Abstract
The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.
Highlights
Edges are a basic image feature and they are present between a target and a background and between two targets, two regions, or two primitives
To solve the abovementioned problems, this study proposes a parallel image edge detection algorithm based on the Otsu operator by optimizing the thresholds of the Canny operator on the Hadoop platform
Using the Pascal VOC2012 image database, the traditional serial Canny algorithm, the Otsu-Canny algorithm in the literature [21], the parallel Canny algorithm, and the parallel Otsu-Canny algorithm proposed in this study were compared in terms of their edge detection performances
Summary
Edges are a basic image feature and they are present between a target and a background and between two targets, two regions, or two primitives. An image edge is a set of pixels around which the gray-level values exhibit a step change. Edge detection technology is based on the discontinuity or mutation of gray levels or textural characteristics between an object and its background. Edge detection is an important aspect of image processing and is the basis of many analytical methods in such fields as image segmentation, pattern recognition, machine vision, and regional shape extraction. Image edge detection algorithms have been widely studied [1]. The performance of the edge detection algorithm directly affects the precision of extracted object contours and the performance of the system. After nearly 60 years of research, many different edge detection methods have been designed, and each has its own characteristics and limitations
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