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Research on Railway Track Edge Detection Based on BM3D and Zernike Moments

Wang Nan, Huo Tao, Zhang Tianming

Archives of Transport

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2023

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Vol. 68, iss. 4

7--20

EN

With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.

2

Separation of overlapping bacilli in microscopic digital TB images

Priya E., Srinivasan S.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 2

87--99

EN

The sputum smear microscopy based tuberculosis (TB) screening method is a conventional method employed for disease identification. It provides significant benefit to TB burdened communities across the globe; however, there are many challenges faced in processing the sputum smear images. When the smear is thick or uneven the number of overlapping bacilli is more which impedes the diagnosis. The separation of overlapping bacilli is significant without which the results lead to gross errors in identification of the disease causing agent. In this work, separation of overlapping bacilli is carried out by method of concavity (MOC) and is compared with the conventional methods such as multi-phase active contour (MAC) and marker-controlled watershed (MCW). Performance of the methods is evaluated based on the statistical mean quality score of shape descriptors extracted from the separated and existing true bacilli. The shape descriptors employed in this work include geometric features, Hu's, Zernike moments and Fourier descriptors. Results of separated overlapping bacilli demonstrate that MOC performs better than MAC and MCW. It is observed that the statistical mean quality score of the separated bacilli using the proposed MOC shows nearest match with true bacilli. The validation performed with experimental results to that of human annotations highlights the performance of MOC in separating the overlapping bacilli in the sputum smear images.

3

Improvement and invariance analysis of Zernike moments using as a region-based shape descriptor

Ye B., Peng J.

Image Processing & Communications

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2002

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Vol. 8, no 3

11-20

EN

In order to retrieve an image from a large image database, the descriptor should be invariant to scale and rotation. It must, also have enough discriminating power and immunity to noise for retrieval from a large image database. The Zernike moment descriptor has manv desirable properties such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns, but it does not possess scale invariance. In this paper, we present an improved Zernike moment descriptor that not only has rotation invariance, but also has scale invariance. We apply the improved Zernike moments to image recognition using as an elective descriptor of global shape of an image in a large image database. The experimemtal results show that the improved Zernike moment has better invariant properties than unimproved Zernike moment using as region-based shape descriptor.