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

共 16349 条记录，以下是231-240 订阅

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

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

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and imageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training Computer vision Sensitivity Neurons Signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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Multi-Model Deep Learning Framework for Plant Disease Detection and Classification using YOLOv5, Xception, and DenseNet

Multi-Model Deep Learning Framework for Plant Disease Detect...

引用

Visual Analytics and Data Visualization (ICVADV), International conference on

作者： Kunal K. Meshram Sarika Khandelwal Jyoti Kumre Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Plant diseases lead to food loss and yield reduction, which is a serious challenge to food security globally. Researchers have suggested deep learning models, which are trained using plant disease data like PlantVillage and PlantDoc, as a means of solving this issue. The traditional solutions usually do not work under practical agricultural environments. This research combines machine learning (ML) models and image processing methods for agricultural use. This study utilizes the algorithms, such as MobileNet, VGG16, YOLOv8, and Faster R-CNN, to upgrade plant disease identification and classification. Our method utilizes deep learning architectures for both object detection and plant disease classification. We offer real-time crop analysis to help farmers make informed decisions, achieve maximum productivity, and enhance sustainable agriculture. Leveraging previous work, we seek to enhance performance through DenseNet and Xception for classification and YOLOv5 for object detection. We also use a Flask-based, easy-to-use front-end for authentication-based client testing.

关键词： YOLO Deep learning Productivity Plant diseases image processing Visual analytics Food security Metadata Real-time systems Testing

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A Comprehensive Review of Hybrid V2X Communication and Edge processing Techniques for Real-Time Emergency Vehicle Detection and Prioritization in Urban Traffic systems

A Comprehensive Review of Hybrid V2X Communication and Edge ...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Jerim Justin Lokkeshkumar R Vijayasri P Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This review paper presents the use of hybrid Vehicle-to-Everything (V2X) communication systems to detect and prioritize emergency vehicles in congested urban areas. To reduce response times, researchers are merging edge processing with Dedicated Short-Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X). The technology provides extremely fast, low-latency connections, which are essential for real-time applications, by utilizing recent developments in 5G and edge computing. To increase detection accuracy in noisy urban settings, researchers are experimenting with multi-sensor fusion systems that combine GPS, acoustic sensors, and AI-powered vision. Adaptive traffic signal preemption algorithms that dynamically modify traffic flows to grant priority access to emergency vehicles are also being investigated in some research. Despite its promise, there are still obstacles to be addressed, such as interoperability problems and network congestion. However, the benefits might be enormous: quicker emergency reactions, more seamless traffic patterns, and generally more effective urban transportation systems. In the upcoming years, we should witness some fascinating breakthroughs as researchers continue to hone these technologies.

关键词： Accuracy Reviews 5G mobile communication image edge detection Urban areas Vehicular ad hoc networks Real-time systems Dedicated short range communication Vehicle-to-everything Edge computing

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Learning to Reconstruct Signals With Inexact Sensing Operator via Knowledge Distillation

Learning to Reconstruct Signals With Inexact Sensing Operato...

引用

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

作者： Roman Jacome Leon Suarez Romario Gualdrón-Hurtado Luis Gonzalez Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In computational optical imaging and wireless communications, signals are acquired through linear coded and noisy projections, which are recovered through computational algorithms. Deep model-based approaches, i.e., neural networks incorporating the sensing operators, are the state-of-the-art for signal recovery. However, these methods require exact knowledge of the sensing operator, which is often unavailable in practice, leading to performance degradation. Consequently, we propose a new recovery paradigm based on knowledge distillation. A teacher model, trained with full or almost exact knowledge of a synthetic sensing operator, guides a student model with an inexact real sensing operator. The teacher is interpreted as a relaxation of the student since it solves a problem with fewer constraints, which can guide the student to achieve higher performance. We demonstrate the improvement of signal reconstruction in computational optical imaging for single-pixel imaging with miscalibrated coded apertures systems and multiple-input multiple-output symbols detection with inexact channel matrix.

关键词： Wireless communication Computational modeling Symbols Optical imaging Signal reconstruction Robustness Sensors Speech processing Knowledge transfer image reconstruction

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Lossless Medical image Compression Using ROI Segmentation with Deep Learning Techniques

Lossless Medical Image Compression Using ROI Segmentation wi...

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International conference on Inventive Computation Technologies (ICICT)

作者： Velidi Sai Baba Molaka Ramakrishna Reddy Kombathula Lakshmi Karthik Gandham Lakshmi Tharun Katreddy Surya Mithra Dept. of Information Technology Sagi Rama Krishnam Raju Engineering College Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

With The increasing use of medical imaging has put a greater requirement on fast and efficient storage and transmission. Compression algorithms need to strike a balance between reducing file sizes without distortion of valuable diagnostic information. In this paper, a hybrid lossless compression method is proposed that uses deep learning-based ROI segmentation. A U-Net model is used specifically to segment the cardiac anatomy on CT scans accurately. Lossless compression is used to preserve large diagnostic areas, and lossy compression is used to compress small areas. The approach strikes an effective balance between the two to yield high compression ratio without compromising medical integrity. Experimental evidence proves its efficacy in minimizing storage needs without compromising diagnostic accuracy. This work recognizes the potential of deep learning for medical image processing and paves the way for upcoming AI-based compression methods.

关键词： Heart Deep learning Learning systems image segmentation image coding Accuracy Computed tomography Transform coding Memory Medical diagnostic imaging

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Vehicle Registration Plate Recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

引用

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

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam Vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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A Hybrid Approach for Detection and Reduction of different types of noises in gray scale images

A Hybrid Approach for Detection and Reduction of different t...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Amanpreet Kaur Sandhu University Institute of Computing Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

image denoising is a critical task in various fields such as digital photography, medical imaging, satellite or remote sensing, and, also preserving important image details while removing noise is essential. This paper introduces the Hybrid Adaptive Median-Gaussian Filter (HAMGF), a novel method designed to effectively mitigate both salt-and-pepper and Gaussian noise especially in grayscale images. The proposed HAMGF technique combines adaptive median filtering, which dynamically adjusts the window size to remove impulsive noise, with adaptive Gaussian filtering, which varies the smoothing strength based on local image variance to reduce Gaussian noise. However, the proposed hybrid technique is implemented on a variety of noisy images, demonstrating its ability to outperform traditional filtering methods that are typically specialized for single noise types. HAMGF not only excels in reducing noise but also in preserving critical image features or details, such as edges and fine textures, making it particularly suited for applications requiring high-fidelity image restoration. The results highlight the flexibility and robustness of HAMGF, showcasing its potential for broader adoption in noise reduction tasks. The proposed research work demonstrates an important advancement in the area of image processing which is offering a versatile as well as powerful tool for the enhancement of image quality across varied domains.

关键词： Smoothing methods Filtering Gaussian noise image edge detection Noise reduction Adaptive filters Filtering algorithms image restoration Remote sensing image denoising

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The EcoSync: A Comprehensive Study on Water Quality Prediction and Waste image Recognition 3

The EcoSync: A Comprehensive Study on Water Quality Predicti...

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3rd International conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Mukherjee, Ahana Chatterjee, Bipasha Das, Indrakshi Chakraborty, Koushiki Pramanik, Madhushree Payra, Anjan Kumar Institute of Engineering and Management Newtown Computer Science (Artificial Intelligence & Machine Learning) Kolkata India Institute of Engineering and Management Newtown Computer Science (Artificial Intelligence & Machine Learning) India Institute of Engineering and Management Kolkata Newtown India

ISBN: (纸本)9798331523893

This research was devised to analyze how machine learning tools could assess water quality and classify waste aiming to build a better system for sustaining our environment. We unified decision trees, random forests, and convolutional neural networks to create a multi-model system that identifies and sorts waste via image recognition. To strengthen our model's performance, we expanded our dataset to include sundry labelled images. Using a multilayer perceptron with Adam optimizer, our system achieved 89% accuracy in classifying waste types and 94% accuracy in identifying waste materials. Water potability analysis, however, relied on machine learning models that were trained on a comprehensive dataset of chemical and physical water parameters. The system was able to show an accuracy of 87% in predicting water safety with a low false positive rate of 3.2% when real time monitoring was integrated. These systems when integrated gave a solution that was better than traditional methods by 47% and the processing time was reduced by 62%. Our results showed that machine learning can effectively enhance environmental management systems. This scalable, technology-driven approach proved successful for both waste and water management, making it particularly valuable for regions struggling with these challenges, regardless of their development status. © 2025 IEEE.

关键词： Adam Optimizer Classifiers Machine Learning algorithms Sustainable development Waste analysis Waste image Recognition Waste Management Water analysis Water and Waste Management Water potability XG Boost

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

引用

Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink

Research on Collaborative Filtering Algorithm of E-commerce ...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Haohao Zheng xiaonan Luo xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In online shopping, a person’s interest in a product is closely related to whether they will purchase it Analyzing people’s interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user-item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the is of of matrix sparsity but its accuracy in recommending products based on implicit feedback alone is insufficient. This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by suimming the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product’s maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm’s accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

关键词： Accuracy Collaborative filtering Prediction algorithms Real-time systems Electronic commerce Sparse matrices User preference Matrix converters Root mean square Intelligent control

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