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

共 12901 条记录，以下是1151-1160 订阅

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion

On Momentum Acceleration for Randomized Coordinate Descent i...

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

作者： Matthew Callahan Trung Vu Raviv Raich School of EECS Oregon State University Corvallis OR USA Department of CSEE University of Maryland Baltimore County MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.

关键词： Closed-form solutions Shape Signal processing algorithms Machine learning Signal processing Computational efficiency Computational complexity Speech processing Recommender systems Convergence

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A Survey of Artificial Intelligence Integrating image processing Techniques for Predicting Solar Energy Production: Challenges and Innovations

A Survey of Artificial Intelligence Integrating Image Proces...

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2024 International conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Gupta, Tushar Tiwari, Gagan Joshi, Kapil Noida International University Department of Computer Science Uttar Pradesh G.B.Nagar India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350379716

Solar energy production prediction is an essential prerequisite for the successful implementation of renewable energy sources and the elaboration of sustainable strategies. The paper provides a detailed overview of artificial intelligence methods used to predict the performance of solar energy facilities. It examines traditional prediction methods, such as meteorological reports, and their disadvantages, such as numerous factors' influence on the report, changes on the current data availability, and atmospheric conditions. This probes the exploitation of satellite pictures, earth-based cameras, and drone monitoring, depicting how these graphic evidentiary sources are handled using sophisticated machine gaining formulas for example Convolutional Neural systems and Creative Opposing Networks. The potential of AI-based prediction models that apply machine learning and neural networks to analyze numerous data and identify complicated dependencies is underlined. As conclusive remarks, we have reviewed various techniques and frameworks so far, organized AI algorithms based on their respective advantages and limitations, and compared their performance by presenting examples. Furthermore, we have indicated future work directions, particularly the incorporation of advanced data sources, enhanced model interpretability, scalability optimization, adaptive learning algorithm development, multidisciplinary cooperation, strengthened validation processes, and widened spread in practice. By combining these findings in a comprehensive manner, we aspire to propel knowledge on AI-driven solar energy prediction and inspire increased reliance on it for a cleaner world. © 2024 IEEE.

关键词： Artificial intelligence Deep learning Machine learning Neural networks Prediction accuracy Renewable energy Solar energy prediction Sustainability

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Optimization of a MIMO Antenna using the API metaheuristic algorithm

Optimization of a MIMO Antenna using the API metaheuristic a...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Bitat Hiba Ines Maamri Fouzia Khelfaoui Fatima Djellab Hanane Messai Yacine Aouissi Feriel SATIT Laboratory University Abbes Laghrour Khenchela Khenchela Algeria University Alarbi TBESSI Tebessa Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Antenna technology has undergone significant advancements with the integration of innovative techniques such as MIMO (Multiple Input Multiple Output) and phased array systems. By utilizing multiple antennas for transmission and reception, MIMO enhances data rates, spectral efficiency, and overall system performance. Phased array antennas, on the other hand, introduce beamforming capabilities, allowing for dynamic control and steering of radio wave beams without physical movement. The goal of this work is to optimize the control of the antenna network (MIMO) from the perspective of directivity and secondary lobe reduction using the API (pachycondyla apicalis algorithm) metaheuristic algorithm. Specifically, the API algorithm will be employed to determine the optimal element spacing for an N=16 element linear array operating at the LTE frequency of 2.5 GHz, and phase shift Ψ=2.3 radians with the objective of achieving the best value of the inter element spacing. while minimizing the secondary lobes.

关键词： Phased arrays Array signal processing Heuristic algorithms System performance Metaheuristics Signal processing algorithms Space exploration

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Quick and Efficient Large Scale Approximate Nearest Neighbor Search on High-Dimensional Data

Quick and Efficient Large Scale Approximate Nearest Neighbor...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Hu Zhibing University Of Electronic Science and Technology Of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Large scale approximate nearest neighbor search on high-dimensional data is always a critical research question, including recommendation systems and image search. Graph-based ANNS algorithms have received more and more attention due to its accuracy and efficiency. Nevertheless, the graph-based ANNS algorithms faces the following challenges: computation latency and memory requirements. In this work, we present a novel graph-based similarity search algorithm called HNSWQ, which optimizes the HNSW algorithm by building quick and comprehensive uppers without using compression. Those uppers can bring high-quality entry point to accelerate query processing by reducing the number of points to be accessed during the search and shorten the whole computation latency. At the same time, we consider the parallelism of GPU and take advantage of GPU to shorten the build and search time of the novel HNSWQ algorithm. Finally, we verify the presented algorithm on several representative datasets. Compared with the state-of-the-art graph-based algorithm, our novel method can acquire great accuracy and search speed.

关键词： Accuracy High dimensional data Query processing Memory management Graphics processing units Nearest neighbor methods Parallel processing Media Approximation algorithms Recommender systems

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Implementation of Digital image Zooming On DSK6713 DSP Platform For Real-Time Applications

Implementation of Digital Image Zooming On DSK6713 DSP Platf...

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Electronic Engineering and Signal processing (EESP), International conference on

作者： Abdellah Skoudarli Lina Kebri Yasser Bouzaz LTIR Laboratory FGE - USTHB Algeria Department of Electronics FGE-USTHB Algeria

ISBN: (数字)9798331520632

ISBN: (纸本)9798331520649

Digital image zooming is very important process in image processing. Basically, zooming process consist of two steps: the creation of new pixels locations and the assignment of intensity of gray level to those new location. In general, zooming process provides an enlarged image to get better view of the original digital image with preserving as much as possible the information content of the original image. In fact, digital image zooming operation requires a high storage size and computational complexity. In this paper, we consider an interpolation method for zooming digital images 16 times because of their inherent abilities to preserve sharp edges and detail. In order to achieve real time application, the interpolation algorithm based on 15 filters has been implemented on DSK6713 Platform. According to the experimental results our implementation on DSK6713 platform is able to perform digital image zooming for 32×32 size in real time by using DSP internal memory. For larger sizes 64×64 and 128×128, we recommend to utilize DSK6414 with high DSP frequency and larger internal memory.

关键词： Interpolation Time-frequency analysis Filters Digital images System performance image processing image edge detection Signal processing algorithms Digital signal processing Real-time systems

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Comprehensive image Classification and Object Detection Using Convolutional Neural Networks

Comprehensive Image Classification and Object Detection Usin...

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Disruptive Technologies (ICDT), International conference on

作者： Archana Kumari Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In the modern era, Deep Learning (DL) methodology is used extensively in multiple areas and Convolutional Neural Network (CNN) is majorly used for addressing the problems in real-time for computer vision activities due to its capability of enabling acquisitions with higher accuracy. The concept at the back of this DL technique was inspired from architecture of biotic visual systems. In the current time, it is applied extensively in multiple areas and industries like automotive, medical and healthcare, space, aviation, agriculture etc. Moving forward, numerous algorithms on CNN were proposed to address the requirement of multiple areas, with Natural Language processing (NLP) and computer vision as the major fields for application. The activities in computer vision with major application of CNN includes image categorization, object identification, recognition of face, estimation of human pose, recognition of action, scene labelling along with document analysis. This paper delves into modern techniques of CNN for classification of images and detection of objects.

关键词： Deep learning Computer vision image recognition Face recognition Medical services Real-time systems Natural language processing Agriculture Convolutional neural networks Biomedical imaging

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Kid Activity Recognition: A Comprehensive study of kid activity recognition with monitoring activity level using YOLOv8s algorithms 3

Kid Activity Recognition: A Comprehensive study of kid activ...

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3rd International conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Bisla, Tiya Shukla, Ritik Dhawan, Mansi Islam, Md. Rahatul Koyasu, Takao Teramoto, Keisuke Kataoka, Yui Horio, Keiichi Dronacharya Group of Institution Dept. of Computer Science & Engineering Greater Noida India Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Fukuoka Japan Kyushu Women's Junior College Fukuoka Japan Aichi University of Education Aichi Japan Fukuoka Kodomo Junior College Fukuoka Japan

ISBN: (纸本)9798350372120

Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm for the classification of active states (sitting, standing, running, laying, and jumping). The research focuses on the detection of a child's activities in the kindergarten playroom. To train the model, we created the KAR (Kids Action Recognition) dataset using our own sources. We labeled over 6,489 images in the dataset using ***. Each image is annotated with activity labels and the location of the student (in a room or on a mat). This assisted in presenting insights on the student's vitality level and physical involvement over a period of approximately an hour. We analyzed the whole activity and compared it with the times. Standing in the room and mat, standing in the room, and jumping in the room and mat are most of the activities of the kid. The model achieved an impressive overall accuracy of 88.7 %. We conducted all the training in a Google collaborative environment with A-100 GPU specifications. The results demonstrate that the model can identify and classify each activity with high precision. © 2024 IEEE.

关键词： Graphics processing unit

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4D-Rossler Hyperchaotic System for image Encryption and Decryption with High Security

4D-Rossler Hyperchaotic System for Image Encryption and Decr...

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International conference on Communications and Signal processing

作者： Soumil Agarwal M ByramDharmika Sree Dhathri V Thanikaiselvan S Subashanthini Rengarajan Amirtharajan School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In today’s digital world, safeguarding data at all times is important, especially with the widespread use of images across various processes. image encryption is vital in concealing sensitive information through cryptographic techniques, rendering data indecipherable to unauthorized parties. However, existing encryption systems often fall short in terms of both efficiency and security. This research explores chaos theory for image encryption methodologies to address these limitations. A comprehensive study of image encryption fundamentals was conducted, including chaos theory, various chaotic maps, DNA encoding, and encryption system evaluation metrics. Implementation of different chaotic maps such as the logistic map, tent map, and combined logistic-tent map, along with 3D chaotic systems like the Lorenz and Rossler systems, was performed for encryption purposes. Additionally, the 4D extension of the Rossler hyperchaotic system was introduced for enhanced encryption capabilities. Encryption methods were evaluated using metrics such as UACI, NPCR, correlation coefficients, image entropy and histogram analysis, with results compared and tabulated. This research advances image encryption techniques by leveraging chaos theory to achieve heightened security and efficiency in safeguarding image data.

关键词： Measurement Correlation coefficient Histograms image coding Chaotic communication DNA Signal processing algorithms

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Brain Tumor Segmentation of MRI images Based on K-Means and White Shark Optimizer 24

Brain Tumor Segmentation of MRI Images Based on K-Means and ...

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24th International Arab conference on Information Technology, ACIT 2023

作者： Braik, Malik Al-Hiary, Heba Al-Betar, Mohammed Azmi Al-Balqa Applied University Department of Computer Science Al-salt Jordan Al-Balqa Applied University Department of Computer Information Systems Al-salt Jordan Department of Information Technology Ajman United Arab Emirates Al-Balqa Applied University Irbid Jordan

ISBN: (纸本)9798350384307

Due to the intricacy of the process involved in segmenting and extracting the tumor regions in magnetic resonance imaging (MRI), it is quite difficult to successfully detect the diseases. Due to its critical significance in subsequent image processing phases, this creates a requirement for a suitable clustering algorithm. In this study, a clustering method is presented that may address a variety problems for MRI brain tumor segmentation. One of the most well-known conventional clustering methods that is simple to use, quick to process, and ensures convergence is k-means (KM). Its weakness, which is defined by its susceptibility to the initial center's random initialization, is present. Because of this, the KM was optimized in this study using the white shark optimizer (WSO) to address this weak side of the KM. The KM would set the starting point for WSO's final position. Evaluation results of WSO-based KM were judged on a publicly available datasets in terms of several relevant evaluation methods and contrasted with outcomes of KM, fuzzy c-means (FCM), and other meta-heuristics when practiced to cluster the same datasets. The WSO-based KM clustering approach outperformed the standard KM and other competing algorithms-based KM. © 2023 IEEE.

关键词： FCM image segmentation K-means White shark optimizer

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