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2018 3rd International Conference on Materials Science,Machinery and Energy Engineering(MSMEE 2018)

作者： Jie Zhang Pan Zhou Jihong Long School of Hainan University

The tapping panel dryness（TPD） in Hevea brasiliensis is a complex physiological syndrome and seriously affects the yield of the natural latex from rubber tree. The study of TPD is of great significance for improving the yield of latex. The traditional artificial recognition of TPD has a certain deviation due to the high precision of grading workload, so the impact of TPD cannot be paid attention to in time. This paper uses image processing technology in the field of information science to identify the TPD, and uses image segmentation to determine the level of TPD, thus greatly reducing the artificial judgment of the TPD level of misjudgment rate.

关键词： image processing image segmentation TPD threshold segmentation algorithm

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A multi-stage adaptive otsu thresholding algorithm for pore segmentation in rock thin-section images

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-15页

作者： Yu, Chengzhen Wu, Wenhui Zheng, Jun Zeng, Wei Zheng, Dongyu Li, Zhiwu Chen, Caihua Wang, Sheng Chengdu Univ Technol Coll Comp Sci & Cyber Secur Chengdu 610059 Peoples R China Chengdu Univ Technol State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610059 Peoples R China Sichuan Engn Technol Res Ctr Ind Internet Intellig Chengdu Sichuan Peoples R China Chengdu Univ Technol State Key Lab Genhazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Chengdu Univ Technol Inst Sedimentary Geol Chengdu 610059 Sichuan Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China

Rock thin-section images segmentation refers to the process of dividing images of rock or mineral samples, captured under a microscope, into different regions or objects to extract pore data. These regions or objects may represent different rock components, microstructures, or mineral types. Due to the heterogeneity of rocks and the complexity of diagenetic evolution, the pore distribution observed in thin-section casts is scattered, and the pore shapes are irregular. Such complexities hinder the effectiveness of many conventional semantic segmentation techniques, leading to inaccuracies in porosity assessment and pore characterization. To address the aforementioned issues, this paper introduces a multi-stage adaptive Otsu thresholding algorithm tailored for pore segmentation in rock thin-section images. The algorithm processes the original rock cast thin-section image in three main stages: (1) denoising and color space conversion, (2) coarse segmentation using adaptive thresholds in the Hue (H) channel, complemented by segmentation of the Saturation (S) and Value (V) channels with Otsu thresholding, and (3) refinement of the segmentation results through morphological correction using an eight-connected component method. To validate the effectiveness of this method, we selected cast thin-section images of different lithologies from the Sichuan Basin and the Ordos Basin for a series of comparative studies. Comparative studies demonstrate that the algorithm can accurately and efficiently extract pore characteristics from high-resolution images of rock cast thin-sections with varying colors.

关键词： threshold segmentation algorithm Otsu HSV color space Image segmentation Rock cast thin-section

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Architectural design visual information mining system based on image processing technology

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JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期34卷

作者： Pan, Junyu Univ New South Wales Art Design & Architecture Sydney 2000 Australia

With the rapid development of modern society and economy, various types of science and technology have also achieved relatively rapid development, which has led to significant changes in all walks of life in modern society. This change not only makes modern society transition toward an information society but also makes people in modern society obtain more convenience. The construction industry has also achieved higher quality development with the development of science and technology, especially through the informatization reform of existing technical means in multiple processes such as building design and construction, which has saved a lot of manpower and material resources. The most important aspect in the field of architecture is the design work before construction, and the degree of refinement in this process also determines the merits of the building to a certain extent. Therefore, the field of architectural design has also received more attention from relevant researchers. At the same time, the further development of social economy in the new era also puts forward more requirements for architectural design in the construction industry, which urges researchers to conduct in-depth research on existing architectural design. At the same time, combining some emerging information technologies, a new architectural design mode with better structure and performance is proposed. Image processing technology mainly uses computer algorithms to collect images, thereby converting these images into digital signals that can be recognized by a computer, and then displaying them on a computer display. This image processing technology can also identify and extract information from images, thereby displaying the key information therein. This article mainly analyzes image processing techniques and some data analysis algorithms to obtain the feasibility of their application in visual information mining systems for architectural design. The contribution of this study is to propose

关键词： architectural design visualization technology information mining image processing technology wavelet transform denoising threshold segmentation algorithm data analysis algorithm

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Gas-liquid vertical pipe flow patterns convolutional neural network classification using experimental advanced wire mesh sensor images

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2022年 99卷 104406-104406页

作者： Brantson, Eric Thompson Abdulkadir, Mukhtar Akwensi, Perpetual Hope Osei, Harrison Appiah, Titus Fiifi Assie, Konan Roger Samuel, Sibil Univ Mines & Technol GNPC Sch Petr Studies Dept Petr & Nat Gas Engn Tarkwa Ghana Fed Univ Technol Dept Chem Engn Minna Niger State Nigeria Univ Calgary Schulich Sch Engn Dept Geomat Engn Calgary AB Canada China Univ Geosci Beijing Dept Earth Sci & Resources Beijing Peoples R China

Flow pattern identification for simultaneous gas-liquid flow in pipes is a central problem in the petroleum industry. However, there are many conventional methods used for flow patterns identification that come with some form of restrictions limited to specific operating conditions. Although previous studies made efforts to make flow patterns identification free from biases (subjectivity), objective predictions are yet to be fully explored. For the first time, this study employs a 4-layer convolutional neural network (CNN) architecture and threshold segmentation algorithm to classify industrial working fluids of air/silicone oil flow patterns in a vertical pipe. Also, an advanced wire mesh sensor (WMS) instrumentation was used to obtain the cross-sectional frame images from a series of 52 experimental runs. This study made an attempt to use the sequential cross-sectional frames original data obtained from the experimental WMS because it is the crossing wires of the WMS that interact with the flow field and also capture the main flow pattern transitional zones. The obtained experimental testing results indicate that the supervised CNN model has an accuracy of 99.90% better than the seven-benchmarked supervised machine learning classification models used in recognizing bubbly, slug and churn flow patterns. The CNN model experimental test accuracy also outwits other related flow patterns identified in the literature when compared, thereby affirming the model's robustness. Since the WMS provide high spatial and temporal resolutions data, the average pixel intensity values from the segmented images were used as the flow indicator in identifying the transition zones within the main flow patterns. Furthermore, the Locally Interpretable Model agnostic Explanation (LIME) algorithm for the first time was used to explain and interpret features in the WMS flow images that were contributing to the overall CNN classification scores. Finally, the CNN trained model was able to clas

关键词： Wire mesh sensor instrumentation Flow pattern classification Transitional zones Locally interpretable model-agnostic explanation Cross-sectional frame images threshold segmentation algorithm

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An Infrared Thermal Imaging Approach with Cooling Pre-treatment for Monitoring Cracks inside Metal Plate

An Infrared Thermal Imaging Approach with Cooling Pre-treatm...

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Chinese Automation Congress (CAC)

作者： Feng, XiaoDong Li, ZongShu Zhang, RuiQi Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781728176871

This article introduces the basis theory of infrared thermal non-destructive detection, and builds an infrared thermal wave image system for metal plates. Aiming at the difficulty to observe small defects in infrared thermal wave images, the paper innovatively proposes a method of applying refrigeration pre-processing, so that small defects can be observed on infrared images. Though a number of experiments, we select the optimal refrigeration temperature difference and observation time. In view of the serious interference of background noises in infrared thermal wave images, we study the homomorphic filtering enhancement methods for infrared thermal images. Then we use OTSU, KSW entropy and PSO algorithm to perform threshold segmentation on the enhanced images to effectively extract target defects. Finally, we quantitatively analyze the error of the extracted target defect areas.

关键词： infrared non-destructive detection thermal wave imaging refrigeration pre-processing threshold segmentation algorithm homomorphic filtering (key words)

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The internal parameter calibration based on sub-pixel threshold segment algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2018年第1期18卷 3-11页

作者： Guo, Chenxia Yang, Ruifeng Zhao, Luobin North Univ China Sch Instrument & Elect Taiyuan 030051 Shanxi Peoples R China North Univ China Automat Test Equipment & Syst Engn Res Ctr Shanxi Taiyuan 030051 Shanxi Peoples R China

The internal parameters is one of the important calibration parameters of visual measurement, which shows the accurate corresponding relation of the space point and the imaging point in computer image coordinate. Based on fiber optic imaging characteristics, the paper proposed the circular array targets and sub-pixel threshold segmentation algorithm to extract target feature points as to help the follow-up the accurate calibration of camera internal parameters. In contrast to some commonly uesd algorithm to extract the sub-pixel edge contour using interpolation and fitting, the proposed sub-pixel threshold segment algorithm is more small computational complexity and execution time. The experimental results show that the camera internal parameters have been obtained precisely with low execution time, which provides important guarantee for subsequent fiber length measurement based on visual measurement.

关键词： Visual measurement camera calibration internal parameters sub-pixels threshold segmentation algorithm

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Research of Screening Method Based on Glaucoma Image 2018

Research of Screening Method Based on Glaucoma Image

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3rd International Conference on Multimedia and Image Processing (ICMIP)

作者： Liu, Zhenyu Gao, Yakun Zhu, Sicheng Shenyang Univ Technol Shenyang Liaoning Peoples R China

ISBN: (纸本)9781450364683

Glaucoma is caused by the increase of intraocular pressure, and it is easy to cause blindness. By detecting cup-disk ratio (the ratio of optic cup area to optic disc area, CDR) of patients, Glaucoma can be screened. By using high-resolution retinal image in the database HRF, a set of complete screening method is proposed. First, this paper conducts channel extraction and image enhancement, and then uses threshold segmentation algorithm to segregate optic disc, and uses region growing method to segregate optic cup. Thus, CDR can be calculated automatically. This paper uses 30 images to test, 15 images are normal eye pictures, and the other 15 images are glaucoma ones. The result shows that CDRs of patients with glaucoma are greater than 0.6 in all of the 15 glaucoma pictures, and the CDRs of normal eye are between 0.2 and 0.6. And this is the same as the expert's judgment.

关键词： glaucoma region growing algorithm threshold segmentation algorithm cup-disc ratio

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Based on statistical analysis and 3D region growing segmentation method of liver

Based on statistical analysis and 3D region growing segmenta...

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2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

作者： Chenhui Wang Wenhong Qu Junbin Huang Lingquan Meng Software College Northeastern University Shenyang China Software College NortheasternUniversity Shenyang China

ISBN: (纸本)9781424488094

As the traditional image segmentation methods can not show the location targets of object clearly and accurately. The paper base on statistical analysis and 3D region growing segmentation method of the liver. First of all, the original abdominal volume data base on thresholding segmentation algorithm which is based on statistical analysis to achieve pretreatmcnt;Secondly, the volume data after pretreatment base on volume rendering which base on RC algorithm to achieve three-dimensional visualization of abdominal CT images;Then, based on 3D region growing algorithm for three-dimensional abdominal images of liver segmentation. Through the design of the liver segmentation experiment from abdominal CT images to validate the effectiveness of the method of paper.

关键词： Statistical analysis region growing algorithm volume data threshold segmentation algorithm

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