Improved Morphological Band-Pass Filtering Algorithm and Its Application in Circle Detection

Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 3765164, 9 pages https://doi.org/10.1155/2018/3765164 Research Article Improved Morphological Band-Pass Filtering Algorithm and Its Application in Circle Detection Xian Wang ,1 Qiancheng Zhao,1 and Jianping Tan2 1 School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China State Key Laboratory of High Performance and Complex Manufacturing, Central South University, Changsha 410083, China 2 Correspondence should be addressed to Xian Wang; Received 12 January 2018; Accepted 26 March 2018; Published 2 May 2018 Academic Editor: Oscar Reinoso Copyright © 2018 Xian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Existing image segmentation and image enhancement methods are deficient in complex industrial environments. Therefore, an improved morphological band-pass filter algorithm is presented. The first step of the algorithm is obtaining two marker images by erosion operations for a test image with two kinds of structuring elements: one slightly larger and one smaller than the feature but similar in shape. The second step is obtaining an image only, including the background, and an image including the feature with the background, excluding noise. The final step is realizing the feature segmentation by carrying out a difference operation on the two images. Selection of the structuring elements in the algorithm and the computational cost reduction are also discussed for engineering applications. Experimental results show that the proposed algorithm achieves the accurate segmentation of the circle at a specific scale through the velocity-optimized morphological operation and features good real-time performance and high accuracy in complex industrial environments, which could meet the requirements of industrial online monitoring. 1. Introduction In visual measurement systems, it is necessary to remove or suppress background noise and interference with image segmentation and enhancement. In a complex industrial environment, background noise and interference (e.g., vibration, water mist, dust, and illumination changes) are unlikely to be totally suppressed. Yet, they exert adverse effects on testing images. Simultaneous and accurate real-time performance of visual measurement systems in complex industrial environments are thus difficult to ensure. This problem limits the advanced promotion of vision measurement technology in the field of industrial testing [1–4]. Extensive and in-depth studies have been conducted by many scholars in relevant fields. The studies can be divided into two categories. The first category includes studying better ways to weaken background information and highlight feature information. Grigoryan and Agaian [5] described a unified approach for signal thresholding by introducing the general concept of weighted thresholding using monotonic sequences for signal and image set-theoretical representation. The main advantage of this representation is that it allows for the performance of nonlinear operations by a weighted threshold, enhancing many geometric features present in the original signals and images via coefficient manipulation and weighting. Zhang et al. [6] presented a novel image enhancement method inspired by the Retinex framework, which simulates the human visual system. However, the proposed method is more suitable for underwater environments. Liu and Chen [7] proposed a new infrared image detail enhancement approach. It not only achieved the goal of enhancing the digital detail, but also processed an image quite true to the real situation. Sadreazami et al. [8] proposed a new image-denoising method in the contourlet domain by using the alpha-stable family of distributions as input to contourlet image coefficients. In terms of the peak signal-to-noise ratio and mean structural similarity index, as well as the visual quality of the denoised images, the proposed method outperforms other existing methods. The second category includes improving accurate and real-time feature extraction algorithms. Hough transform (HT) is a robust method for feature extraction. However, its poor real-time performance and low precision render the 2 method inefficient for industrial testing. In view of this disadvantage, Xu et al. [9] presented the randomized HT (RHT). Spratling [10] proposed a new method for implementing a voting process in HT, via a competitive neural network. Djekoune et al. [11] presented a new modification of the HT method developed for an automatic biometric iris recognition system. Jiang [12] optimized methods for determining sample points and finding candidate circles of the RHT method for circle detection. These studies improved the accuracy and real-time performance of HT, to a certain extent. Additionally, some new feature extraction algorithms were proposed in recent years. Cai et al. [13] presented an efficient circle detector based on the region growth of gradient and histogram distribution of Euclidean distance. Experimental results demonstrated the ability to detect circular objects under occlusion, image noises, and moderate shape deformations with good precision. Sun et al. [14] proposed improved features from accelerated segment test (FAST) feature extraction, based on the random sample consensus (RANSAC) method. The proposed feature extractor not only effectively extracts features, but also reduces positioning error availably, making the proposed FAST feature extraction based on RANSAC feasible and efficient. De Marco et al. [15] presented a randomized iterative workflow, which exploited geometrical properties of image isophotes, selecting the most meaningful edge pixels and classifying them into subsets of equal curvature. The new method accurately detects circles within a limited number of iterations, maintaining subpixel accuracy, even in the presence of high noise levels. The above studies all show advantages. However, in complex industrial environments, the first category cannot completely suppress all kinds of interference, and the second cannot fully meet the requirements of online monitoring, in terms of accuracy or real-time performance. Morphological band-pass filtering is a method of subtracting and segmenting features using a specific shapescale structuring element [16]. It basically suppresses all kinds of background noise and interference, realizing exact segmentation of small features from an area up to 10 square pixels. For slightly larger features, the algorithm destroys the original feature boundaries and shape, thus forfeiting utility. The circle, or ellipse, is the most widely used basic feature in machine vision. Because extant image segmentation and enhancement methods are deficient in complex industrial environments, t (...truncated)