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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Su, Yunxia Shanghai Publishing and Printing College Shanghai China

ISBN: (纸本)9798350315455

With the popularization of the internet, people have paid more and more attention to image processing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable and promising direction for development. This article first introduced text representation methods and two commonly used classifiers in machine learning, namely the manual annotation model method. Then, it elaborated on the effectiveness and current application status of a fusion of multi-layer feedforward technology and convolutional neural networks for edge detection, feature extraction, and other fields. Finally, a new concept was proposed, and deep learning was applied to explain the important significance of image preprocessing in digital music file classification. Afterwards, a communication field training model based on image recognition algorithms was simulated and tested for its functionality. The test results showed that the image recognition algorithm based on deep learning had good noise processing performance during the propagation process, with an image clarity rate of over 90%. Through deep learning algorithms, automatic recognition and classification of radio signals can be achieved, including determining signal type, modulation method, frequency range, and other information. This is of great significance for radio spectrum monitoring, spectrum management, and interference source localization. By using deep learning algorithms, classification models for different types of radio signals can be trained, making the recognition of radio spectrum more accurate and reliable. © 2023 IEEE.

关键词： communication field deep learning image recognition algorithms neural networks

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Pavement damage detection and evaluation based on UAV image and improved AHP

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CASE STUDIES IN CONSTRUCTION MATERIALS 2025年 22卷

作者： Zuo, Dunren Shao, Lei Chen, Bohan Tan, Jingxin Yang, Jiahao Chongqing Univ Sch Resources & Safety Engn Chongqing Peoples R China Chongqing Univ Coll Aerosp Engn Chongqing Peoples R China Chongqing Jiaotong Univ Sch Aeronaut Chongqing Peoples R China

As a crucial component of road infrastructure, the pavement significantly impacts the comfort of vehicle and pedestrian traffic. Various factors such as traffic flow, weather conditions, and material quality can lead to pavement damage. This paper utilizes UAVs to collect road damage images and establishes a classification and evaluation model by using image recognition algorithms and the analytic hierarchy process (AHP) theory. To verify the effectiveness of the model, a road in Chongqing was selected as the experimental object. This model can precisely and effectively distinguish different types of pavement damage, such as ruts, grooves, and so on. The results show that the classification accuracy of the model is 92.5 %, which has good detection performance compared with other models. In the research, it was found that the contrast of rut damage images varies greatly, ranging from 0.08 to 0.32. This difference may be due to the larger damage area present in the rut image compared to the other images. Considering groove and crack features are similar, there is a 66.7 % chance of identifying one as the other.

关键词： Pavement damage AHP Gray level co-occurrence matrix UAV image recognition algorithms

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Research on intelligent endpoint determination in copper converting based on machine learning

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CANADIAN METALLURGICAL QUARTERLY 2025年

作者： Zhao, Bi Hao, Haibin Xu, Jianxin Chen, Enlin Lu, Yongjie Wang, Hua Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650093 Peoples R China Kunming Univ Sci & Technol State Key Lab Complex Nonferrous Met Resources Cle Kunming 650093 Peoples R China Liangshan Min Ltd Co Liangshan Peoples R China

Copper converters are pivotal in the copper smelting process, playing a critical role in producing high-quality copper. Accurate endpoint determination in these converters is essential for maintaining copper quality and enhancing smelting efficiency. This study addresses challenges such as slag formation, the rudimentary level of intelligence in endpoint prediction, and low accuracy rates in converters. We propose a dynamic, multitask model that integrates converter operational conditions, data collection on smelting parameters, and dynamic endpoint prediction to intelligently predict the termination of copper and slag production. A multitask dynamic model based on a Deep Convolutional Neural Network (DCNN) was developed through the collection and analysis of data during the smelting process. This model predicts and optimises the smelting process within copper converters. Our results demonstrate that the model achieved an average prediction error of 1.9% for slag formation and 1.8% for copper production endpoint prediction. Furthermore, it attained hit rates of 97.8% and 98.3% for slag formation and copper production endpoints, respectively. Consequently, this model significantly enhances the accuracy of endpoint determination in slag and copper production, thus improving the efficiency and quality of output from copper converters. Les convertisseurs de cuivre sont essentiels dans le proc & eacute;d & eacute;de fusion du cuivre, jouant un r & ocirc;le critique dans la production de cuivre de haute qualit & eacute;. Une d & eacute;termination pr & eacute;cise du point final dans ces convertisseurs est essentielle pour maintenir la qualit & eacute;du cuivre et am & eacute;liorer l'efficacit & eacute;de la fusion. Cette & eacute;tude r & eacute;pond aux d & eacute;fis tels que la formation de scories, le niveau rudimentaire d'intelligence dans la pr & eacute;diction du point final et les faibles taux de pr & eacute;cision des convertisseurs. Nous proposons un mod & egr

关键词： Copper converting neural networks image recognition algorithms endpoint determination multitask learning

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Descriptive image Analysis: Part III. Multilevel Model for algorithms and Initial Data Combining in Pattern recognition

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PATTERN recognition AND image ANALYSIS 2020年第3期30卷 328-341页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This is the third article in a series of publications devoted to the current state and future prospects of Descriptive image Analysis (DIA), one of the leading and intensively developing branches of modern mathematical theory of image analysis. The fundamental problem of computer science touched by the article is the automation of extracting from images of information necessary for intellectual decision-making. A new class of models for the image analysis and recognition process and its constituent procedures is introduced and described - a multilevel model of image analysis and recognition procedures (MMCAI) - which is based on the joint use of methods of combining algorithms and methods of combining fragmentary initial data - partial descriptions of the object of analysis and recognition - an image. The architecture, functionality, limitations, and characteristics of the MMCAI are justified and defined. The main properties of the MMCAI class are as follows: (a) combining the fragments of the initial data and their representations and combining algorithms at all levels of image analysis and recognition processes;(b) the use of multialgorithmic schemes in the image analysis and recognition process;and (c) the use of dual representations of images as input data for the analysis and recognition algorithms. The problems arising in the development of the MMCAI are closely related to the development of the following areas of the modern mathematical theory of image analysis: (a) algebraization of image analysis;(b) image recognition algorithms accepting spatial information as input data;(c) multiple classifiers (MACs). A new class of models for image analysis is introduced in order to provide the following possibilities: (a) standardization, modeling, and optimization of Descriptive Algorithmic Schemes (DAS) that form the brainware of the MMCAI and processing heterogeneous ill-structured information - dual representations - spatial, symbolic, and numerical representations

关键词： image analysis descriptive image analysis mathematical theory of image analysis models of image analysis procedures image representations image models descriptive image representations descriptive image models dual representation of images combinatorial structures of local neighborhoods multiple classifiers support sets image recognition algorithms

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Ip-norm optimum filters for image recognition. Part I. algorithms

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Journal of the Optical Society of America A 1999年第8期16卷 1928-1935页

作者： Nasser Towghi Bahram Javidi Department of Electrical and System Engineering University of Connecticut U-157 Storrs Connecticut 06269-3357

Ordinarily, filters are derived from the optimization of certain expressions with respect to the mean squared metric. We construct a family of linear and nonlinear processors (filters) for image recognition that is l p -norm optimum in terms of tolerance to input noise and discrimination capabilities. The l p norm is the generalization of the usual mean squared ( l 2 ) norm, which we obtain by replacing the exponent 2 with any positive constant p (usually p ≥ 1 ) . These processors are developed by minimizing the l p norm of the filter output that is due to the input scene and the output that is due to input noise. We use the l p norm to measure the size of the filter output that is due to noise so that we can obtain greater freedom in adjusting the noise robustness and discrimination capabilities. We give a unified theoretical basis for developing these filters. This family of filters includes some of the existing linear and nonlinear filters, giving us a subfamilies of processors, which we denote by H q σ and H q . The values of q control the discrimination capabilities and the robustness of the processors. The parameter σ is the standard deviation of the noise process.

关键词： image recognition image recognition algorithms Joint transform correlators Linear filtering Matched filtering Phase only filters

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Noise robustness of nonlinear filters for image recognition

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2001年第9期18卷 2054-2071页

作者： Towghi, N Pan, LT Javidi, B Univ Connecticut Dept Elect & Syst Engn Storrs CT 06269 USA

We analyze the performance of the Fourier plane nonlinear filters in terms of signal-to-noise ratio (SNR). We obtain a range of nonlinearities for which SNR is robust to the variations in input-noise bandwidth. This is shown both by analytical estimates of the SNR for nonlinear filters and by experimental simulations. Specifically, we analyze the SNR when Fourier plane nonlinearity is applied to the input signal. Using the Karhunen-Loeve series expansion of the noise process, we obtain precise analytic expressions of the SNR for Fourier plane nonlinear filters in the presence of various types of additive-noise processes. We find a range of nonlinearities that need to be applied that keep the output SNR of the filter stable relative to changes in the noise bandwidth. (C) 2001 Optical Society of America.

关键词： Discrete Fourier transforms image recognition algorithms Matched filtering Nonlinear filtering Power spectra Spatial filtering

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Illuminant direction estimation for a single image based on local region complexity analysis and average gray value

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APPLIED OPTICS 2014年第2期53卷 226-236页

作者： Yi, Jizheng Mao, Xia Chen, Lijiang Xue, Yuli Compare, Angelo Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Bergamo Dept Human Sci I-24129 Bergamo Italy

Illuminant direction estimation is an important research issue in the field of image processing. Due to low cost for getting texture information from a single image, it is worthwhile to estimate illuminant direction by employing scenario texture information. This paper proposes a novel computation method to estimate illuminant direction on both color outdoor images and the extended Yale face database B. In our paper, the luminance component is separated from the resized YCbCr image and its edges are detected with the Canny edge detector. Then, we divide the binary edge image into 16 local regions and calculate the edge level percentage in each of them. Afterward, we use the edge level percentage to analyze the complexity of each local region included in the luminance component. Finally, according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model, we calculate the illuminant directions of the luminance component's three local regions, which meet the requirements of lower complexity and larger average gray value, and synthesize them as the final illuminant direction. Unlike previous works, the proposed method requires neither all of the information of the image nor the texture that is included in the training set. Experimental results show that the proposed method works better at the correct rate and execution time than the existing ones. (C) 2014 Optical Society of America

关键词： Computation methods Edge detection image processing image recognition algorithms Light properties Light sources

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New interpretations of Wiener filters for image recognition

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 1996年第7期13卷 1325-1330页

作者： Marom, E Inbar, H []Faculty of Engineering Tel Aviv University Tel Aviv 69978 Israel

Wiener biters for restoration of images and Wiener biters for image recognition are well established. We present the relations between two versions of Wiener restoration filters and develop new conceptual interpretations of the Wiener recognition filter, in the process of which interesting features are revealed. In one interpretation we show that the correlation signal resulting from Wiener recognition filtering may be regarded as the restoration of a hidden delta function. In another interpretation we regard the Wiener recognition filter as a two-filter scheme whereby an inverse filter and a Wiener restoration filter are operated sequentially. Results of simulations of the latter representation are presented for various input-noise models, providing useful insight into the implications of such an interpretation. (C) 1996 Optical Society of America

关键词： Fourier transforms image recognition algorithms Imaging noise Joint transform correlators Linear filtering Power spectral density

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Dictionaries for image and video-based face recognition [Invited]

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2014年第5期31卷 1090-1103页

作者： Patel, Vishal M. Chen, Yi-Chen Chellappa, Rama Phillips, P. Jonathon Univ Maryland UMIACS Ctr Automat Res College Pk MD 20742 USA Natl Inst Stand & Technol Gaithersburg MD 20899 USA

In recent years, sparse representation and dictionary-learning-based methods have emerged as powerful tools for efficiently processing data in nontraditional ways. A particular area of promise for these theories is face recognition. In this paper, we review the role of sparse representation and dictionary learning for efficient face identification and verification. Recent face recognition algorithms from still images, videos, and ambiguously labeled imagery are reviewed. In particular, discriminative dictionary learning algorithms as well as methods based on weakly supervised learning and domain adaptation are summarized. Some of the compelling challenges and issues that confront research in face recognition using sparse representations and dictionary learning are outlined. (C) 2014 Optical Society of America

关键词： Face recognition Feature extraction image processing image recognition algorithms Iris Saliency

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High precision angular displacement measurement based on self-correcting error compensation of three image sensors

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APPLIED OPTICS 2022年第1期61卷 287-293页

作者： Yu, Hai Wan, Qiuhua Lu, Xinran Zhao, Changhai Liang, Lihui Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

As the feedback link of a numerical control system, the measurement accuracy of absolute angular displacement measurements directly affects the control performance of a numerical control system. In previous research, angular displacement measurements based on dual image sensors can achieve higher measurement performance. However, the elimination of harmonic error by the dual image sensor is still limited. For this reason, this paper proposes an image-type angular displacement measurement method based on self-correction error compensation of three image sensors. First, the mathematical model of harmonic error is established, and the shortcomings of using dual image sensors to compensate the error are analyzed. Then, a high precision angular displacement measurement method based on three image sensors is proposed. Fmally, the self-correction error compensation method of three image sensors is applied to the angular displacement measurement system, and the measurement performance is verified. The experimental results show that a measurement accuracy of 1.76 '' can be achieved on the circular grating with a diameter of 96 mm. In contrast, the dual image sensor can only achieve a measurement accuracy of 2.88 ''. It is concluded that the odd number of image sensors can achieve higher measurement accuracy than the even number. This research lays a foundation for the realization of high precision image angular displacement measurement. (C) 2021 Optica Publishing Group

关键词： Charge coupled devices Digital image processing image recognition algorithms image sensors Precision metrology Spatial resolution

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