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

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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...

引用

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

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

引用

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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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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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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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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NLP Based Automated Text Summarization and Translation: A Comprehensive Analysis

NLP Based Automated Text Summarization and Translation: A Co...

引用

Sustainable Computing and Smart systems (ICSCSS), International conference on

作者： Nikhil Zade Gitanjali Mate Kamal Kishor Nishant Rane Manmath Jete Department of Information Technology JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Extractive text summarization stands as a fundamental pursuit within natural language processing, offering the capability to distil extensive textual content while preserving essential information. This research study presents a Graphical User Interface (GUI) application developed in Python using the TKinter library, designed to streamline the process of document summarization. Leveraging advanced image processing techniques, including face recognition through libraries such as OpenCV and PIL, the proposed system integrates robust security measures for user registration and login functionalities. By utilizing NLP, speed and accuracy, our system offers scalable and adaptable solution for text summarization and language translation with accuracy between 91% to 95%. By employing efficient algorithms, users can extract pivotal sentences from documents, facilitating expedited comprehension and analysis. The fusion of text summarization with secure authentication mechanisms addresses both productivity and security concerns within document management systems, culminating in a professional-grade solution tailored to contemporary information processing needs.

关键词： Productivity Accuracy image processing Text summarization Information processing Streaming media Natural language processing

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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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Automatic Number Plate Recognition System Using Deep Learning algorithms and image processing for Surveillance

Automatic Number Plate Recognition System Using Deep Learnin...

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International conference on Science Technology Engineering and Management (ICONSTEM)

作者： A. Christy Jeba Malar Deepika S Jayasree S Keerthana G Department of Information Technology Sri Krishna College of Technology Coimbatore India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The increase in amount of vehicles in the past few years have made traffic management a difficult job. Technologies play an important role in these systems to regulate the traffic. Number plates are distinguished by their shape, bulk, and color in different countries. Number plates must adhere to rules in India, where a light hue serves as the background and a black color serves as the foreground. This paper recommended a new technique to locate the number plate on a vehicle by using histogram equalization and then identifying the alphabets and digits on the plates using character segmentation using Sobel and Canny edge detection image processing technique.

关键词： Deep learning image segmentation Histograms image color analysis Shape Surveillance image edge detection

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing

A Survey on Automated Disease Diagnosis and Classification o...

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International conference on Information systems and Computer Networks (ISCON)

作者： Ankit Khandelwal Aasheesh Shukla Mangal Sain Electronics & Communication Dept. GLA University Mathura India Information Engineering Dept. Dongseo University Busan Republic of Korea

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.

关键词： Deep learning Plant diseases Machine learning algorithms Shape image color analysis Digital images Neurons

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