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

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Tatsukichi Shibuya Nakamasa Inoue Rei Kawakami Ikuro Sato Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in fine-tuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO

关键词： Training Speech coding Memory management Signal processing algorithms Signal processing Classification algorithms Servers Speech processing Optimization image classification

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

International image processing, Applications and systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance Computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders image annotation Closed box

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Integration of Digital Technologies and Artificial Intelligence in the Ecomonitoring System of the Black Sea Coast

Integration of Digital Technologies and Artificial Intellige...

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International conference on Applied Innovations in IT, ICAIIT 2024

作者： Surguladze, Gia Topuria, Nino Mirtskhulava, Lela Petriashvili, Lily Georgian Technical University Kostava Str. 77 Tbilisi0160 Georgia Iv. Javakhishvili Tbilisi State University/ San Diego State University Ilia Tchavtchavadze Avenue 1 Tbilisi0179 Georgia

ISBN: (纸本)9783031892950

The existence of the Black Sea water area of Georgia is an important factor in its geographical location, which determines the country’s political, social, and economic potential, especially in terms of foreign relations, marine industry, and tourism development. It has a great influence on the formation of the country’s climate, a scientific study of the diversity of the Black Sea and the coastal region, which from a multidisciplinary point of view will study the condition of hydro resources (rivers, lakes, etc.), existing seaports and oil terminals in this region, probable risks and their impact on the quality of the Black Sea water, especially in the background of climate change, it is very important and relevant for the blue development of the Black Sea. Within the framework of the project, the tasks of creating the concept of the information system supporting eco-monitoring of the mentioned region, automated design, and software implementation were addressed. This was based on the system analysis of the region’s hydro sources and sensitive areas, along with new digital technologies. Object- and process-oriented models of their research were created, with automated initial data collection devices (IoT sensors) and processing methods based on appropriate machine learning algorithms and programs. In addition, a service-oriented web portal creation methodology was developed. The portal served experts of the Black Sea eco-monitoring system and managers working on artificial intelligence in the decision-making process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marine industry

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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An Efficient image-Based Virtual Scene Generation and Stitching Algorithm

An Efficient Image-Based Virtual Scene Generation and Stitch...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Luo Wei Chongqing Three Gorges Vocational College Chongqing Wanzhou China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the development of computer graphics and image processing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm.

关键词： Three-dimensional displays Accuracy Splicing Heuristic algorithms Reliability theory Real-time systems Splines (mathematics) image stitching Standards Optimization

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

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International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making Signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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A Comparative Review on Design and Development of Automation System for Grading of Onion

A Comparative Review on Design and Development of Automation...

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Rahul Suryawanshi Jayesh Jagtap Dnyaneshwari Shegokar Siddhesh Jaju Chandan M. N Avinash M. Badadhe Department of Automation and Robotics JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researchers because of the rising need for quality produce. Thus, the findings highlighted in this work show the progress and current approaches and methodologies designed for the design and implementation of automation systems for grading onions. This work outlines various methods such as image processing, gas sensor technology, Near Infra-Red (NIR) spectroscopy, X-ray imaging, Laser Doppler Vibrometry (LDV), and some artificial intelligence approaches that have been used in improving onion sorting systems. In addition, it reveals the drawbacks of these technologies, such as the realization of high image processing, the availability of small data sets, and the stability of the sortation systems. Based on the findings of this review, some recommendations for future research are suggested, such as the integration of multi-sensor systems and the enhancement of the efficiency of the automated onion grading systems using different types of algorithms.

关键词： Automation Reviews image processing X-ray lasers Laser stability Doppler effect Gas lasers Artificial intelligence X-ray imaging Sorting

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Research on the Application of image Recognition and processing Technology in Integrated Media Information Dissemination

Research on the Application of Image Recognition and Process...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Shiyu Pan Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid development of the era of integrated media, the form of information dissemination is becoming more and more diversified, in which the image as an important carrier of information dissemination, its quality and processing efficiency directly affect the dissemination effect. By virtue of its unique advantages in data analysis, image optimization and content distribution, image recognition and processing technology has become an important support means in the field of integrated media information dissemination. This paper firstly analyzes the current situation of the application of image recognition and processing technology in the information dissemination of integrated media; secondly, it discusses the core methods of image recognition and processing technology, including deep learning algorithms, feature extraction technology and real-time image processing strategies; finally, it elaborates the specific application effects of these technologies in the fields of news reporting, advertising and marketing, and live video broadcasting through practical application examples. The study shows that image recognition and processing technology not only improves the intelligent level of integrated media content production, but also provides new possibilities for the innovation of information dissemination mode. In the future, with the further improvement of algorithm performance and the technological integration of multimedia platforms, this field is expected to gain wider application.

关键词： Industries Technological innovation image recognition Face recognition Production Media Real-time systems User experience Optimization Standards

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Exploration of Motion Increase Rate Based on Infrared Feature Capture and CFD Numerical Simulation

Exploration of Motion Increase Rate Based on Infrared Featur...

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Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Minghan Wang Beijing University of Technology Beijing China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The purpose of this study is to explore the motion incremental speed technology based on infrared feature capture and CFD numerical simulation, through the sensor, image processing and analysis algorithms, etc. On the basis of exploring the target's motion characteristics, combined with the Chancellor's theory of ballistic trajectory, and real-time capture of the target's thermal feature information is to achieve accurate target tracking and motion trajectory adjustment. Firstly, this paper adopts the image processing and analysis algorithm, and uses the normal distribution model to process the infrared data, so as to construct the target motion model. Secondly, CFD numerical simulation is used to analyse mechanisms such as motion boost guidance in order to provide more accurate target tracking and trajectory correction schemes. Finally, the motion region of the updraft is predicted by spherical averaging of the fluctuation equation. The results show that the infrared feature capture technique is able to maintain efficient motion track correction and motion speed-up capability based on real-time data processing in dynamic environments.

关键词： Target tracking Accuracy image processing Gaussian distribution Numerical simulation Mathematical models Real-time systems Data models Numerical models Trajectory

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A Novel Artistic image Compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance.

关键词： image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain Compression algorithms Frequency-domain analysis Collaborative intelligence

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