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

共 12980 条记录，以下是801-810 订阅

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A New Efficient Split & Merge Algorithm for Embedded systems

A New Efficient Split & Merge Algorithm for Embedded Systems

引用

IEEE International conference on image processing

作者： Nathan Maurice Julien Sopena Lionel Lacassagne LIP6 Sorbonne University Paris

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This article presents a new image segmentation algorithm based on a Split & Merge approach. By nature, the execution time of Split & Merge algorithms is data-dependent, as their halting conditions are tied to the homogeneity of each region. While previous algorithms made the Split step less sensitive to input data, the execution time of the more complex Merge step remains highly sensitive to image content. This paper tackles the sensitivity and performance problems from a system and architecture perspective. Memory reallocations due to array fusions are eliminated with the introduction of a TTA (Three Table Array) structure in the Merge step. As iterating over entries in this structure causes a loss of memory locality, we propose two new mechanisms that implement a software cache to mitigate this. An experimental study on an embedded system (Nvidia Jetson Xavier NX) has shown our Merge algorithm to be 10.6 times faster than the state-of-the-art Split & Merge algorithm for $960 \times 720$ images. Moreover, the execution time of our algorithm is also more resistant to image characteristics.

关键词： Resistance image segmentation Embedded systems Sensitivity Software algorithms Streaming media Prediction algorithms

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Vision Transformer based ResNet Model for Pneumonia Prediction 4

Vision Transformer based ResNet Model for Pneumonia Predicti...

引用

4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Sowmya, Thatikonda Sai Narasimhulu, Thumma Sunitha, Gurram Manikanta, Thupakula Venkatesh, Thirupathi Sree Vidyanikethan Engineering College Department of CSE A.P Tirupati India Mohan Babu University School of Computing Department of Ai & Ml A.P Tirupati India

ISBN: (纸本)9798350300093

This research study aims to develop a pneumonia detection system using vision transformers. Pneumonia is a very serious respiratory illness that may result in severe health issues, and early detection is essential for effective treatment. Deep learning-based computer vision algorithms have yielded encouraging results in medical image analysis in recent years, and vision transformers have emerged as a potent tool for processing visual data. The proposed system uses a vision transformer to process chest x-ray images and extract visual traits, which can be utilized for classification. The proposed model 'Vit_base_resnet50_224_in21k' is trained on a vast and diverse dataset of annotated chest x-ray images to understand the patterns and characteristics of pneumonia. The system's performance is estimated using standard evaluation parameters - accuracy, loss, specificity, sensitivity, F1-score and ROC curve. The proposed model has achieved a performance accuracy of 92.96% for pneumonia detection. The results demonstrate the potential of vision transformers in chest x-ray image analysis and contribute to the development of more accurate and efficient tools for pneumonia detection. This system has the potential to assist healthcare professionals in making faster and more accurate diagnosis, which can ultimately improve outcomes and save lives. © 2023 IEEE.

关键词： image analysis

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Face Recognition Implementation Based on image processing Techniques

Face Recognition Implementation Based on Image Processing Te...

引用

Signal processing and Intelligent Computing (SPIC), International conference on

作者： Bingcan Yang Xiai Chen School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

image processing is a technique that involves the manipulation and enhancement of digital images. It encompasses various aspects such as image acquisition, image preprocessing, image enhancement, image segmentation, feature extraction, and object detection. The purpose of image processing is to process and enhance images using computer technology, aiming to achieve higher image quality for subsequent processing and applications. In practical applications, image processing techniques employ different algorithms and methods to achieve various processing and enhancement effects. Additionally, image processing techniques can be applied in fields such as medicine, security, autonomous driving, VR/AR, etc. For instance, in the medical field, image processing techniques can be utilized for image segmentation and diagnosis of medical images, thereby improving the accuracy and analytical capabilities of medical imaging. In the security field, image processing techniques can be employed for tasks such as video surveillance and facial recognition, enhancing the efficiency and accuracy of security monitoring. In the domain of autonomous driving, image processing techniques can be used for detecting and recognizing the surrounding environment of vehicles, thereby enhancing the safety and performance of autonomous driving systems. In conclusion, image processing technology is a very important technology. By continuously improving the accuracy and reliability of the algorithm, it is expected to be applied in a wider range of applications.

关键词： image segmentation image recognition Accuracy image processing Face recognition Signal processing algorithms Signal processing Video surveillance Medical diagnostic imaging Autonomous vehicles

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Direction-of-arrival estimation with new versions of Min-Norm algorithm based on Nyström method 7

Direction-of-arrival estimation with new versions of Min-Nor...

引用

7th International conference on image and Signal processing and their Applications, ISPA 2022

作者： Naceur, Aounallah Ali, Khalfa Kasdi Merbah University Faculty of New Information Technologies and Communication Department of Electronic and Telecommunications Ouargla30000 Algeria Mohamed Boudiaf University Faculty of Technologies Department of Electronic M'Sila28000 Algeria

ISBN: (纸本)9781665480420

Direction-of-arrival estimation is a significant problem in many telecommunication systems which their architecture is composed with antenna arrays. In literature, several algorithms were proposed as a challenge, for different scenarios, to detect rapidly and accurately the direction of impinging signals on the antenna array. In this paper firstly, we use the Nyström method to develop a low computational Min-Norm version for solving the problem of direction-of-arrival estimation by spectral peak searching. Then, we generalize the root version of the proposed algorithm. Finally, we give some simulation results for demonstrating the efficiency and the performances of the new proposed algorithm versions. © 2022 IEEE.

关键词： Array processing

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Computer-Aided Audit Software Based on Fuzzy Clustering Algorithm

Computer-Aided Audit Software Based on Fuzzy Clustering Algo...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Yuan Fen Wuhan Qingchuan University Wuhan City Hubei Province China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

According to the needs of auditing work, efforts are made to develop suitable computer-aided auditing software to gradually change or enhance traditional auditing techniques and methods, which can better play the role of audit supervision. The purpose of this paper is to study the design and optimisation of computer-aided audit software based on fuzzy clustering algorithms. By analysing the technical difficulties of computer-aided auditing, this paper proposes the design concept of the project software. The improved fuzzy C-mean clustering algorithm DFCM and the fast audit steps of the project based on improved fuzzy clustering are specifically introduced, and the data processing methods of the system are described in detail, and the specific application of the project software is also presented. The experimental results show that the computer-aided audit software in this paper can be applied to calculate the audit price of the project to be audited and the software is feasible.

关键词： image processing Software algorithms Clustering algorithms Quality control Data warehouses Data processing Software Protection Expert systems Optimization

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Comparison and Application of Vehicle Target Detection Methods Based on YOLOv5s and YOLOv7 7

Comparison and Application of Vehicle Target Detection Metho...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Gu, Chenyu Du, Hong Zhang, Xiaozheng Li, Lidan Yang, Zhonglin Liu, Gaotian China Northern Vehicle Research Institute Beijing China

ISBN: (纸本)9798350350760

Currently, the methods for pedestrian detection have undergone significant expansion, yet vehicle target detection still retains immense potential for widespread application in the field of intelligent recognition. Nevertheless, vehicle target detection encounters challenges in terms of detection effectiveness, impacted by factors such as lighting conditions, environmental variations, and occlusions. This study centers on vehicle target detection, aiming to tackle the issues of difficult detection, easy missed detection, and algorithm selection within complex backgrounds. By utilizing the YOLOv5s and YOLOv7 algorithms, we have constructed a dataset that encompasses images featuring vehicles against complex backgrounds and in small scales. This dataset is then used to train the models, which are subsequently employed to perform target recognition for vehicles individually. Through comparative experimentation, we have proven the superiority of the YOLOv7 algorithm in detecting vehicles amidst complex backgrounds and those of small scales. ©2024 IEEE.

关键词： image processing vehicle target detection YOLOv5s YOLOv7

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A Fast image Mosaic Algorithm Based on Feature Partition Extraction

A Fast Image Mosaic Algorithm Based on Feature Partition Ext...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Zhiyou Lian Jianhua Ren School of Electronics and Information Engineering Liaoning University of Engineering and Technology Huludao Liaoning China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

This paper proposes a fast image stitching algorithm based on feature partitioning extraction to address the issues of long processing time, high computational complexity, and poor stitching performance in existing image stitching algorithms. This algorithm is implemented through two stages: optimizing image registration and image fusion. In the image registration stage, feature extraction and accumulation are carried out using image partitioning and an improved Gaussian pyramid layer series method; In the image fusion stage, an improved adaptive weighted average fusion algorithm is used for image fusion operations to improve stitching efficiency and make the image clearer and more natural. Experimental verification shows that the algorithm has strong robustness, significantly improving feature extraction speed and matching rate. Compared with mainstream algorithms, the stitching speed has increased by nearly 2 times, and the image related evaluation indicators have increased by about $5 \%$, basically meeting the real-time and timely needs of image stitching in daily life.

关键词： image registration Deformation Feature extraction Robustness Real-time systems Partitioning algorithms Computational complexity image stitching image fusion image reconstruction

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Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf Disease Detection

Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf...

引用

2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Sakshi Sharma, Chetan Sharma, Shamneesh Sharma, Tushar Gochhait, Saikat School of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India UpGrad Campus UpGrad Education Private Limited Karnataka Bangalore India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Sidtm Symbiosis International Deemed University Maharashtra Pune India Nri Samara State Medical University Russia

ISBN: (纸本)9798350372229

Mustard plants are a crucial agricultural commodity for food and oil production. However, they are frequently vulnerable to various diseases that can substantially reduce crop yield. Early identification and diagnosis of these illnesses are essential for efficient management and control, ensuring sustainable production and agricultural output continuity. This research presents a new method for categorizing mustard leaf diseases using Convolutional Neural Networks (CNNs). The study utilizes sophisticated machine learning algorithms to differentiate between healthy and diseased mustard leaves, which is crucial for maintaining agricultural output and guaranteeing food security. The study involves training CNN architectures - Sequential CNN, ResNet-50, VGG, and AlexNet - on a meticulously curated dataset comprising images of healthy and diseased mustard leaves. The classifier's effectiveness is validated through comprehensive testing, demonstrating significant precision, recall, and F1-score advancements over conventional methods. This approach provides an efficient tool for disease detection in mustard crops and contributes to sustainable agricultural practices, aligning with the global goal of food security and environmental sustainability. © 2024 IEEE.

关键词： Agricultural productivity Crops Data Augmentation image processing Precision Agriculture Sustainable Agriculture

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Smart Traffic Light Control System Using image processing

Smart Traffic Light Control System Using Image Processing

引用

2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Banu, Asha S.M Lakchida, Soundarya A S Shanthini, V.S. Stinsha, S.L. Sri Krishna College of Engineering and Technology Dept of ECE India

ISBN: (纸本)9781665489621

Crossing the road is one of the major problems, due to the increase of fast-moving vehicles on the road. The challenges in the existing techniques utilized for traffic control can be overcome by using the Smart Traffic Light Control System using image processing, proposed in this paper and allowing pedestrians to walk on busy roads conveniently. It is found that the Canny Edge Detection Technique is a very effective method among the various edge detection algorithms. The suggested method reduces the waiting time in zebra crossing by immediately controlling the traffic light and gives more time for elderly and handicapped pedestrians to cross the roads safely even during heavy traffic. When compared to all the traditional techniques, image processing is an efficient method for traffic control. This technique removes the usage of unwanted hardware like sensors which are used to sense noises. This prevents the wastage of time for vehicles which is more essential for emergency vehicles. The output of the code is obtained by image matching. The results are shown in three scenarios: less traffic, moderate traffic, and more traffic. © 2022 IEEE.

关键词： Roads and streets

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A Dual-Approach to Drone image Enhancement: Software-Based processing and FPGA Implementation

A Dual-Approach to Drone Image Enhancement: Software-Based P...

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Smart Electronics and Communication systems (ISENSE), International conference on

作者： Arun T Nair Nanditha N Varma Nandakumar R National Institute of Electronics and Information Technology Calicut

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is the main goal since it is essential for applications like environmental monitoring, navigation, and surveillance. In order to estimate the atmospheric light and transmission map, the suggested methodology makes use of the dark channel. A farrow filter and a guided filter are then applied to improve the transmission. Metrics including the PSNR, SSIM, Execution time, and MSE that are derived from Python-based image processing implementations are used to assess the effectiveness of the dehazing algorithms. The outcomes show notable gains in processing efficiency and image clarity. In order to accelerate the performance, this method was implemented on an FPGA based SoC. Power consumption and throughput of the FPGA - based solution were evaluated, demonstrating its effectiveness and appropriateness for real-time applications.

关键词： Surveillance Video sequences Random access memory Gray-scale Filtering algorithms Throughput Real-time systems System-on-chip Field programmable gate arrays Tuning

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