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

共 16444 条记录，以下是1511-1520 订阅

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Review of Reconstruction Methods for Spectral Snapshot Compressive Imaging 11th

Review of Reconstruction Methods for Spectral Snapshot Compr...

引用

11th International conference on Communications, Signal processing, and systems, CSPS 2022

作者： Yuan, Hao Ding, xiaoming Yan, Qiangqiang Wang, xiaocheng Li, Yupeng Han, Tingting Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin300387 China CAS Key Laboratory of Spectral Imaging Technology Xi’an Institute of Optics and Precision Mechanics Xi’an710119 China

ISBN: (纸本)9789819926527

Snapshot compressive imaging (SCI) uses a 2D sensor to obtain higher dimensional data and then reconstructs the underlying high-dimension data by elaborate algorithms. Applying SCI to capture hyperspectral images is known as spectral SCI. Although this technique has been proposed for more than a decade, it has not been widely used, mainly because its reconstruction accuracy and reconstruction speed are not yet satisfactory, which is the research focus on spectral SCI. This paper investigates the literatures on reconstruction methods of spectral SCI, mainly involving coded aperture optimization, model-based reconstruction algorithms and deep learning-based reconstruction algorithms. In this paper, we also provide a summary of studies on noise modeling and denoising for reconstructed spectral SCI data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

引用

5th International conference on Computer Vision, image and Deep Learning, CVIDL 2024

作者： Wu, xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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False Correspondence Removal via Revisiting Semantic Context with Position-Attentive Learning

False Correspondence Removal via Revisiting Semantic Context...

引用

IEEE International conference on image processing

作者： Ruiyuan Li Zhaolin xiao Meng Zhang Haonan Su Haiyan Jin Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

False correspondence removal remains a challenge for many feature-matching-based applications. This paper proposes a solution that revisits the local semantic context via position-attentive learning. First, a cross-divisional module is introduced to extract semantic features from image-patch pairs. Then, through a position-attentive mechanism, the parametric positions and extracted semantic features are jointly utilized to compute the probabilities of a set of putative correspondences. The proposed approach is evaluated on several indoor and outdoor challenging datasets, containing up to 70%+ false correspondences. In most cases, the solution outperforms existing algorithms in terms of matching precision and F1-score. Furthermore, an ablation study indicates that revisiting the semantic context improves precision by nearly 5%. The code is available at the link below 1 .

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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation 6

Ingenious Waste Management: Implementing Machine Learning in...

引用

6th IEEE International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Asha, V. Nithya, B. Govindaraj, M. Brunda, T.M. Hiremath, Ashwini Jayaprakash, B. New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306613

Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promising results and revolutionized the waste separation process. However, this research paper highlights the gaps that exist in the understanding and efficacy of machine learning for waste separation. This study provides an overview of state-of-the-art techniques for waste separation using machine learning, including the use of computer vision, sensor technology, and neural networks. This paper portrays case studies with empirical data from various waste management establishments to demonstrate the potential advantages of machine learning for recycling and waste classifying. Although progress has been made, this article highlights some significant gaps in our knowledge. These gaps include the need for standardized datasets, optimization of algorithms for real-world scenarios, the impact of technology on the environment, and the socio-economic aspects of integrating automation into waste management. The essential and most important tasks of intelligent mechanisms in the digital age is waste separation. Segregation of waste is necessary to apply proper disposal and waste management strategies. The existing systems use drones to identify waste by using deep learning, image processing, GPS, and GSM approaches, locating and notifying the authorities about them. The improvement made is in the analysis and utilize multi-object detection and picture classification to accomplish trash segregation. Waste management may be effectively accomplished with higher accuracy through Machine Learning practices which therefore considerably lowers labor expenses. © 2024 IEEE.

关键词： image Detection Machine Learning Prediction Sensor Waste Segregation

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Evaluating Cyberbullying Detection Algorithm Performance in Text and image Analysis 2

Evaluating Cyberbullying Detection Algorithm Performance in ...

引用

2nd International conference on Machine Learning, Control, and Robotics, MLCR 2023

作者： Ea, Philippe xiang, Jiahui Salem, Osman Mehaoua, Ahmed Université Paris Cité Centre Borelli Umr 9010 Paris France

ISBN: (纸本)9798350381245

Cyberbullying has become a widespread concern in our society, and its detection has grown in significance. In recent years, machine learning algorithms have exhibited considerable potential in detecting cyberbullying. This paper presents an approach for detecting it employing Natural Language processing and image analysis methods to analyze both textual and visual content. The study utilized an online comment dataset from Twitter and a self-created image dataset, where labeling was done for cyberbullying and non-cyberbullying, while tweets underwent detailed labeling. Multiple models were tested to determine the most suitable one. The Support Vector Machine model achieved an accuracy rate of 81.4% on our text dataset, according to our experimental results. The model achieved a remarkable performance with Area Under the Curve scores exceeding 0.97 in classifying comments based on age, ethnicity, gender, and religion. For cyberbullying detection in image data, pre-trained models with CNN architecture were utilized. Regrettably, our experiments in this regard failed to produce satisfactory outcomes, possibly due to varying interpretations of visual content among individuals. We will further discuss this issue in our paper. Overall, our approach offers a potentially effective solution for identifying instances of cyberbullying in textual content, which could be utilized in the development of tools and systems aimed at preventing cyberbullying and promoting online safety. © 2023 IEEE.

关键词： image classification

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Design and Implementation of Real-Time Underwater Acoustic Multimedia Transmission Modem

Design and Implementation of Real-Time Underwater Acoustic M...

引用

IEEE International conference on Information Communication and Signal processing (ICICSP)

作者： Jie xi Yulin Jiang Yifan Wang Yan Wei xingbin Tu Fengzhong Qu Ocean College Zhejiang University Zhoushan China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhejiang University Zhoushan China Hainan Institute of Zhejiang University Sanya China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

With the escalating demand for image observation and video transmission in underwater environments, real-time wireless multimedia transmission stands as the pivotal stride in the advancement of ocean exploration and observation systems. While acoustic communication remains the most dependable solution for underwater wireless communication, multimedia transmission still grapples with the challenges of limited band-width and poor channel quality. In this paper, we introduce a self-developed underwater acoustic modem especially tailored to real-time multimedia transmission. This comprehensive design spans across hardware components, the application layer, data link layer, and the physical layer. The utilization of multi-band retransmission mechanisms and sophisticated physical layer algorithms contributes to the system's resilience against the complexities of the underwater acoustic channel. Subsequent communication experiments have been conducted in both Zhoushan, Zhejiang, and the South China Sea, focusing on horizontal and vertical underwater acoustic channels respectively. Through these trials, the system's demodulation and retransmission capabilities have been duly validated, resulting in the successful transmission of multimedia video files on multiple occasions.

关键词： Wireless communication Oceans Signal processing algorithms Streaming media Signal processing Physical layer Modems Real-time systems Multimedia communication Underwater acoustics

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Fast Star Detection Algorithm Based on Dyadic Wavelet Transform

Fast Star Detection Algorithm Based on Dyadic Wavelet Transf...

引用

International conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Zhanglei Chen, Bing Zheng, Yong Chen, xiao Information Engineering University Henan Zhengzhou450000 China

ISBN: (纸本)9789811966125

A star sensor takes the stars as the reference sources for attitude measurement to output the star vectors in the coordinate system of the star sensor, providing high-precision measurement data for the attitude control of spacecraft and astronomical navigation systems. Star detection is one of the key technologies in the data processing of star sensors. In this paper, a fast star detection method based on the à Trous algorithm of dyadic wavelet transform is proposed to solve the problem of the long elapsed time of star detection algorithms in the spatial domain. By analyzing the characteristics of stars, a suitable dyadic wavelet base is constructed to perform wavelet transform on two-dimensional star maps. The background and stars are separated according to the wavelet coefficient maximum at a specific scale, and the corresponding method is designed to separate individual stars. The simulation results show that the proposed algorithm can effectively detect stars of different magnitudes, and has good robustness to strong light interference and white Gaussian noise of different power. Compared with other star detection algorithms, the algorithm proposed has a shorter elapsed time, only taking 0.40–0.43 s to detect all stars in the simulated star map. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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UAV image Analysis for Road Surface Violation Detection

UAV Image Analysis for Road Surface Violation Detection

引用

2022 International Siberian conference on Control and Communications, SIBCON 2022

作者： Kataev, Mikhail Kartashov, Eugeny Avdeenko, Vladislav Tomsk Russia

ISBN: (纸本)9781665476287

Violations of the roadway leads to a decrease in the level of driver safety and the condition of the car, reduces the speed of movement. Violations include cracks, pits, ruts, the size of which can change every day, depending on weather conditions and traffic intensity. One of the well-known approaches for detecting violations of the roadway is technical vision and the entire set of image processing algorithms. The article considers a variant of solving the problem of detecting violations of the road surface from images obtained with the help of unmanned aerial vehicles. The approach is based on classical crack boundary detection algorithms and real images of the road are considered. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Assistant Recognition Method Based on Multi-Scale Feature Algorithm

An Assistant Recognition Method Based on Multi-Scale Feature...

引用

Data Science and Network Security (ICDSNS), International conference on

作者： Yawei Han Xi'an Eurasia University Xi'an Shaanxi China

The auxiliary recognition method based on multi-scale feature algorithms is a technology used to improve the accuracy of image recognition. Traditional image recognition algorithms often face difficulties in processing images with rich details, as changes in details may lead to difficulty in feature extraction and a decrease in accuracy. To overcome this problem, multi-scale feature algorithms have been introduced, which can analyze images at different scales and capture feature information at different levels. The Committed step of this method include the following aspects: First, the original image is divided into multiple sub images of different scales by using image pyramid technology. Then, for each sub image, feature extraction algorithms are used to extract its feature representation. These features can be local features, global features, or mixed features, selected according to specific application requirements. Next, by fusing features at different scales, a comprehensive feature representation is obtained. Finally, this feature representation can be used for image recognition tasks such as object detection, classification, etc.

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Identification of a Person Based on Block processing of a Signature image

Identification of a Person Based on Block Processing of a Si...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Ildus Rizaev Elza Takhavova Mikhail Shleimovich Automated Systems for Information processing and Control Department Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russia

The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processing systems play a crucial role. There is a lot of research in this direction, and one of the most difficult problems is handwritten text recognition. This includes the tasks of signature identification, which the authors of the paper consider. The article considers the main stages of signature analysis to establish authorship. They are the preprocessing of the signature, representation in a form convenient for applying author identification. A dataset containing sample signatures of various people is used. The described tools became means to develop software for preprocessing images of signatures, extracting characteristics and identifying the author of the signature.

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