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

共 12927 条记录，以下是171-180 订阅

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Learning to Reconstruct Signals With Inexact Sensing Operator via Knowledge Distillation

Learning to Reconstruct Signals With Inexact Sensing Operato...

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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

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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...

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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

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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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A Survey on Intelligent Attack Mitigation and Latency Optimization in High-Speed Edge Networks using Deep Learning and Bio-Inspired algorithms

A Survey on Intelligent Attack Mitigation and Latency Optimi...

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Gandham Kiran Kumar M. Thirumaran Department of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the Internet connected devices and cyber– physical systems (CPS) are exponentially increasing, edge computing has become a crucial enabler to support real time processing in high-speed networks (HSNs). However, integrating edge computing into broadband infrastructure is a challenging problem and that includes mitigating security risks such as DDoS and time-delay attacks and keeping ultra-low latency. Explored for this survey are some recent technologies on how to detect attacks and optimize latency on the edge, focusing on forms of deep learning and bioinspired approaches. It analyzes the lack of effectiveness in traditional mitigation strategies and represents new means, based on the use of ensemble learning, dimensionality reduction and hybrid models. Distributed edge infrastructures are specially emphasized on real time implementation, computational overhead and security. This paper attempts to contribute valuable insight for researchers developing robust, scalable, and intelligent edge-based solutions in high-speed networking environments by consolidating recent methodologies and their outcomes.

关键词： Surveys Deep learning High-speed networks image edge detection Prevention and mitigation Focusing Real-time systems Security Optimization Edge computing

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LLM-Based Task Planning for Mobile Robots Using a Novel SLAM Method for Materials Distribution

LLM-Based Task Planning for Mobile Robots Using a Novel SLAM...

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International conference on Autonomous Robots and Agents, ICARA

作者： Aphilak Lonklang Pedro Alejandro Cordero Jara Haichen Xu Wentao Zhang Natabara Máté Gyöngyössy János Botzheim Department of Artificial Intelligence Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper presents a novel autonomous mobile robot system for material distribution, integrating dynamic map generation, natural language processing (NLP), and efficient path-planning for real-world factory applications. The system begins with a top-view image capture of the environment using a fisheye-lens camera, followed by image correction and color-based object detection to identify stations and generate an occupancy map for navigation, eliminating the need for traditional SLAM techniques. User interaction is facilitated through an NLP module connected to a large language model (LLM) trained on predefined assembly manuals, enabling the robot to interpret requests and determine the required materials for specific assembly tasks. The global path-planning is managed by the Improved RRT* with Reduced Random Map Size (IRRT*-RRMS) algorithm, ensuring optimal and efficient motion in dynamic environments. Experimental validation using a TurtleBot3 Waffle Pi equipped with an OpenManipulator-X demonstrates the system's effectiveness, particularly in handling assembly model H, which includes bolts, nuts, and screws.

关键词： Simultaneous localization and mapping Navigation Heuristic algorithms Robot vision systems Object detection Fasteners Natural language processing Mobile robots Assembly Autonomous robots

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Proceedings of SPIE-IS and T Electronic Imaging - image processing: algorithms and systems vii

Proceedings of SPIE-IS and T Electronic Imaging - Image Proc...

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image processing: algorithms and systems vii

ISBN: (纸本)9780819474957

The proceedings contain 43 papers. The topics discussed include: texture preservation in de-noising UAV surveillance video through multi-frame sampling;fast geodesic distance approximation using mesh decimation and front propagation;image object removal in redundant wavelet transform domain;nonlinear mapping of the luminance in dual-layer high dynamic range displays;color enhancement in a high dynamic range environment;active contours that grow and compete driven by local region descriptors;a fast intensity based non-rigid 2D-3D-registration using statistical shape models with application in radiotherapy;a Kernel representation for exponential splines with global tension;compression of multispectral fluorescence microscopic images based on a modified set partitioning in hierarchal trees;robust measurement of the blocking artefact;and image pixel guided tours: a software platform for non-destructive X-ray imaging.

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