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Application of temperature sensors and edge detection algorithms optimized based on thermal energy consumption in dance posture training

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 58卷

作者： Jin, Shanshan Chifeng Univ Educ Sci Coll Chifeng 024000 Inner Mongolia Peoples R China

In recent years, with the development of sensor technology and edge computing, new possibilities for dance posture training have been provided. The aim of this study is to develop a temperature sensor and edge detection algorithm based on thermal energy consumption optimization to improve the efficiency and accuracy of dance pose training. Wearable devices containing high-precision temperature sensors are able to monitor temperature changes in various parts of the dancer's body in real time. Through wireless transmission technology, this data is sent to edge computing devices for processing. edge computing devices use edge detection algorithms to quickly analyze a dancer's thermal consumption patterns and identify which postures or movements are causing excessive energy consumption. The results show that the temperature sensor and the edge detection algorithm can effectively monitor and analyze the dancers' thermal energy consumption. In the experiment, dancers trained with the system were able to get real-time feedback about changes in their body temperature, so that they could adjust their movements and training intensity in time. Compared with traditional training methods, dancers using this system have shown significant improvement in the optimization of energy consumption, training efficiency and effect have been improved, not only improve the scientific and accurate training, but also provide real-time feedback for dancers to help them manage their energy consumption more effectively.

关键词： Thermal energy consumption optimization Temperature sensor edge detection algorithm Dance posture Training application

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An Improved edge detection algorithm Based on Mathematical Morphology and Directional Wavelet Transform 8

An Improved Edge Detection Algorithm Based on Mathematical M...

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Proceedings 2015 8 International Congress on Image and Signal Processing (CISP)

作者： Zhang, Weiguo Shi, Dan Yang, Xiaoqiang Xian Univ Sci & Technol Coll Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781467390989

Traditional methods of the edge detection can't completely extract the low frequency image edge. It is easy for them to discard some important information in the high frequency sub-images. If there is much noise in the image, these methods can not entirely eliminate the noise. We will get the poor image edge because of the noise. In order to solve these problems, we respectively analyze the theories of the mathematical morphology algorithm and the directional wavelet transform. We propose an improved edge detection algorithm based on the mathematical morphology algorithm and the directional wavelet transform. We firstly use the binary mathematical morphology to de-noise and detect the edge of the low frequency sub-image in the wavelet domain. Then we de-noise the high frequency sub-image in horizontal, vertical and diagonal directions. We adopt the Canny operator to detect the edge of the high frequency image. The low frequency edge image and the high frequency edge image can be respectively got by the above algorithms. Finally we can get a complete and continuous edge by using some fusion rules. The results show that the improved edge detection algorithm can well suppress the noise interference. It also can get a more continuous and complete edge image comparing to other methods.

关键词： mathematical morphology directional wavelet filtering edge detection algorithm fusion rules

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Quality Assessment Methods to Evaluate the Performance of edge detection algorithms for Digital Image: A Systematic Literature Review

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IEEE ACCESS 2021年 9卷 87763-87776页

作者： Tariq, Nazish Hamzah, Rostam Affendi Ng, Theam Foo Wang, Shir Li Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Malaysia Univ Tekn Malaysia Melaka Fak Teknol Kejuruteraan Elekt & Elekt Durian Tunggal 76100 Malaysia Univ Sains Malaysia Ctr Global Sustainabil Studies CGSS Hamzah Sendut Lib Level 5 Minden 11800 Malaysia Univ Pendidikan Sultan Idris Fac Art Comp & Creat Ind Tanjung Malim 35900 Malaysia

A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.

关键词： Image edge detection detection algorithms Quality assessment Systematics Bibliographies Image segmentation Digital images Digital image processing edge detection algorithm image segmentation assessment validation quality measures reviews

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Research and Application of Log Defect detection and Visualization System Based on Dry Coupling Ultrasonic Method

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Journal of Renewable Materials 2023年第11期11卷 3917-3932页

作者： Yongning Yuan Dong Zhang Usama Sayed Hao Zhu Jun Wang Xiaojun Yang Zheng Wang College of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjing210037China College of Materials Science and Engineering Nanjing Forestry UniversityNanjing210037China College of Civil Engineering Nanjing Forestry UniversityNanjing210037China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent *** detection and visualization analysis of internal log defects were realized through log specimen *** main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively *** system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs *** content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

关键词： Ultrasonic method log defect detection visualization system dry coupling B-scan pulse transmission method bilinear image interpolation algorithm edge detection algorithm

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A Model for Detecting Fungal Diseases in Cotton Cultivation using Segmentation and Machine Learning Approaches

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 628-636页

作者： Odukoya, O. H. Aina, S. Degbesse, F. W. Obafemi Awolowo Univ Comp Sci & Engn Dept Ife Nigeria

This research detailed a model for detecting fungal diseases via techniques for processing images of cotton leaves. The work allowed to develop a model based on the set of preprocessed data, to formulate the developed model, to simulate and evaluate the model. It is about detecting fungal diseases in cotton cultivation. The image data records were collected in an online data repository consisting of images of cotton leaves infected with fungal diseases and normal leaf images. In addition, other images of infected and uninfected cotton leaves were collected in cotton production fields in the Segbana region in Benin Republic. The model was formulated based on watershed segmentation technique by applying edge detection algorithm and K-Means Clustering;and Support Vector Machine (SVM) for classification. The simulation was done using MATLAB with Image Processing Toolbox 9.4. The results gave an accuracy of 99.05%, specificity 90%, misclassification rate 0.95%, recall rate 99.5% and precision 99.5%. In addition, with less computational effort and in less than a minute, the best results were obtained, showing the efficiency of the image processing technique for the detection and classification of infected and uninfected leaves. It was concluded that this approach was applied to detect fungal diseases on cotton leaves to promote the production and harvest of good quality cotton and valuable cotton products.

关键词： Fungal diseases watershed segmentation SVM K-means edge detection algorithm

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Assessment of blasting impacts on the discontinuities in a salt stope and pillar mine using a developed image processing

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第4期81卷 1-14页

作者： Kashtiban, Yavar Jalili Shahriar, Kourosh Bakhtavar, Ezzeddin Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Univ Quebec Chicoutimi Dept Appl Sci Chicoutimi PQ Canada Urmia Univ Technol Fac Environm Dept Min Engn Band Rd Orumiyeh *** Iran

Salt rocks exhibit a complex mechanical behaviour by reflecting large deformations at low deviatoric stresses. Since the propagation and creation of cracks due to blasting can disrupt the mine safety and cause irreparable failures in underground spaces, it is important to assess the impacts of blasting on different parts of a salt stope and pillar mine after each blasting round. This study assessed the impacts of blasting operation on discontinuities in a salt stope and pillar mine by using a developed image processing-based methodology. Various photo samples were collected from different parts of the mine pillars and walls. Photographs were taken at four different blasting rounds before and after each round. The Sobel filter for edge detection was developed in Matlab, and the photographs were processed to assess the impacts of blasting on the discontinuities. The assessment was performed by comparing the processing results before and after each blasting round for the photographs. The effects of distance from blasting points, detonation direction and the amount of charge per each blasting round were considered in the assessment process. The amount of charge per blasting rounds and distance from the blasting points indicated higher effects than detonation directions. The assessment results indicated that the impacts of the blasting operation on the discontinuities decreased and became almost ineffective at distances greater than 30 m. Also, the points located at the end of the mine, as a closed space, experienced the greatest impact of the blasts.

关键词： Discontinuities Underground blasting Stope and pillar Image processing edge detection algorithm

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Application of fingerprint image fuzzy edge recognition algorithm in criminal technology

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OPEN COMPUTER SCIENCE 2023年第1期13卷

作者： Lv, Xinhua Shandong Univ Polit Sci & Law Jinan 250014 Shandong Peoples R China Univ Shandong Key Lab Evidence Identify Jinan 250014 Shandong Peoples R China

In the context of the rapid development of science and technology and the modernization of the legal system, criminal activities are becoming more and more intelligent and technological, which also puts forward higher requirements for criminal technology. The current criminal technology equipment is relatively backward, and the technical level is not high enough, resulting in a low utilization rate of trace material evidence extraction, which directly affects the role of criminal technology in the investigation and solving of cases. In recent years, fingerprint recognition algorithms and image edge detection algorithms have been widely used in various fields. This work studied the application of fingerprint image fuzzy edge recognition algorithm in criminal technology, in order to improve the level of criminal technology and the utilization rate of physical evidence extraction. The criminal technology system is upgraded and optimized by combining fingerprint recognition algorithm and image edge detection algorithm. And fuzzy theory is added to ensure the feasibility of the research. The experimental results show that the fuzzy edge recognition algorithm of fingerprint image can improve the level of criminal technology and the utilization rate of material evidence to a certain extent. The utilization rate is increased by 7.04%. The recognition accuracy of the fuzzy recognition method is also 13.2% higher than that of the methods in the literature.

关键词： criminal technology fingerprint recognition algorithm edge detection algorithm fuzzy theory image enhancement

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Design of a Groundwater Level Monitoring System Based on Internet of Things and Image Recognition

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TRAITEMENT DU SIGNAL 2021年第4期38卷 979-984页

作者： Bo, Qiuyu Cheng, Wuqun Agr Univ Hebei Inst Urban & Rural Construct Baoding 071001 Peoples R China

This paper designs an intelligent groundwater level monitoring system based on image recognition and Internet of things (IoT). Image recognition technology was employed to process the water level image, and determine the water level line. The IoT was adopted to transmit the collected multimedia data accurately to the monitoring end, thereby realizing the automatic remote monitoring of real-time water level. After analyzing the image recognition technology and the key algorithm of water level recognition, the authors designed the whole process of groundwater level monitoring with two modules: water level monitoring base station, and remote monitoring management center. The water level monitoring base station is embedded with a data acquisition module to periodically collect data, including water level, videos, and images. The collected data were sent to the remote monitoring management center through the cellular network. Then, flood or low water warning could be determined according to the historical data. Finally, the proposed groundwater level monitoring system was tested. The results show that the system not only solves the problem of measurement accuracy, but also improves the work efficiency.

关键词： image recognition internet of things (IoT) groundwater level monitoring edge detection algorithm

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Model-based contour extraction: an enabler for very low-frame SEM images metrology 36

Model-based contour extraction: an enabler for very low-fram...

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Conference on Metrology, Inspection, and Process Control XXXVI Part of SPIE Advanced Lithography and Patterning Conference

作者： Sezestre, Elie Scoarnec, Juline Pradelles, Jonathan Perraud, Loic Fay, Aurelien Berard-Bergery, Sebastien Bustos, Jessy Henry, Jean-Baptiste Dubreuil, Olivier Mendes, Ivanie Valade, Charles Moly, Alexandre Batte, Alice Schuch, Nivea Robert, Frederic Figueiro, Thiago Univ Grenoble Alpes CEA Leti F-38000 Grenoble France ASELTA NANOGRAPH 4 Pl Robert Schuman F-38000 Grenoble France

ISBN: (纸本)9781510649828;9781510649811

Among metrology tools in the semi-conductor manufacturing, critical dimension scanning electron microscopes (CD-SEM) are the most broadly used, especially due to their high resolution, low destructivity, and high throughput. Contour metrology on CD-SEM images has become essential for characterization, modelling, and control of advanced lithography processes. In particular, OPC model's accuracy can be highly improved using contours metrology. One of the issues when dealing with CD-SEM metrology is that the results are noise sensitive. Moreover, diminishing noise in CD-SEM acquisition leads to resist shrinkage due to exposure time increase. In addition, post-treatment of these shrinkage effects requires compensation algorithms such as artificial intelligence (AI)driven algorithms, that are another contributor to the error budget of metrology systems. There is thus a need for an accurate, robust to noise, and purely deterministic edge detection algorithm. In this article, we evaluate the benefits of relying on a model-based contour extraction approach for performing measurements. This approach is applied onto both synthetic and experimental CD-SEM images with various patterns (mostly 2D) and noise levels to assess the influence of image integration (frame number) on the contour detection and CD measurement. We demonstrate that a model-based contour extraction algorithm is able to precisely characterize SEM-induced 2D resist shrinkage. We observe that this model-based approach is more robust to noise than standard algorithms by 21% on synthetic data and by 36% on experimental data. Another way of seeing it is, while keeping the same precision, a model-based contour extraction approach can significantly reduce the requested image frame number. The benefits of adopting this approach range from reducing the shrinkage effects to improving SEM image acquisition time. Eventually, no step of shrinkage modelling calibration nor AI-driven image post processing are needed which i

关键词： contour metrology edge detection algorithm robustness to noise ultra-low frame resist shrinkage NTD process CD-SEM accuracy

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Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network

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AUTOMATION IN CONSTRUCTION 2018年 94卷 203-213页

作者： Nhat-Duc Hoang Quoc-Lam Nguyen Van-Duc Tran Duy Tan Univ Inst Res & Dev Fac Civil Engn P809-03 Quang Trung Danang 550000 Vietnam Duy Tan Univ Fac Civil Engn P809-03 Quang Trung Danang 550000 Vietnam Duy Tan Univ Int Sch 254 Nguyen Van Linh Danang 550000 Vietnam

Crack detection is a crucial task in periodic pavement survey. This study establishes and compares the performance of two intelligent approaches for automatic recognition of pavement cracks. The first model relies on edge detection approaches of the Sobel and Canny algorithms. Since the implementation of the two edge detectors require the setting of threshold values, Differential Flower Pollination, as a metaheuristic, is employed to fine-tune the model parameters. The second model is constructed by the implementation of the Convolution Neural Network (CNN) - a deep learning algorithm. CNN has the advantage of performing the feature extraction and the prediction of crack/non-crack condition in an integrated and fully automated manner. Experimental results show that the model based on CNN achieves a good prediction performance of Classification Accuracy Rate (CAR) = 92.08%. This performance is significantly better than the method based on the edge detection algorithms (CAR = 79.99%). Accordingly, the proposed CNN based crack detection model is a promising alternative to support transportation agencies in the task of periodic pavement inspection.

关键词： Pavement crack detection Convolution neural network Image processing edge detection algorithm Deep learning Metaheuristic

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