Weld Seam Edge Extraction Algorithm Based on Beamlet Transform

edge detector such as Sobel, Laplacian of Gaussian, Canny and so on is applied to the whole image to detect all the edges in the filtered version of the image. Sometimes wavelet based edge detectors are also used. Hitherto comes the last step, line detection. Usually Hough transform is used to extract the centerline [6,7]. These typical methods are of low efficiency due to so many algorithms involved and due to the high time complexity and large memory required. It is also not robust when coping with highly noisy welding images. In order to increase the efficiency and to improve the anti-noising performance of welding image processing, we propose a novel method which is powerful in detecting faint linear features in highly noisy welding images. We develop the new method based on beamlet analysis, a new multiscale image analysis theory defined by Donoho [10]. Beamlets have many great applications in image processing [11]. For example, they can be used in extraction of objects hidden in very noisy images, or in dynamic programming with usage of the so-called beamlet graphs [11]. Many beamlet based algorithms are developed to detect/recognize lines, curves and objects in very noisy image data[12,13]. In theory, these new algorithms are far more sensitive and accurate than any previously proposed methods of edge detection and feature extraction [14]. Standard beamlet based line detection (BBLD) algorithm [10] first divides the whole image into 2 j × 2 j dyadic squares where the positive integer j is referred as the scale coefficient. Then beamlets are constructed along the boundary of each dyadic square, and the beamlet transform for all beamlets are calculated. At last a kind of criteria is applied to determine which beamlet should be kept while the rest are screened out. Those kept are visualized to approximate lines to be detected. Taking into account of some special characteristics of welding image processing, we introduce an orientationthresholding step to the standard BBLD algorithm. Our new method is characterized by its high efficiency and its prominent performance in dealing with highly noisy weld seam images. It is capable of detecting straight lines of arbitrary location, orientation and length without any preprocessing steps exactly. Moreover, a two-scan method is developed to extract reliable edge features from low SNR welding images.
Abstract
We present a novel algorithm based on Beamlet Transform for extracting seam edges from noisy welding images. Taking into account of some special characteristics of welding image processing, we introduce an orientation-thresholding step as well as a two-scan method to the standard beamlet-based line detection algorithm. Experiments are conducted to extract edge features from noisy welding images at different scales and to test the anti-noising performance of our algorithm. The result of experiments show that our algorithm is capable of directly extracting weld seam edges from highly noisy welding images without any pre-processing or post-processing steps, thus is characterized by its high efficiency and its prominent anti-noising performance. The orientation threshold can not only reduce the calculation load, expedite its running speed, but also it can help to improve the anti-noising performance of the algorithm. The two-scan method is particularly helpful when coping with low SNR welding images.
2008 Congress on Image and Signal Processing
Weld Seam Edge Extraction Algorithm Based on Beamlet Transform
DENG Shuangcheng, JIANG Lipei, JIAO Xiangdong, XUE Long, CAO Yingyu Opto-Mechatronic Equipment Technology Beijing Area Major Laboratory, Beijing Institute of Petrochemical Technology, Beijing 102617, China {dengshuangcheng & lipei & jiaoxiangdong & xuelong & caoyingyu}@
Introduction
Line detection plays a key role in welding image processing. The goal of welding image processing is to figure out the centerline of the weld seam by extracting both of its edges, which in turn serve as a guide for an automatic welding system. To detect and recognize straight lines in digital images, Hough Transform (HT) and its numerous variants are the classical approaches[1-3]. The various HT variants have been developed to try to overcome the major drawbacks of the standard HT, namely, its high time complexity and large memory requirements. In some cases, variants such as the randomized, probabilistic and hierarchical HT [4,5], achieve an effective complexity reduction. In others, however, they face serious difficulties and fail to provide solutions of the desired quality. This happens, for instance, when there is a relatively high level of noise present in the images [1, 2]. Unfortunately, weld seam images are replete with noises due to severe interference from welding arc, heat and splatters. The SNRs (signal to noise ratio) of weld seam images are usually too low for the ordinary line detection methods such as Hough transform to directly extract reliable edge features from them. So typical weld seam edge extraction methods require several pre-processing steps before line detection [6-9]. First the original welding image has to be filtered to eliminate as much noise as possible and is optionally enhanced to increase the contrast between foreground and background. Then, an