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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16210 条记录，以下是381-390 订阅

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Research on recognition and localization method of Gomoku based on image processing 8

Research on recognition and localization method of Gomoku ba...

引用

8th International conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Chen, Huanxi Li, Wei Southwest Forestry University School of Machinery and Transportation Kunming China

ISBN: (纸本)9798331506230

a method for recognition and localization of Gomoku based on image processing techniques is proposed in this paper. Firstly, the chessboard is recognized by image preprocessing technology, and the contour of the chessboard is extracted by means of adaptive binarization, convex packet detection and quadrilateral extraction, so as to obtain the vertices of the chessboard, and then the corners of the chessboard are obtained by computation based on the vertices of the chessboard. Subsequently, the types of pieces are identified by means of Hough transform and gray value, and the positioning of chess pieces is accomplished by comparing the positions of chess pieces and corner points. Through experimental testing, the findings demonstrate that the method introduced in this paper can successfully achieve Gomoku recognition and positioning, which serves as a reference for developing the vision systems of intelligent devices, including gaming devices. © 2024 IEEE.

关键词： Hough transforms

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Research and Optimization of Frequency Measurement algorithms Based on Monobit Receiver 8

Research and Optimization of Frequency Measurement Algorithm...

引用

8th International conference on Communication, image and Signal processing, CCISP 2023

作者： Yu, Haoxiang Wang, Jia Dai, Chen Chu, Ran Gu, Peng School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China Xi'an Institute of Space Radio Technology China Academy of Space Technology Xi'an China

ISBN: (纸本)9798350305838

In the design of conventional single-bit receivers, a high error rate is often observed due to interpolation direction errors near the quantized frequency points caused by the Rife algorithm. To address this issue, this study proposes an optimization technique using a mean filter. By integrating this technique with single-bit quantization and approximate kernel fast Fourier transform (FFT), a 38.4Gbps ultra-wideband single-bit digital receiver based on a single-bit analog-to-digital converter (ADC) and field-programmable gate array (FPGA) is designed. Subsequent MATLAB simulation and FPGA validation demonstrate that this receiver can reduce the frequency measurement error to within 3MHz, meeting the real-time or near real-time frequency measurement requirements for electronic warfare applications. © 2023 IEEE.

关键词： Fast Fourier transforms

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Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 IEEE.

关键词： image segmentation

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Reversible Data Hiding in Encrypted images based on Classic McEliece Cryptosystem 2

Reversible Data Hiding in Encrypted Images based on Classic ...

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Jiang, Zongbao Zhang, Minqing Di, Fuqiang Dong, Weina University of PAP Key Laboratory of Network and Information Security of PAP Engineering Shaanxi Xi'an710086 China

ISBN: (纸本)9798350331417

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms' embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm's embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security. © 2023 IEEE.

关键词： Classic McEliece error-correcting codes high-capacity hiding reversible data hiding in encrypted images

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Analysis of Decision Tree and Random Forest algorithms for Nutrient Deficiency Classification in Citrus Leaves through image processing 17

Analysis of Decision Tree and Random Forest Algorithms for N...

引用

17th International conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Kamelia, Lia Nurmalasari, Rin Rin UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350309164

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-Texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method's highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method. © 2023 IEEE.

关键词： Decision trees

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HVS-based AR Display image Enhancement With Delta Look-Up Table Considering Ambient Light 20

HVS-based AR Display Image Enhancement With Delta Look-Up Ta...

引用

20th International SoC Design conference (ISOCC)

作者： Cho, Donghun Lee, Hyeseong Lee, Jeonghun You, Jaehee Hongik Univ Integrated Syst Chip Seoul South Korea

ISBN: (纸本)9798350327038

Based on human visual systems, image processing algorithms, and efficient hardware implementation methodologies are proposed to optimize the image qualities of AR displays according to the changes in ambient lights. To this end, methods are described to improve the image qualities perceived by humans. In addition, the delta look-up table is presented to minimize the number of additional circuits without significant changes in existing hardware. HOSA, an image quality assessment based on the human visual system is used to verify the image qualities for the extreme ambient light conditions.

关键词： image quality enhancement AR display Ambient light Human visual system Delta LUT

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A Near-Duplicate Video Cleaning Method Based on AFENet Adaptive Clustering 8

A Near-Duplicate Video Cleaning Method Based on AFENet Adapt...

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8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Fu, Yan Duan, Rui Ye, Ou Xi'an University of Science and Technology College of Computer Science and Technology Xi'an China

ISBN: (纸本)9798350302455

In recent years, with the growth of video data, problems such as video quality degradation and copyright protection difficulties have arisen due to the redundancy of a large number of near-duplicate video data. Aiming at the problems of insufficient video feature extraction and low accuracy of clustering algorithms in existing near-duplicate video cleaning algorithms. This paper proposes a near-duplicate video cleaning method based on AFENet adaptive clustering. The method in this paper mainly includes two parts: Firstly, in the feature extraction stage, this paper adopts the spatiotemporal features of video and enhances the features of salient objects in each frame of image, enhancing the feature expression ability of video;Secondly, in the video cleaning stage, an adaptive feature distance threshold SFD-Means clustering algorithm is proposed, and a clustering optimization function is designed to overcome the defect of the original FD-Means algorithm that is too sensitive to threshold selection. Experimental results show that our proposed method has high recognition accuracy and cleaning performance, effectively improving the quality of video data. © 2023 IEEE.

关键词： Feature extraction

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Enhancing Palm Recognition Accuracy Through image processing Deep Learning Techniques 2

Enhancing Palm Recognition Accuracy Through Image Processing...

引用

2nd IEEE International conference on Data Science and Network Security, ICDSNS 2024

作者： Mercy, J. Sheela Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Infrared small target detection method based on full-direction dilation Gaussian filtering and local background estimation

Infrared small target detection method based on full-directi...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Sun, xiyu Pan, Shengda College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510677685

Addressing the limitations of currently rare small target detection algorithms based on Human Visual systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times. © 2024 SPIE.

关键词： image enhancement

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Deep Learning Approaches In Automatic Number Plate Recognition: Result and Analysis 4

Deep Learning Approaches In Automatic Number Plate Recogniti...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sharma, Rajeshwari Soni, Nandani Chaturvedi, Payashwani Malleri, Nimisha Bahety, Priyal Kukade, Jyoti Mishra, Trapti Medicaps University Indore India

ISBN: (纸本)9798331518981

systems known as Automatic Number Plate Recognition (ANPR), license plate recognition or LPR are now widely used in many sectors such as law enforcement, traffic control, vehicle access etc. This is a technology that uses object detection and optical character recognition to identify the numbers on number plates from input images or video. These systems use high-level hardware and software components together to automatically capture, recognize and process license plate information. Key elements of any ANPR system include high-resolution cameras, image processing algorithms, and optical character recognition (OCR) methods. The operational workflow of an ANPR system comprises several steps like image acquisition, preprocessing, license plate localization, character segmentation, OCR, and post-processing stages. Different applications of ANPR technology include traffic flow monitoring;helping police track stolen vehicles;congestion detection for traffic management purposes;and automated entry/exit processes based on authorized number plate recognition among others for supporting vehicle access control. A accuracy of 90% was achieved by proposed, ANPR systems represent a significant technological advance that improves accuracy;efficiency as well as automation in various critical areas of our lives. © 2024 IEEE.

关键词： image acquisition

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