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检索条件"任意字段=International Symposium on Image and Signal Processing and Analysis"

共 54084 条记录，以下是121-130 订阅

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Smart Tunnel Traffic Control System using IoT and Machine Learning

Smart Tunnel Traffic Control System using IoT and Machine Le...

引用

2025 international Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Subramani, V. Vishnu Kumar, S. Rishikanth, S.R. Poonkuzhali, Ramadoss K S Rangasamy College of Technology Department of Information Technology Tiruchengode India

ISBN: (纸本)9798331521394

Smart Tunnel Traffic Control System Using IoT and Machine Learning' is designed to offer safety and effective management of traffic in single-lane tunnels. The conventional tunnel system does not prohibit big vehicles from entering the tunnel through either end, which may lead to congestion and increase the likelihood of accidents. The designed system uses an ESP32-CAM module to sense the images of the vehicles in real time at any entry point of the tunnel. Using a CNN model, the system classifies incoming vehicles by size, and if a large vehicle is detected, it triggers a red 'STOP' signal and message at the opposite end. In this way, only one large vehicle occupies the tunnel at any time. The system further calculates the transit time in terms of the length of the tunnel and updates it dynamically to handle incoming vehicles. As soon as the tunnel becomes free, a green 'GO' signal allows the next vehicle to move forward. Keywords: loT, ESP32-CAM, CNN, traffic, tunnel safety, automation, image processing, classification, monitoring, accident prevention, congestion, ML. © 2025 IEEE.

关键词： image analysis

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Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights 5th

Data-Driven Strategies for Twitter Engagement: Hashtag Recom...

引用

5th international symposium on signal and image processing, ISSIP 2024

作者： Ramasamy, Gobi Natarajan, Arul Kumar Rajan, Arokia Paul Bangalore India Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (纸本)9789819795147

Twitter is a great way to connect with people worldwide, and one of the best ways to do that is by using hashtags. A hashtag is a keyword or phrase attached to a particular topic, and users can use it to find related tweets. Using a hashtag relevant to the needs or for business can increase the tweets’ visibility and make it easier for people to see the content they want. It can hugely help content creators who want to increase engagement and influence their tweets. This research introduces TagMate, a hashtag recommender system for Twitter that offers significant benefits. By accessing the tweets using Twitter API and after analyzing and performing algorithms, recommendations for hashtags can be obtained. The Twitter API allows access to various information about the account, including followers, tweets, content, etc. This information can be used to generate recommendations for hashtags related to the business. The system will generate hashtags according to the tweet and recommend trending or popular hashtags to increase their reach or engagement on the Twitter platform. Using the API, a dashboard can be created showing which hashtags are being used most frequently and which are most popular. This information can help create more relevant and engaging tweets, attracting more followers and interest. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Effects of visual rotation axis and clockwise/counterclockwise directions on circular vection in virtual reality 24

Effects of visual rotation axis and clockwise/counterclockwi...

引用

2024 international Conference on Virtual Reality, image and signal processing, ICVISP 2024

作者： Xu, Xiaotian Song, Zhe Zhang, Kexin Xu, Yan Zhang, Yuankun Fan, Xiaoya Pu, Fang Key Laboratory of Human Motion Analysis and Rehabilitation Technology of the Ministry of Civil Affairs Beihang University Beijing China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Liaoning Dalian China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China

ISBN: (纸本)9798400710926

Vection, an illusion of self-motion induced by visual stimulus frequently experienced in virtual reality, significantly impacts user experiences in gaming, driving simulation, and rehabilitation training. User often experience circular vection, an illusion of self-rotation. However, the effects of visual rotation axes and rotation directions on circular vection remain elusive. We employ a 3 (rotation axis: sagittal, coronal, vertical) × 2 (rotation direction: clockwise, counterclockwise) repeated-measures design. Twenty participants were recruited. They were exposed to a virtual visual rotating drum at 60 °/s to induce circular vection. The real-time estimation of perceived self-motion rotation velocity (PSRV) was obtained. Results indicated that during the 30-second constant rotation, the group-level PSRV showed an initial rapid increase followed by a slower rise. A significant interaction effect between rotation axis and direction was revealed. The rotation direction showed no significant effect on PSRV under sagittal and vertical conditions. However, under coronal condition, the PSRV was significantly higher when rotating counterclockwise, compared to rotation clockwise. In addition, when rotating counterclockwise, the highest PSRV appeared under sagittal condition, with the lowest under the coronal condition and the middle under the vertical condition. These findings provide valuable insights for designing VR scenarios with better user experience by vection manipulation. © 2024 Copyright held by the owner/author(s).

关键词： Virtual reality

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Multi-class Histology image analysis Using Handcrafted Texture and Colour Features for Breast Abnormality Classification 11th

Multi-class Histology Image Analysis Using Handcrafted Textu...

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11th international symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Mohanta, Anindita Roy, Sourav Dey Nath, Niharika Bhowmik, Mrinal Kanti Suryamaninagar Tripura Agartala799022 India Biological and Chemical Sciences Department New York Institute of Technology New YorkNY10023 United States

ISBN: (纸本)9789819797615

Breast cancer is one of the most common cancers among women worldwide, affecting around 10% of all women. Its fatality rate is extraordinarily high when compared to other cancer types. Histopathological analysis is one of the reliable methods for identifying breast cancer with confidence. But it's a highly specialized, time-consuming procedure that depends on the pathologist's experience. An automatic computerized histopathological image classification model can assist doctors and pathologists for early diagnosis. But most of the work is based on the non-interpretable black box model for feature selection tasks which is not very encouraging in the medical field. To introduce features interpretability in histopathological imaging, handcrafted features analysis is important first. In this paper, a novel hybrid computer-aided multi-class classification framework is proposed for predicting breast abnormality using histopathological images and handcrafted features. The proposed framework uses traditional handcrafted features (i.e. texture features, colour features, and fusion of texture and colour features) and a deep neural network (DNN) for breast cancer abnormality classification in order to facilitate the screening process for early diagnosis. The DNN classifier is trained using the features that were extracted using the handcrafted methods for two-class and multi-class classifications. The experimental results show that the use of handcrafted texture features had a better prediction performance in histopathological image classification as compared to the other handcrafted features for breast abnormality prediction. Using texture features with a DNN classifier, we achieved average values for accuracy, recall, precision and F1-score of 73%, 79%, 81% and 82% respectively for multi-class classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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A Comparative analysis of Denoising Techniques in image processing 3rd

A Comparative Analysis of Denoising Techniques in Image Proc...

引用

3rd international Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Jaiswal, Nitesh Kumar Srivastava, Subodh Mahto, Manipuran Department of Electronics and Communication Engineering NIT Patna Bihar Patna India

ISBN: (纸本)9789819743582

This paper presents the results obtained from an experimental study on the effectiveness of a noise reduction technique applied to medical images. This study compares the effectiveness of various noise-removal filters in order to determine which is the most efficient. Mode, median, Gaussian, adaptive median, non-local mean, wavelet, and deep learning-based denoising convolutional neural network (CNN) filters were used. This paper seeks to demonstrate experimental results on two types of noisy input images used: impulse noise and Gaussian noise, and compare them to the denoising effect of various methods. Among all the methods evaluated, the deep learning-based Convolutional Neural Network (CNN) approach demonstrates superior performance compared to the other techniques. While maintaining the edges and fine structures in the images, this method was able to eliminate both additive and multiplicative noise. The evaluation of these methods performance primarily relied on several metrics, including visual assessment, Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Mean (NAM), and Cross-Correlation (CC). These metrics provided a comprehensive analysis of the effectiveness and quality of the methods in reducing noise and preserving image details. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

引用

27th international Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： signal to noise ratio

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Performance analysis of Wallace Multiplier Using Novel Approximate 4: 2 Compressors

Performance Analysis of Wallace Multiplier Using Novel Appro...

引用

2025 international Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Dantuluri, Bhavani Kandula, Bala Sindhuri Tirumala, Siva Sri Thumuluri, Sai Teja Tatikonda, Srinu Battu, Varshitha Department of E.C.E. Bhimavaram India

ISBN: (纸本)9798331521394

Approximate computing is revolutionizing VLSI design by striking a balance between accuracy, power efficiency, and performance. This paper delves into the Approximate 4:2 Compressor, a vital component in arithmetic circuits like multipliers and adders, particularly in the context of Wallace multipliers(WMs), where it enhances speed and efficiency by reducing the complexity of partial product reduction. Utilizing tools such as Vivado 2023.2 ISIM and Cadence, we evaluate the compressor's performance across 180nm, 90nm, and 45nm technology nodes, revealing significant decrease in area, power consumption, and delay. Notably, the results show that approximate compressors achieve substantial hardware savings while maintaining acceptable accuracy. In error-tolerant applications such as image processing, signal processing, as well as machine learning, the benefits of these methods are especially notable, as minor inaccuracies can be tolerated. The capacity of these designs to adapt to various technology nodes showcases their flexibility in the dynamic VLSI landscape. Thus approximate compressors provide a revolutionary option compared to traditional designs, meeting the challenges faced in contemporary VLSI. This work emphasizes their potential to drive innovative, energy-efficient solutions in high-performance computing, showcasing the transformative impact of approximate computing on future technology. © 2025 IEEE.

关键词： image enhancement

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Spatial-Frequency Cross-Domain Mutual Guidance for Visible-Infrared image Fusion

Spatial-Frequency Cross-Domain Mutual Guidance for Visible-I...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhili Lin Biao Leng School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary objective of Visible-Infrared image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transformations, which results in fused images lacking sufficient detail and failing to effectively preserve crucial details from the source images. In this paper, we propose Spatial-Frequency Mutual Guidance for VIF. The framework comprises two branches. Each branch reconstructs input features in the frequency and spatial domains. We introduce a novel cross-domain mutual guidance mechanism. It fully integrates information between the frequency and spatial domains to enhance the detail quality of fused images. Furthermore, a weight allocation network is utilized to adaptively assign importance to visible and infrared images based on scene characteristics. Experiments on three VIF datasets show that our method outperforms recent advanced algorithms.

关键词： image color analysis Frequency-domain analysis Heuristic algorithms signal processing algorithms signal processing Resource management Speech processing image fusion image reconstruction Visual perception

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SentiFormer: Metadata Enhanced Transformer for image Sentiment analysis

SentiFormer: Metadata Enhanced Transformer for Image Sentime...

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international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bin Feng Shulan Ruan Mingzheng Yang Dongxuan Han Huijie Liu Kai Zhang Qi Liu University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Shenzhen International Graduate School Tsinghua University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As more and more internet users post images online to express their daily emotions, image sentiment analysis has attracted increasing attention. Recently, researchers generally tend to design different neural networks to extract visual features from images for sentiment analysis. Despite the significant progress, metadata, the data (e.g., text descriptions and keyword tags) for describing the image, has not been sufficiently explored in this task. In this paper, we propose a novel Metadata Enhanced Transformer for sentiment analysis (SentiFormer) to fuse multiple metadata and the corresponding image into a unified framework. Specifically, we first obtain multiple metadata of the image and unify the representations of diverse data. To adaptively learn the appropriate weights for each metadata, we then design an adaptive relevance learning module to highlight more effective information while suppressing weaker ones. Moreover, we further develop a cross-modal fusion module to fuse the adaptively learned representations and make the final prediction. Extensive experiments on three publicly available datasets demonstrate the superiority and rationality of our proposed method.

关键词： Sentiment analysis Visualization Analytical models Fuses Neural networks Metadata signal processing Transformers Feature extraction Speech processing

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Frequency Domain Information Integrated Network for Low-Light image Enhancement

Frequency Domain Information Integrated Network for Low-Ligh...

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international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Na Li Xi Luo Dunlu Peng Zied Bouraoui USST Shanghai China CRIL CNRS & Univ. Artois Lens France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light images often suffer from significant noise and detail loss, making it challenging to effectively distinguish signals from noise when processed directly in the spatial domain. To this end, we introduce frequency domain information to better distinguish high-frequency details from low-frequency components, thereby suppressing noise while enhancing details. The proposed method converts sRGB images into raw-RGB images by reversing the image signal Processor (ISP) pipeline to avoid unnecessary effects. A frequency information interaction processing unit is then designed to enhance low-frequency textures using high-frequency information and correct high-frequency data through low-frequency information to reduce structural deformation during restoration. The signal-to-noise ratio prior is introduced in the hidden layer to further remove noise. Experimental results show that the proposed method outperforms existing color and structure enhancement methods.

关键词： image color analysis Deformation Frequency-domain analysis Pipelines Lighting Speech enhancement image restoration Colored noise image enhancement signal to noise ratio

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