Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

2016 ◽  
Vol 11 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Abdullah Alamareen ◽  
Omar Al-Jarrah ◽  
Inad A. Aljarrah
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Edge Detection ◽  
Processing Speed ◽  
Message Passing ◽  
Detection Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Edge Detection Algorithm ◽  
Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

Fog Computing ◽  
2018 ◽  
pp. 183-197
Author(s):  
Abdullah Alamareen ◽  
Omar Al-Jarrah ◽  
Inad A. Aljarrah
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Edge Detection ◽  
Processing Speed ◽  
Message Passing ◽  
Detection Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Edge Detection Algorithm ◽  
Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

scholarly journals Algorithm to Image Identification and Artix-7 FPGA Implementation for Adequate Edge Detection.

2020 ◽  
Vol 8 (5) ◽  
pp. 1656-1660
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Low Contrast ◽  
Image Identification ◽  
Canny Edge ◽  
Set Up ◽  
Canny Edge Detection Algorithm

For any image identification based applications, edge detection is the primary step. The intention of the edge detection in image processing is to minimize the information that is not required in the analysis of identification of an image. In the process of reduction of insignificant data in the image, it may lead to some loss in information which in turn raise some problems like missing of boundaries with low contrast, false edge detection and some other noise affected problems. In order to reduce the effects due to noise, a modified version of popular edge detection algorithm “Canny edge detection algorithm” is proposed. Artix 7 FPGA board set up is used to implement, by using Xilinx platform the image that is obtained as output is displayed on monitor which is connected with FPGA board using connector port DVI. MATLAB Simulink is used for algorithm simulation and then it is executed on FPGA board using Xilinx platform. The results provide good motivation to use in different edge detection applications.

Edge Detection Algorithm for Surface Image Based on Euclidean Distance Discriminant

2012 ◽  
Vol 459 ◽  
pp. 347-350 ◽  
Author(s):  
Gang Li ◽  
Zi Qiang Li ◽  
Ya La Tong
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Euclidean Distance ◽  
Detection Algorithm ◽  
Target Information ◽  
Edge Detection Algorithm ◽  
Background Interference ◽  
Surface Image

For the problem of difficult to detect the edge in the surface image processing, we used the Euclidean distance from the selected pixels in the corner of the neighborhood window to the centre point of that to measure the extent of being edge, and then search for suitable threshold to extract the expected edge pixels. This algorithm can extract target information better, while also inhibiting the background interference, and it is a good algorithm which is worthy of further exploration

A Novel Approach of Edge Detection Based on Vector Morphology

2014 ◽  
Vol 511-512 ◽  
pp. 545-549
Author(s):  
Qiang Chen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Color Image ◽  
Detection Algorithm ◽  
Difficult Problem ◽  
Color Images ◽  
Edge Detection Algorithm ◽  
Morphological Gradient ◽  
Novel Approach ◽  
Image Edges

Edge detection of color image is a difficult problem in image processing. Although a lot of corresponding to methods have been proposed, however, none of them can effectively detect image edges while suppressing noises. In this paper, a novel edge detection algorithm of color images based on mathematical morphology is proposed. Through designing a new anti-noise morphological gradient operators, we can obtain better edge detection results. The proposed gradient operators are applied to detect edge for three components of a color image. An then, the final edge can be obtained by fusing the three edge results. Experimental results show that the feasibility and effectiveness of the proposed algorithm. Moreover, the proposed algorithm has better effect of preserving the edge details and better robustness to noises than traditional methods.

scholarly journals A Low Redundancy Wavelet Entropy Edge Detection Algorithm

2021 ◽  
Vol 7 (9) ◽  
pp. 188
Author(s):  
Yiting Tao ◽  
Thomas Scully ◽  
Asanka G. Perera ◽  
Andrew Lambert ◽  
Javaan Chahl
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Shannon Entropy ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Real Time Image ◽  
Time Image ◽  
Vision Application

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon Entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.

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