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Research on Weld Identification and Defect Localization Based on an Improved watershed algorithm

IEEE ACCESS 2025年 13卷 6869-6877页

作者： Guo, Lingjiang Yan, Yong Cui, Junjie Xu, Zhongsi North Univ China Coll Mechatron Engn Taiyuan 030051 Shanxi Peoples R China Shanxi Key Lab High End Equipment Reliabil Technol Taiyuan 030051 Shanxi Peoples R China

The quality of weld joints is a pivotal factor influencing the strength and structural reliability for mechanical parts. Difficulties in identification caused by weld joint adhesion and defects, such as missing weld joints, this paper designs an efficient welded joint detection system, which utilizes Otsu threshold segmentation and morphological processing methods to achieve the initial segmentation of the welded joint region. Furthermore, the contour of the weld joint is extracted with greater accuracy by random incremental algorithm, which contributes to accelerating the following the detection speed only considering the weld joint region. Subsequently, watershed algorithm based on distance transformation is adopted to segment each weld joint precisely. Considering defect localization, the edge features of the missing weld region are identified and separated using the difference calculation method. The results demonstrate that the system could accurately segment adhesive weld joints and identify missing weld joint locations with the variation of the workpiece positions and angles, fully satisfying the real-time detection requirement during the weld quality identification.

关键词： Welding watersheds Image segmentation Gray-scale Feature extraction Image edge detection Skeleton Spot welding Location awareness Inspection Weld spot recognition and defect localization Otsu threshold segmentation missing weld area detection watershed algorithm

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watershed algorithm Based on Morphology for Dental X-Ray Images Segmentation

Watershed Algorithm Based on Morphology for Dental X-Ray Ima...

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Hui Li Guoxia Sun Huiqiang Sun Wei Liu School of Information Science and Engineering Shandong University School of Stomatology Shandong University

ISBN: (纸本)9781467321969

A new watershed algorithm based on mathematical morphology, which can be applied to dental X-ray images segmentation, is proposed in the paper. In order to separate each tooth and improve the serious problem of over-segmentation of traditional watershed algorithm, we apply the processing based on morphology to get the images enhanced before watershed algorithm. First, using the top-hat-bottom-hat transformation to amplify the contrast of foreground and background and remove noises. And then erosion procedure is used to weaken the degree of adhesion between teeth. Hole-filling can help to eliminate some unnecessary split-line which can cause the phenomenon of wrong segmentation. Finally we apply the watershed algorithm using distance transformation of the binary image to get the segmentation result. From the experiment results, we can see that this algorithm can separate teeth accurately and overcome the over-segmentation efficiently compared with traditional method.

关键词： watershed algorithm mathematical morphology dental X-ray image image segmentation distance transformation

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WAILS: watershed algorithm With Image-Level Supervision for Weakly Supervised Semantic Segmentation

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IEEE ACCESS 2019年 7卷 42745-42756页

作者： Zhou, Hongming Song, Kang Zhang, Xianglei Gui, Wenyong Qian, Qiusuo Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Coll Math Phys & Elect Informat Engn Wenzhou 325035 Peoples R China

Image semantic segmentation has great development in many fields, and the lack of fully supervised segmentation labels has always been a major problem in the development of image semantic segmentation. In this paper, we propose a WAILS method to solve this problem. First, the image is coarsely segmented through a weakly supervised network at the image level. Second, to further obtain the shape of the target in the image, the watershed algorithm is used to refine the result of the coarse segmentation. Third, this refined image is used as a label for the first round of training of a fully supervised image semantic segmentation network. At last, the results are refined through the watershed as the label of the second round of fully supervised training and then it iterates. This method achieves the pixel-level semantic segmentation only through image-level labels and watershed pre-segmentation. Our method achieved good performance on the PASCAL 2012 dataset and the COCO dataset, while its segmentation accuracy surpasses all current weakly supervised semantic segmentation models in the category of bird, airplane, sheep, and so on.

关键词： Semantic segmentation weakly supervised watershed algorithm

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An Individual Tree Segmentation Method Based on watershed algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 1055-1067页

作者： Yang, Juntao Kang, Zhizhong Cheng, Sai Yang, Zhou Akwensi, Perpetual Hope China Univ Geosci Sch Land Sci & Technol Beijing 100083 Peoples R China Minist Educ Peoples Republ China Subctr Int Cooperat & Res Lunar & Planetary Explo Ctr Space Explorat Beijing 100083 Peoples R China Shanxi Key Lab Resources Environm & Disaster Moni Jinzhong 030600 Peoples R China

Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.

关键词： Vegetation Three-dimensional displays Forestry Laser radar Clustering algorithms Remote sensing Image segmentation Airborne LiDAR canopy height model (CHM) individual tree segmentation profile analysis watershed algorithm

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Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018年 154卷 161-171页

作者： Kim, Jung Jin Nam, Jimin Jang, In Gwun Korea Adv Inst Sci & Technol Cho Chun Shik Grad Sch Green Transportat 373-1 Guseong Dong Daejeon 305701 South Korea

Background and Objective: Automated segmentation with high accuracy and speed is a prerequisite for FEA-based quantitative assessment with a large population. However, hip joint segmentation has remained challenging due to a narrow articular cartilage and thin cortical bone with a marked interindividual variance. To overcome this challenge, this paper proposes a fully automated segmentation method for a hip joint that uses the complementary characteristics between the thresholding technique and the watershed algorithm. Methods: Using the golden section method and load path algorithm, the proposed method first determines the patient-specific optimal threshold value that enables reliably separating a femur from a pelvis while removing cortical and trabecular bone in the femur at the minimum. This provides regional information on the femur. The watershed algorithm is then used to obtain boundary information on the femur. The proximal femur can be extracted by merging the complementary information on a target image. Results: For eight CT images, compared with the manual segmentation and other segmentation methods, the proposed method offers a high accuracy in terms of the dice overlap coefficient (97.24 +/- 0.44%) and average surface distance (0.36 +/- 0.07 mm) within a fast timeframe in terms of processing time per slice (1.25 +/- 0.27 s). The proposed method also delivers structural behavior which is close to that of the manual segmentation with a small mean of average relative errors of the risk factor (4.99%). Conclusion: The segmentation results show that, without the aid of a prerequisite dataset and users' manual intervention, the proposed method can segment a hip joint as fast as the simplified Kang (SK)based automated segmentation, while maintaining the segmentation accuracy at a similar level of the snake-based semi-automated segmentation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image segmentation Hip joint Optimal thresholding watershed algorithm Automated segmentation

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Real-time image segmentation based on a parallel and pipelined watershed algorithm

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2007年第4期2卷 319-329页

作者： Dang Ba Khac Trieu Maruyama, Tsutomu Univ Tsukuba Tsukuba Ibaraki 3058573 Japan

The watershed transformation is a popular image segmentation technique for gray scale images. This paper describes a real-time image segmentation based on a parallel and pipelined watershed algorithm which is designed for hardware implementation. In our algorithm: (1) pixels in a given image are repeatedly scanned from top-left to bottom-right, and then from bottom-right to top-left, in order to achieve high performance on a pipelined circuit by simplifying memory access sequences, (2) all steps in the algorithm are executed at the same time in the pipelined circuit, (3) the amount of data that are scanned is gradually reduced as the calculation progresses by memorizing which data are modified in the previous scan, and (4) N pixels can be processed in parallel. In our current implementation on an off-the-shelf field-programmable gate array board, up to four pixels can be processed in parallel. The performance for 512 x 512 pixel images is fast enough to be the first step in real-time applications.

关键词： watershed algorithm Segmentation Real time FPGA

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Probability of failure of the watershed algorithm for peak detection in comprehensive two-dimensional chromatography

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JOURNAL OF CHROMATOGRAPHY A 2010年第8期1217卷 1375-1385页

作者： Vivo-Truyols, Gabriel Janssen, Hans-Gerd Univ Amsterdam Analyt Chem Grp vant Hoff Inst Mol Sci NL-1018 WV Amsterdam Netherlands Unilever Res Labs Adv Measurement & Data Modelling NL-3130 AC Vlaardingen Netherlands

The watershed algorithm is the most common method used for peak detection and integration In two-dimensional chromatography However, the retention time variability in the second dimension may render the algorithm to fail A study calculating the probabilities of failure of the watershed algorithm was performed The main objective was to calculate the maximum second-dimension retention dine variability. Delta(2)t(Rcut). above which the algorithm fails Several models to calculate Delta(2)t(Rcut) were developed and evaluated (a) exact model. (b) simplified model and (c) simple-modified model Model (c) gave Isle best performance anti allowed to deduce an analytical expression for the probability of failure of the water shed algorithm as a function of experiment al Delta(2)t(R). modulation time anti peak width in the first and second dimensions It could be demonstrated that The probability of failure of the watershed algorithm under normal conditions in GC x GC Is around 15-20% Small changes of Delta(2)t(R), modulation time and/or peak width in the first anti second dimension could Induce subtle changes in the probability of failure of the watershed algorithm Theoretical equations were verified with experimental results from a diesel sample injected in GC x GC and were found to be ill good agreement with the experiments (C) 2009 Elsevier 13 V All I fights reserved

关键词： Two-dimensional chromatography watershed algorithm Peak detection

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Algebraic Multi-Grid Based Multi-Focus Image Fusion Using watershed algorithm

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IEEE ACCESS 2018年 6卷 47082-47091页

作者： Huang, Ying Li, Weisheng Gao, Mingliang Liu, Zheng Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China Univ British Columbia Sch Engn Okanagan BC V1V 1V7 Canada Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China

This paper proposes a new multi-focus image fusion method named AMGW, and it is based on algebraic multi-grid (AMG) algorithm and watershed segmentation method. In the implementation, the coarse grids of the source images are first extracted with the affinity matrix, and with a spatial interpolation function the approximation of the source image can be reconstructed from the coarse grids. A considerable amount of edge and textural information is still preserved in such approximation. The two source images are compared with their corresponding approximation block by block respectively by employing the mean square error (MSE) as a sharpness criterion. The MSE values are then used to identify the blocks of higher fidelity from the source images. The watershed segmentation is applied to those uncertain blocks in one source image. The two source images are compared again with the MSEs of the segmented regions. The fused image is obtained by reserving the blocks and regions with higher MSEs and applying a post-processing operation. Experimental results demonstrate that the AMG-based method outperforms the state-of-the-art fusion approaches in terms of selected objective image quality assessments. The details of the source images are well preserved in the fused image.

关键词： Multi-focus image fusion algebraic multi-grid watershed algorithm sharpness metric

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Flooding-based watershed algorithm and its prototype hardware architecture

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 2004年第3期151卷 224-234页

作者： Rambabu, C Chakrabarti, I Mahanta, A Indian Inst Technol Dept Elect & Commun Engn Gauhati 781039 India

watershed transformation is a powerful image segmentation technique. The potential of its real-time application can be realised by a dedicated hardware architecture. However, little work has been reported so far on hardware realisation of watershed transformation. The authors propose an improved watershed algorithm derived from Meyer's simulated flooding-based algorithm by ordered queues and a prototype FPGA-based architecture for its effective implementation. The improvement in computational complexity results from use of a single queue and conditional neighbourhood comparisons while processing the 3 X 3 neighbouring pixels. Besides analysing the computational complexity of the principal steps of the proposed algorithm, the authors present simulation results of running the proposed algorithm and the conventional algorithm on different images for comparison. The proposed architecture has been modelled in VHDL and synthesised for Virtex FPGA. The implementation results show acceptable performance of the proposed architecture.

关键词： .neighbourhood hardware architecture grey scale watershed algorithm flooding watershed lines watershed transformation

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Improving Reliability of pQCT-Derived Muscle Area and Density Measures Using a watershed algorithm for Muscle and Fat Segmentation

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JOURNAL OF CLINICAL DENSITOMETRY 2015年第1期18卷 93-101页

作者： Wong, Andy Kin On Humme, Kayla Moore, Cameron Beattie, Karen A. Shaker, Sami Craven, B. Catharine Adachi, Jonathan D. Papaioannou, Alexandra Giangregorio, Lora McMaster Univ Dept Med Hamilton ON L8N 1Y2 Canada Univ Waterloo Dept Kinesiol Waterloo ON N2L 3G1 Canada Univ Hlth Network Toronto Rehabil Inst Toronto ON Canada

In peripheral quantitative computed tomography scans of the calf muscles, segmentation of muscles from subcutaneous fat is challenged by muscle fat infiltration. Threshold-based edge detection segmentation by manufacturer software fails when muscle boundaries are not smooth. This study compared the test-retest precision error for muscle-fat segmentation using the threshold-based edge detection method vs manual segmentation guided by the watershed algorithm. Three clinical populations were investigated: younger adults, older adults, and adults with spinal cord injury (SCI). The watershed segmentation method yielded lower precision error (1.18%-2.01%) and higher (p < 0.001) muscle density values (70.2 +/- 9.2 mg/cm(3)) compared with threshold-based edge detection segmentation (1.77%-4.06% error, 67.4 +/- 10.3 mg/cm(3)). This was particularly true for adults with SCI (precision error improved by 1.56% and 2.64% for muscle area and density, respectively). However, both methods still provided acceptable precision with error well under 5%. Bland-Altman analyses showed that the major discrepancies between the segmentation methods were found mostly among participants with SCI where more muscle fat infiltration was present. When examining a population where fatty infiltration into muscle is expected, the watershed algorithm is recommended for muscle density and area measurement to enable the detection of smaller change effect sizes.

关键词： Muscle cross-sectional area muscle density pQCT precision watershed algorithm

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