edge detection algorithm. In this paper the comparative analysis of various image edge detection techniques is presented using MATLAB 8.0 .

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