Book chapter
A Comparison of Canny Edge Detection Implementations with Hadoop
Advances in Information and Communication Networks, pp.733-743
Advances in Intelligent Systems and Computing, Springer International Publishing
12/27/2018
DOI: 10.1007/978-3-030-03405-4_53
Abstract
Edge detection plays a large role in digital image processing; it allows for easier identification of objects within the image from a human perspective and also opens the door for automated object detection via machine learning. One of the common edge detection algorithms, developed by John F. Canny in 1986, is a multi-stage algorithm making use of gradients within the image to calculate potential edges. Hadoop is a Java library built on the MapReduce framework and was designed to be used for distributed processing of big data projects. We, instead, choose to adapt the Canny edge detection algorithm to run via Hadoop using two methods: a streaming Python implementation and a Java implementation to compare their run times and to determine whether or not using Hadoop for such a problem is desirable over the classic sequential implementation. In Sect. 1, we introduce edge detection, explaining what it is and why it is important. In Sect. 2, we explore Canny’s algorithm. In Sect. 3, we then explain our methodology of parallelizing the sequential code and the issues which arise during the process. In Sect. 4, we display the results of our implementations. In Sect. 5 we conclude with a possible explanation of why our code does not perform as well as we had anticipated; due to memory limitations of our specific Hadoop cluster, the implementations created do not perform well for processing typical images, but do allow for the processing of very large images. We also explore possibilities for future work.
Details
- Title: Subtitle
- A Comparison of Canny Edge Detection Implementations with Hadoop
- Creators
- Josiah Smalley - University of IowaSuely Oliveira - University of Iowa
- Resource Type
- Book chapter
- Publication Details
- Advances in Information and Communication Networks, pp.733-743
- Publisher
- Springer International Publishing; Cham
- Series
- Advances in Intelligent Systems and Computing
- DOI
- 10.1007/978-3-030-03405-4_53
- eISSN
- 2194-5365
- ISSN
- 2194-5357
- Language
- English
- Date published
- 12/27/2018
- Academic Unit
- Computer Science; Mathematics
- Record Identifier
- 9984259470802771
Metrics
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