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

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Informational image of a Person's Gait According to Mobile Phone Data

Informational Image of a Person's Gait According to Mobile P...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Dorofeev, Nikolay Grecheneva, Anastasya Sharapov, Ruslan Vladimit State University Murom Institute Vladimir Russia

ISBN: (纸本)9781665464291

The article describes the results of the research of an information digital model (map) of a person's gait based on preliminary processing of mobile phone accelerometer signals in biometric authentication systems, as well as a personal health monitoring systems. The relevance of collecting biometric information extracted is explained from human gait data. The features of the primary processing of data are briefly described from the accelerometer of a mobile phone in order to extract gait parameters. The results of the research of the influence of various factors are described on the data of a mobile phone accelerometer. The obtained results of the research indicate the possibility of separating gait data into different groups and further monitoring the change in parameters within each group. Building a personal information model (map) of a person's gait will make it possible with a preliminary division into data categories. It is to more effectively apply various processing algorithms and improve the accuracy of assessing individual gait characteristics and the quality of the entire system as a whole. © 2023 IEEE.

关键词： Accelerometers

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Application of an Advanced Dijkstra Algorithm for Automation systems of 3D Piping Routing 8

Application of an Advanced Dijkstra Algorithm for Automation...

引用

8th International conference on Communication and Information systems, ICCIS 2024

作者： Choi, So-Won Lee, Eul-Bum Pohang University of Science and Technology Graduate Institute of Ferrous and Eco Materials Technology Pohang Korea Republic of

ISBN: (纸本)9798331531171

As engineering, procurement, and construction (EPC) plant projects increase in size and complexity, the importance of accurate design in a short time frame is rising. In particular, the automation of piping route design is essential to shorten the project schedule and reduce the cost. This study aims to develop an automated pipeline route design model and proposes an advanced Dijkstra algorithm. First, digital image processing technology was applied to preprocess the data. In addition, 3D model data of plant equipment was set up to realize the developed model in 3D. The Advanced Dijkstra algorithm proposed in this study for piping design automation consists of three algorithms. First, the Dijkstra algorithm was applied to find the shortest route, and the Routing Priority algorithm, which considers the importance of design, was added to optimize the route. In addition, the Interference Obstacle Avoidance algorithm was used to consider errors caused by interference between equipment and pipes. The automatic piping route design model developed in this study was verified using the project data of the 'D' petrochemical plant in Korea. As a result of comparing the Bill of Material (BoM) extracted through the developed model with the BoM of the actual completed project, the matching accuracy was 98.9% in weight and 84.9% in length for the pipe quantity. The automatic pipe route design model is expected to store project data to manage advanced design know-how and contribute to sustainable growth in the field of pipe design. © 2024 IEEE.

关键词： Advanced Dijkstra algorithm digital image processing EPC Projects piping auto-routing model

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Dog Breed Detection using YOLOv7 and WOA-CNN 10

Dog Breed Detection using YOLOv7 and WOA-CNN

引用

10th International conference on Communication and Signal processing, ICCSP 2024

作者： Singh, Aniket Kothari, Rushil Arulalan, V. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

The need to accurately identify dog breeds is important due to their popularity and role as companions. With an increase in the variety of dog breeds worldwide, there is a pressing demand for better classification methods. Advances in artificial intelligence have resulted in the creation of a new model that combines YOLOv7 with the Whale Optimization Algorithm (WOA) in a Convolutional Neural Network (CNN) setup, designed specifically for detecting dog breeds. This model uses the extensive Stanford Dogs dataset, which includes 120 unique breeds, and has been thoroughly tested. It has shown to be more accurate and precise than current methods. The effectiveness of this model in identifying dog breeds marks a significant improvement in the field and suggests its usefulness in various areas such as veterinary services, dog behavior studies, and improved pet management systems. Additionally, this method showcases the benefits of merging advanced machine learning techniques with nature-inspired algorithms to address complex identification challenges, making a significant contribution to the development of AI-based solutions in animal identification and related areas. © 2024 IEEE.

关键词： image recognition

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AI-Based Bone Cancer Detection Using image processing and CNN 5th

AI-Based Bone Cancer Detection Using Image Processing and CN...

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5th EAI International conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： Srividya, K. Reddy, Gangannagari Varunteja Bakki, Vishwaja Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9783031770777

This study presents a new Technology-driven approach for early detection of bone cancer using preliminary image processing technologies and neural networks (CNN) used for diagnosing cancer from pathological images. The main aim is to provide a powerful and reliable tool for cancer diagnosis done in clinics. Survival rates of cancer decrease with an increase in age, urging the need for early diagnosis and precaution. The project aims to use machine learning and image preprocessing to provide solutions for the detection of cancer, necessary for timely intervention. Using carefully selected datasets and advanced image preprocessing techniques, the project aims to provide doctors with reliable tools to accurately diagnose bone cancer. Analysis of existing literature demonstrates the effectiveness of CNN and deep learning algorithms in the analysis of medical images as the basis of the proposed method. The proposed methodology includes image enhancement, data acquisition, CNN development, training, validation, transformation, and model evaluation. The CNN model is evaluated on accuracy, precision, recall, and f1-scores of bone cancer diagnosis images. It provides insight into model development by addressing issues such as classification error, class inequality, and biases. While comparisons with existing literature demonstrate the model's uses, practical considerations are also discussed. The findings suggest a significant potential to improve bone cancer diagnosis and stimulate further research on improving CNN model architecture and developing real-world deployment strategies. The training accuracy of the CNN model came out to be 94.64%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Diseases

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Finding Concrete Cracks in a Building using Improved Deep Learning algorithms 2

Finding Concrete Cracks in a Building using Improved Deep Le...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Devisurya, V. Paulson, J. Seshadhri, M. Varshini, P. Nagaraj, P. Muneeswaran, V. Kongu Engineering College Department of Information Technology Erode India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350325799

To ensure functionality and durability, engineering constructions must have fractures identified and repaired. Since cracks are typically discovered through visual inspection, the examiner's personal judgment plays a vital part in both the detection of concrete cracks and the estimation of their characteristics. In the past, this has helped with both the evaluation of the crack phenomenon and the repair of various structures. The number of engineering structures has expanded as a result of industrial development, but relative to that, the level of skill in the crack-detecting field has not improved. Therefore, a quicker yet more precise method is required. The image processing method detects the cracks and their characteristics simultaneously. The formulation of the algorithm and the implementation is simple. However, other flaws, such as mistaking noises for fractures and a lack of accuracy in spotting micro-cracks, have turned into major obstacles for this technology. Finding transverse cracks in concrete structures is another critical problem. Therefore, a thorough analysis of the most recent articles is required to establish an accurate technique. In order to comprehend the potential of this technology, a critical study of the image-processing phenomenon and a thorough literature review has been done in this paper. A universal DenseNet121 classification-based algorithm with camera pictures for crack identification could be an effective strategy with greater accuracy. © 2023 IEEE.

关键词： image enhancement

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Enhancing Sign Language Recognition Using Deep Convolutional Neural Networks 14

Enhancing Sign Language Recognition Using Deep Convolutional...

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14th International conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kanavos, Athanasios Papadimitriou, Orestis Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. This research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. The developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance. © 2023 IEEE.

关键词： image classification

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Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods 2

Classification and recognition of soybean leaf diseases in M...

引用

2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Dixit, Shriniket Kumar, Anant Haripriya, Akash Bohre, Khitij Srinivasan, Kathiravan SCOPE Vellore India School of Bioscience and Technology Vellore India Oriental College of Science and Technology SCOPE Bhopal India Acropolis Institute of Technology SCOPE Indore India

ISBN: (纸本)9798350310719

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach's feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively. © 2023 IEEE.

关键词： image enhancement

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An Enhanced Approach to Content-based image Retrieval using Harris Hawks Optimization and Automated image Captioning 8

An Enhanced Approach to Content-based Image Retrieval using ...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Padate, Roshni Gupta, Ashutosh Chakrabarti, Prasun Sharma, Arvind Fr. Conceicao Rodrigues College of Engineering Computer Engineering Mumbai India Sir Padampat Singhania University Computer Science and Engineering Udaipur India Sir Padampat Singhania University Department of Mathematics Udaipur India

ISBN: (纸本)9798350376425

This paper introduces an innovative approach to Content-Based image Retrieval (CBIR) that leverages Harris Hawks Optimization (HHO) to improve feature selection and retrieval accuracy. CBIR systems are increasingly important in fields such as medical imaging and remote sensing, where efficient and accurate image retrieval is essential. Inspired by the cooperative hunting strategies of Harris hawks, the HHO algorithm efficiently balances exploration and exploitation, optimizing the feature extraction process to enhance retrieval outcomes. The proposed system is evaluated on various datasets and compared to other optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic algorithms (GA). The findings show that the HHO-optimized system outperforms these methods in terms of precision, recall, and F1-scores, while also reducing retrieval ***, the incorporation of image captioning into the CBIR framework further improves the user experience. By integrating Computer Vision (CV) and Natural Language processing (NLP), the system generates descriptive captions along- side retrieved images, offering valuable textual context. This capability allows users to quickly evaluate the relevance of retrieved images, particularly in complex fields like medical imaging. Experimental results demonstrate that the combination of HHO-based optimization and image captioning significantly enhances both retrieval accuracy and user satisfaction, making it a powerful solution for large-scale image retrieval tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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New Type-2 Fuzzy Logic Algorithm Based image Segmentation for Medical Imaging Applications 9

New Type-2 Fuzzy Logic Algorithm Based Image Segmentation fo...

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9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Sangeetha, S. Latha, B. Anna University Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

Medical image segmentation is a plan that has a lot of potential. The achievement of automated picture segmentation makes it simple to gather biomedical and anatomical information. In terms of the subject, more study and work have increased efficacy. In the field of image processing, this work provides a type-2 fuzzy system-based image application. Computer aided diagnosis and medical image analysis both heavily rely on segmentation. This study presents a fuzzy type 2 algorithm based automated threshold selection method for the division of therapeutic pictures. It has been established that fuzzy set theory may be an effective tool for recitation surroundings when the facts are unclear or inaccurate. The algorithms have been put into practice and evaluated using brain MRI (magnetic resonance imaging) images. The outcomes have been evaluated and documented. The comparison of the controller's efficiency of CNN has 92%. © 2023 IEEE.

关键词： image segmentation

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A matching-score-based template reconstruction attack

A matching-score-based template reconstruction attack

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2024 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Liao, Fengxiang Leng, Lu Guo, Guodong Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo 315000 China

ISBN: (纸本)9781510689275

Biometric systems, even with high accuracies, commonly suffer from various attacks. In practical applications, some systems directly display the matching scores, or attackers can obtain the matching scores. This paper proposes a novel matching-score-based template reconstruction attack, which reveals that matching scores can leak the spatial neighborhood range of the attacked template, resulting in some potential security and privacy risks. The dissimilarity (matching score) between two real-number templates is measured by the Euclidean distance. A small number of match scores can be obtained by matching the attacked template with a few randomly selected or generated fake templates. An equation set can be established based on the formulae of the matching scores. However, it is difficult to directly solve the quadratic equation set. We skillfully simplify the quadratic equation set into a linear equation set, which obviously reduces the complexity, especially for large-size templates. This novel attack can be extended to other algorithms, and used to evaluate the security and privacy of the related algorithms. © 2025 SPIE.

关键词： biometric privacy biometric security matching score Template reconstruction attack

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