Author
W X Wang
Bio: W X Wang is an academic researcher from Chang'an University. The author has contributed to research in topics: Edge detection & Adaptive filter. The author has an hindex of 2, co-authored 2 publications receiving 35 citations.
Papers
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TL;DR: A new type of algorithms to improve the fractional differential Tiansi operator, which can significantly enhance the edge detection result and can show much more detailed information than traditional edge detection operators especially for the images of fine edges such as complicated rock fracture images.
Abstract: It is a new research topic that fractional differential theory is used into image processing. This paper presents a new type of algorithms to improve the fractional differential Tiansi operator, which can significantly enhance the edge detection result. The studied algorithms are based on the enhancement ability of fractional differential to image details, and they can be used to analyse the properties of fractional differential. The general procedure of the algorithms is as follows: firstly, Tiansi template is divided into eight sub-templates with different directions around the detecting pixel, and then the eight weight sum values for the eight sub-templates are obtained. Furthermore, those eight weights are classified into different groups. In this way, the three improved algorithms with different enhancing ranges are obtained. Finally, the experiments of edge detection show that the improved algorithms can obtain edge information more effectively and can show much more detailed information tha...
21 citations
TL;DR: A new way to determine the adjustable parameters is proposed and a modified Canny edge detection algorithm is constructed that can achieve the better edge detection results in most of the cases, and it is also useful for object boundary closing as a pre-segmentation step.
Abstract: The Canny edge detection algorithm contains a number of adjustable parameters, which can affect the computation time and effectiveness of the algorithm. To overcome the shortages, this paper proposes a new way to determine the adjustable parameters and constructs a modified Canny edge detection algorithm. In the algorithm, an image is firstly smoothed by an adaptive filter that is selected based on the properties of the image, instead of a fixed sized Gaussian filter, and then, the high and low thresholds for the gradient magnitude image are determined based on maximum cross-entropy between inter-classes and Bayesian judgment theory, without any manual operation; finally, if it needs, the object closing procedure is carried out. To test and evaluate the algorithm, a number of different images are tested and analysed, and the test results are discussed. The experiments show that the studied algorithm can achieve the better edge detection results in most of the cases, and it is also useful for object boundary closing as a pre-segmentation step.
18 citations
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TL;DR: The presented method can extract the edges of an image accurately and enhance them while preserving smooth areas and weak textures; these improvements can be particularly helpful to doctors' diagnoses.
106 citations
TL;DR: A review of the pavement crack image acquisition methods and 2D crack extraction algorithms, and a summary of the research artwork status, are given.
Abstract: The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.
70 citations
TL;DR: The most common rule-driven-based and data-driven image segmentation algorithms are compared and discussed in this article , and strategies to obtain better results such as hybrid integration algorithms and optimization methods are presented.
63 citations
TL;DR: Two methods to deal with the problem that traditional image denoising algorithms may easily neglect image texture details are presented, and the AFC-SPS algorithm has a better effect than other methods in enhancing the edge and preserving the texture.
62 citations
TL;DR: A novel colony analysis system including an adjustable image acquisition subsystem and a wavelet-watershed-based image segmentation algorithm that can obtain good results for ordinary colony images and for the images including a lot of small (tiny) colonies and dark colonies and overlapping (or touching) colonies.
Abstract: This paper presents a novel colony analysis system including an adjustable image acquisition subsystem and a wavelet-watershed-based image segmentation algorithm An illumination box was constructed-both front lightning and back lightning illuminations can be chosen by users based on the properties of Petri dishes In the illumination box, the lightning is uniform, which makes image processing easy A digital camera at the top of the box is connected to a PC computer; all the camera functions are controlled by the developed computer software in this study As usual, in the image processing part, the hardest task is image segmentation which is carried out by the four different algorithms: 1 recursive image segmentation on gray similarity; 2 canny edge detection-based segmentation; 3 the combination of 1 and 2, and 4 colony delineation on wavelet and watershed The first three algorithms can obtain good results for ordinary colony images, and for the images including a lot of small (tiny) colonies and dark colonies and overlapping (or touching) colonies, the algorithm 4 can obtain better results than the others The algorithms are tested by using a large number of different colony images, and the testing results are satisfactory
21 citations