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

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Gait Recognition under Different Covariate Conditions using Deep Learning Technique 14

Gait Recognition under Different Covariate Conditions using ...

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14th IEEE International conference on Signal processing and Communications (SPCOM)

作者： Junaid, Iman Ari, Samit Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Gait as a biometric has become a popular research topic in recent years as a result of its numerous applications in sectors such as surveillance, authentication, and so on. It is capable of achieving detection at a distance that few other technologies can equal. It is still a difficult problem to solve since real human gait is influenced by several variable elements such as alterations in clothing, speed, and carrying situation. Also, unknown covariate circumstances may impact the training and testing settings for a specific individual in gait recognition. image sequences are typically used by computer-aided gait recognition systems without taking into account variables such as clothes and the contents of carrier bags while on the move. In this work, we provide a technique for selecting gait energy image-based (GEI) features, that is both effective and robust. The covariate factors have less impact on the given gait representation. A simple ten-layered convolutional neural network (CNN) is designed which intakes GEI as input. Several typical variations and occlusions that impact and worsen gait recognition ability are less susceptible to the suggested method. For both clothing and mobility variations, we used the CASIA datasets to assess our observations. The experimental findings reveal that in numerous circumstances, the deep neural network model created in this study achieved better results when compared with other existing algorithms.

关键词： Gait Recognition Covariates Normalization Gait Energy image Convolutional Neural Network

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Thermal image-Based Fault Detection Using Machine Learning and Deep Learning in Industrial Machines: Issues-Challenges and Emerging Trends 4th

Thermal Image-Based Fault Detection Using Machine Learning a...

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Kulkarni, Vaishnavi V. Hulipalled, Vishwanath R. Kundu, Mayuri Simha, Jay B. Abhi, Shinu School of CSE REVA University Bangalore India School of C&IT REVA University Bangalore India REVA University Bangalore India

ISBN: (纸本)9789819970926

Thermal imaging has long been utilized across industries to maintain electrical equipment and detect faults in machines, ensuring their reliable operation. Infrared thermography, or thermal imaging, has emerged as a powerful, non-contact, non-destructive monitoring tool. Unlike RGB imaging, it offers a non-intrusive approach that respects privacy and does not rely on ambient lighting. As heat signatures vary based on defects or machine conditions, infrared imaging is essential for effective condition monitoring. This survey study introduces the principles and applications of thermal imaging in fault detection and a comprehensive analysis of recent developments and approaches in the field. The survey encompasses various machine types, including electrical systems, mechanical components, and industrial equipment. Multiple techniques, algorithms, and deep learning models for image processing, feature extraction, and fault classification are extensively discussed. The paper also includes an in-depth literature review on fault detection in distinct machines, such as induction motors, rotating machines, and transformers. The survey aims to identify open challenges and issues in fault detection for different machines, such as induction motors and transformers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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Developing an Efficient image Encryption Algorithm for Secure Data Transmission in Healthcare systems 2

Developing an Efficient Image Encryption Algorithm for Secur...

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2nd IEEE International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Ali, Hassan Mohamed Reddy, P Niranjan Rao, S. Govinda Dixit, Krishna Kant Santhi, G.B. Kurukuntla, Srinivasa Reddy The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Telangana Hyderabad India GRIET Bachupally Department of CSDS Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India Teegala Krishna Reddy Engineering College Department of ECE Hyderabad India

ISBN: (纸本)9798350368109

As the use of Healthcare Information Technology has continued to rise, the issue of safe transfer of medical information with especial emphasis on images has become a major concern. The findings of this study propose a new approach to the encryption of images that is well suited to applications in healthcare and which guarantees the safety of medical images over weak networks. This proposed algorithm successfully implements a chaotic encryption method alongside wavelet transformation so as to design an effective and virtually invulnerable encryption method that does not consume an excessively large amount of computing power. There is also a way for the algorithm to be tested on X-Ray, CT, and MRI images so that the algorithm is not confined to one particular imaging modality. Such results clearly prove that the proposed algorithm provide better performance than AES as well as RSA with respect to encryption speed and sensitivity of the keys. image quality after decryption is also maintained making it possible to retain important information about the patient's health. Due to the algorithms high security, efficiency and capability of processing data in real time healthcare environment the proposed algorithm, provides a feasible solution for securing sensitive healthcare information and is a worthy addition to the literature on healthcare data security. © 2024 IEEE.

关键词： Electronic health record

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

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

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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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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation 6

Ingenious Waste Management: Implementing Machine Learning in...

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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 ...

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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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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

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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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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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Secure images Transmission Using a Three-Dimensional S-Box-Based Encryption Algorithm 5

Secure Images Transmission Using a Three-Dimensional S-Box-B...

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5th IEEE International conference on Advanced systems and Emergent Technologies (IC_ASET)

作者： Boussif, Mohamed Mnassri, Aymen Univ Tunis El Manar Fac Sci Tunis Tunis Tunisia

ISBN: (纸本)9781665427357

It is known that information security and privacy revolve around three principal keys: confidentiality, integrity, and availability. Depending on the environment, application, context, or use case, one of these principles may prevail over the others. For a medical application, for example, confidentiality is essential. Therefore, any imaging transferred electronically must be encrypted to prevent unauthorized persons from seeing its contents. This paper exhibits a new efficient ciphering algorithm for the security of transmitted images in public networks such as the Internet or archived in cloud computing based on a three-dimensional S-box. The suggested method encrypts and decrypts the image block by block. Each block is 8x8 pixels. The encrypted data are obtained by replacing the original data and the key by its value in S-box. Substitution boxes (S-box) are generated using a proposed algorithm that produces a 3D S-box using the random function and permutation. The security analyses show that the presented algorithm has an important security level. The runtime of the proposed algorithms is fast compared to others image encryption algorithms.

关键词： image processing Securing Communication images Encryption Three-Dimensional (3D) S-box

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