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7th International Conference on Digital Medicine and image processing, DMIP 2024

作者： Swathi, A. Prasad, Siva Madhavi, M. Gopal, Venu Nihaal Ahmed, K. Medical Image Processing Research Laboratory QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

As computer vision and image analysis technologies rapidly mature, they can revolutionize medical imaging, ushering in a new era of precision in diagnosis and treatment. In this research study, we explore innovative methodologies that leverage these technologies to enhance the image interpretation process and mitigate human error. Our findings reveal a significant increase in segmentation, classification, and anomaly detection accuracies achieved through deep learning-based models across various medical imaging modalities. These results underscore the transformative power of computer vision in healthcare, offering a promising path for more targeted and accurate interventions and instilling confidence in the effectiveness of these technologies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： computer Vision

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Teeth YOLACT: An Instance Segmentation Model Based on Impacted Tooth Panoramic X-Ray images

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computers, Materials and Continua 2025年第3期83卷 4815-4834页

作者： Tao Zhou Yaxing Wang Huiling Lu Wenwen Chai Yunfeng Pan Zhe Zhang School of Computer Science and Engineering North Minzu University Yinchuan 750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan 750021 China School of Medical Information & Engineering Ningxia Medical University Yinchuan 750004 China

The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched. However, due to the complex structure, low contrast, and complex background of teeth in panoramic X-ray images, the task of instance segmentation is technically tricky. In this study, the contrast between impacted Teeth and periodontal tissues such as gingiva, periodontal membrane, and alveolar bone is low, resulting in fuzzy boundaries of impacted teeth. A model based on Teeth YOLACT is proposed to provide a more efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films. Firstly, a Multi-scale Res-Transformer Module (MRTM) is designed. In the module, depthwise separable convolutions with different receptive fields are used to enhance the sensitivity of the model to lesion size. Additionally, the Vision Transformer is integrated to improve the model’s ability to perceive global features. Secondly, the Context Interaction-awareness Module (CIaM) is designed to fuse deep and shallow features. The deep semantic features guide the shallow spatial features. Then, the shallow spatial features are embedded into the deep semantic features, and the cross-weighted attention mechanism is used to aggregate the deep and shallow features efficiently, and richer context information is obtained. Thirdly, the Edge-preserving perception Module (E2PM) is designed to enhance the teeth edge features. The first-order differential operator is used to get the tooth edge weight, and the perception ability of tooth edge features is improved. The shallow spatial feature is fused by linear mapping, weight concatenation, and matrix multiplication operations to preserve the tooth edge information. Finally, comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image datasets. The results show that the APdet, APseg, ARdet, ARseg, mAPdet, and mAPseg indicators of the proposed model are 89.9%, 91.9%, 77.4%, 77.6%, 72.8%, an

关键词： The oral panoramic X-ray instance segmentation impacted teeth vision transformer the edge-preserving

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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Three-dimensional survey of submarine sedimentary trails by means of spaceborne remote sensing

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GeoJournal 1991年第1期24卷 93-93页

作者： Buchroithner, M. Joanneum Research Institute for Image Processing and Computer Graphics Graz Austria

Precise geometric description of river sediment trails in the sea may assist in a better understanding of the complex processes happening in this type of environment. By means of stereo restitution of a Space Shuttle Large Format Camera photo pair it could be shown that a three-dimensional survey of the margins of individual sediment trails is possible. It turns out, however, that the positioning of the measuring mark is not always easy, and that this type of plotting needs a well-experienced operator. The bathymetric information obtained is brought in relation to sedimentological aspects.

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on Mobile Computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Dynamics of Masked image Modeling in Hyperspectral image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Ma, Chen Li, Huayi Jiang, Junjun Aybar, Cesar Yao, Jiaqi Camps-Valls, Gustau Harbin Institute of Technology Research Center of Satellite Technology State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster School of Astronautics Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China University of València Image Signal Processing Laboratory València46980 Spain Tianjin Normal University Tianjin300387 China

Masked image modeling (MIM), a common self-supervised learning (SSL) technique, has been extensively studied for remote sensing (RS) image processing. Nevertheless, its effectiveness for hyperspectral imagery (HSI) remains underexplored due to the distinct data structures and high dimensionality. This article aims to provide a detailed understanding of MIM from different perspectives of representation learning and statistical analysis for HSI classification tasks. Our study reveals that the MIM paradigm injects inductive bias in the attention mechanism of the transformer model, which is advantageous for capturing the local discrepancies between the spectra. We also show that MIM can increase the diversity of the attention heads in every layer, which is beneficial for the model in extracting more discriminative features from different spectral bands. The similarity of representations from various layers further proves this. Furthermore, our investigation highlights how MIM introduces a dynamic perspective to spectral representations, enabling the model to learn more robust and discriminative features. The final numerical experiments indicate that a moderate mask ratio can enhance the performance of downstream tasks. This suggests that designing a more targeted masking strategy might be necessary to achieve higher and more stable gains in downstream task performance. Without bells and whistles, the vanilla MIM improves the overall classification accuracy by an average of 2.69% over its supervised learning (SL) counterpart. We hope that our findings can advance the understanding of MIM in HSI and inspire the design of a more stable SSL paradigm for HSI processing. © 1980-2012 IEEE.

关键词： Self-supervised learning

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COMPRESSION OF BINARY FACSIMILE imageS BY PREprocessing AND COLOR SHRINKING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1990年第9期38卷 1592-1598页

作者： ALGAZI, VR KELLY, PL ESTES, RR Graphics Image and Vision Engineering Research Laboratory CIPIC Center for Image Processing Integrated Computing University of California Davis CA USA

Algorithms for preprocessing, global modeling and segmentation, local modeling and representation, and binary encoding of facsimile images are proposed, and their performance is examined. Some binary morphological operations that can be used to improve the quality and increase the compressibility of binary images and the modeling of binary images are discussed. The techniques are incorporated into a set of comprehensive encoding schemes, the PCSE codes. Their effectiveness in compressing the International Telegraphy and Telephony Consultative Committee (CCITT) binary images is demonstrated. The PCSE code using preprocessing and color shrinking prior to encoding is shown to outperform the READ code by from 7 to 42% for the standard CCITT test images with a very small change in image quality.< >

关键词： image coding Facsimile image segmentation Morphological operations Encoding Telegraphy Telephony Code standards Testing image quality

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DATA STRUCTURE FOR INTERACTIVE ATTRIBUTE ASSIGNMENT IN GEOGRAPHICAL INFORMATION SYSTEMS.

DATA STRUCTURE FOR INTERACTIVE ATTRIBUTE ASSIGNMENT IN GEOGR...

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Technical Papers, 45th Annual Meeting - ACSM: Theodolite to Satellite. Papers presented at the 1985 ASP-ACSM Convention (American Society of Photogrammetry).

作者： Kainz, Wolfgang Research Cent Joanneum Inst for Image Processing & Computer Graphics Graz Austria Research Cent Joanneum Inst for Image Processing & Computer Graphics Graz Austria

This paper presents data structures and procedures for attribute assignment in geographical information system. Despite suggestions to apply conventional data base management systems for the manipulation of both spatial and attribute data we take a different approach. Spatial data is stored in a way which takes into account the special nature of spatial information. Thematic information is represented hierarchically. For each subject a global theme file describes the hierarchy, data fields and types of each attribute item for each level. A copy of this structure gives the local theme file. Attributes may be assigned to spatial items, and vice versa, by an interactive process during or after digitization. Relations between corresponding items are established via a link file with pointers in either direction thus providing even multiple assignments.

关键词： MAPS AND MAPPING

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RELIEF MAPPING USING NONPHOTOGRAPHIC SPACEBORNE imageRY

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 1989年第1期44卷 21-36页

作者： RAGGAM, J BUCHROITHNER, MF MANSBERGER, R Institute for Image Processing and Computer Graphics Joanneum Research Graz Austria Institute for Applied Geodesy and Photogrammetry University of Technology Graz Austria

Three-dimensional restitution of images with unconventional imaging geometry can be performed in an operational mode using existing commercial analytical plotters without any extra investments in hardware. The paper presents two programme systems developed for the Kern DSR analytical stereo-plotter for mapping with spaceborne stereo-imagery, one for images acquired by the SPOT HRV sensor, the other one for stereo-mensuration of SAR images. Due to the movement of the satellite platforms the software requires appropriate algorithms for the consideration of time-varying orientation parameters like sensor position, sensor attitude or others. The performance of the programme systems is demonstrated with practical results derived from Space Shuttle SIR-B imagery as well as from SPOT images showing realistically achievable accuracies of a few pixels in planimetry.

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