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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Comparative Evaluation of Deep Learning Models for image Captioning of Satellite images 4th

Comparative Evaluation of Deep Learning Models for Image Cap...

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4th International conference on Innovations in Computational Intelligence and Computer vision, ICICV 2024

作者： Shree, Aishwary Sharma, Rohit Singla, Jai Gopal Dhaka, vijaypal Singh Verma, Sourabh Singh Malik, Arnav Manipal University Jaipur Jaipur303007 India Space Applications Centre ISRO Ahmedabad380015 India Jayshree Periwal Global School Jaipur302026 India

ISBN: (纸本)9789819769940

In our investigation, we present a thorough comparative examination of contemporary models aimed at generating descriptive narratives based on satellite image analysis, leveraging a fusion of methodologies such as computer vision (CV), natural language processing (NLP), and machine learning (ML). Employing an array of cutting-edge neural network architectures including VGG-19, ResNet-50, DenseNet-201, EfficientNet B7, UNet, and Inception V3, we meticulously analyze intricate features within satellite images to extract essential patterns and information. These features are subsequently integrated into a Long Short-Term Memory (LSTM) network, a robust variant of Recurrent Neural Network (RNN), thereby enhancing our capacity to efficiently interpret and analyze satellite image data. Through exhaustive experimentation on established benchmark datasets such as the Sydney Dataset and RSIC Dataset, we conduct a comprehensive comparative analysis to assess the efficacy of diverse models. The notable advancements achieved by our model in the realm of satellite image captioning hold significant promise for practical applications in domains such as image interpretation. The observed milestones represent a substantive progression within the field, showcasing adaptable utility across diverse sectors. Particularly noteworthy is the superior performance attained through the pairing of EfficientNet B7 (ENet) with LSTM, yielding accuracy rates exceeding 90% across both the Sydney Dataset and RSIC Dataset. This underscores the effectiveness of synergizing ENet with LSTM architectures for satellite image captioning tasks, thereby emphasizing its potential for real-world deployment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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visual Question Answering Optimized Framework using Mixed Precision Training

Visual Question Answering Optimized Framework using Mixed Pr...

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2023 International conference on Artificial Intelligence and applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Chowdhury, Souvik Soni, Badal National Institute of Technology Silchar Department of Computer Science and Engineering Silchar India

ISBN: (纸本)9781665456272

Thanks to the emergence and continued devel-opment of machine learning, particularly deep learning, the research on visual question and answer, also known as VQA, has advanced dramatically, with great theoretical research significance and practical application value. This field of study makes use of multimodal learning, computer vision, and natural language processing techniques. Except for a few academics who presented different types of optimized bi-linear fusion approaches that integrate text and image characteristics in an efficient way, there haven't been many efforts to optimize the VQA framework. In order to optimize the VQA problem, we offer a unique visual Question Answering framework in this research. Because both 16-bit and 32-bit floating points provide automatic mixed precision, deep learning architectures can now be optimized with less computation and execution time. Using the VQA 2.0 and CLEVR datasets, the proposed framework has been tested against two models. In terms of overall accuracy and execution time, the experimental findings demonstrated a significant improvement. © 2023 IEEE.

关键词： Computer vision

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Regional Transformer for image Super-Resolution 7

Regional Transformer for Image Super-Resolution

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7th International conference on machine vision and Information Technology, CMviT 2023

作者： Yang, Sen Yang, Jiahong Xu, Dahong Li, Xi Hunan Normal University China Hunan Normal University Key Laboratory of Sports Intelligence Research China

ISBN: (纸本)9781665464857

In the image super-resolution algorithm model, a large receptive field can provide more valuable features, so the Transformer with strong information interaction ability has achieved excellent results in image super-resolution processing applications. However, when the range of the receptive field reaches a certain critical value, the restoration performance of the super-resolution algorithm also reaches a certain critical value, which indicates that unconditionally increasing the receptive field will not continue to promote the improvement of the restoration performance. At the same time, the larger the receptive field range, the more data the model needs to process, which also seriously increases the computational complexity of the algorithm. In order to exchange information in a wider range more effectively, in this paper, a new type of super-resolution network based on Transformer, namely Regional Transformer, is designed. The key element in the newly designed network structure is the Region Block (RB) with the Boundary Restriction (BR) mechanism. In addition, the paper designs a Boundary Restriction based on coarse-To-fine pipes. This paper conducts a large number of experiments on multiple datasets, and the experiments show that the network structure designed in this paper has a significant improvement in performance. © 2023 IEEE.

关键词： Restoration

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In-Scene Material Detection for Real-Time Autonomous Hyperspectral Reflectance Correction 11

In-Scene Material Detection for Real-Time Autonomous Hypersp...

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conference on image Sensing Technologies - Materials, Devices, Systems, and applications XI

作者： Pietersen, Randall A. Robinson, Brian M. Diltz, Robert A. Einstein, Herbert H. MIT Dept Civil & Environm Engn Room 1-342 77 Massachusetts Ave Cambridge MA 02139 USA USAF Civil Engn Ctr 104 Res Rd Bldg T9719 Tyndall AFB FL 32403 USA Torch Technol 4090 South Mem Pkwy Huntsville AL 35802 USA

ISBN: (纸本)9781510673793;9781510673786

If a U.S. Air Force operated airfield is attacked, the current methodology for assessing its condition is a slow manual inspection process, exposing personnel to dangerous conditions. Advances in drone technology, remote sensing, deep learning, and computer vision have sparked interest in developing autonomous remote solutions. While digital image processing techniques have matured in recent decades, a lack of application-specific training data presents significant obstacles for developing reliable solutions to detect specific objects amongst rubble, debris, variations in pavement types, changing surface features, and other variable runway conditions. Consequently, near-surface hyperspectral imaging has been proposed as an alternative to RGB digital images, due to its discriminatory power in classifying materials. Spatio-spectral data acquired by hyperspectral imagers help address common challenges presented by data scarcity and scene complexity;however, raw data acquired by hyperspectral sensors must first undergo a reflectance correction process before it can be of use. This paper presents an expedient method, tailored to airfield damage assessment, for performing autonomous reflectance correction on near-surface hyperspectral data using in-scene pavement materials with a known spectral reflectance. Unlike most reflectance correction methods, this process eliminates the need for human intervention with the sensor (or its data) pre or post flight and does not require pre-staged reference targets or an additional downwelling irradiance sensor. Positive initial results from real-world flights over pavements are presented and compared to traditional methods of reflectance correction. Three separate flight tests report mean errors between 2% and 2.5% using the new method.

关键词： Hyperspectral Imaging Pavement Reflectance Correction machine Learning Computer vision

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45th Annual conference of the South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT) on Human- machine-Digital-Convergence

作者： Mertens, P. J. Ngxande, Mkhuseli Stellenbosch Univ Stellenbosch South Africa

ISBN: (纸本)9783031648809;9783031648816

Face recognition plays a crucial role in various applications, ranging from security to personal convenience. Recent advancements have emphasized the importance of recognizing individuals based on age-related facial features within this domain. This paper presents a comprehensive evaluation of two deep learning architectures for age-based face recognition: Siamese Convolutional Networks (SCNs) and vision Transformers (viTs). Convolutional Neural Networks (CNNs), which are critical in modern face recognition, serve as the backbone for Siamese Convolutional Networks (SCNs). SCNs are specifically designed to detect similarities between input pairs by emphasising local features crucial for age-related distinctions. In contrast, viTs, initially developed for natural language processing, have demonstrated promising performance in image recognition, showcasing their aptitude for capturing global image context. This work investigates the performance of these distinct architectures in discerning age-related variations within facial data features. Performance comparisons were conducted on three established SCN models and two viT architectures. The results revealed that the optimal SCNs primarily focused on the mouth, nose, and eye regions, indicating their reliance on local features for age estimation. Interestingly, the viT models achieved superior performance despite lacking explicit feature localization. This suggests that a holistic understanding of the facial context may be more effective than focusing solely on isolated features for age-based recognition.

关键词： Age-Based Face Recognition Siamese Networks vision Transformers

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Facial Micro-Expression Recognition using Deep Spatio-Temporal Neural Networks 32

Facial Micro-Expression Recognition using Deep Spatio-Tempor...

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conference on Signal processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Zheng, Yufeng Blasch, Erik Univ Mississippi Med Ctr Jackson MS 39216 USA MOVEJ Analyt Fairborn OH USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

In the billions of faces that are shaped by thousands of different cultures and ethnicities, one thing remains universal: the way emotions are expressed. To take the next step in human-machine interactions, a machine must be able to clarify facial emotions. Allowing machines to recognize micro-expressions gives them a deeper dive into a person's true feelings at an instant which allows designers to create more empathetic machines that will take human emotion into account while making optimal decisions;e.g., these machines will be potentially able to detect dangerous situations, alert caregivers to challenges, and provide appropriate responses. Micro-expressions are involuntary and transient facial expressions capable of revealing genuine emotions. We propose to design and train a set of neural network (NN) models capable of micro-expression recognition in real-time applications. Different NN models are explored and compared in this study to design a hybrid deep learning model by combining a convolutional neural network (CNN), a recurrent neural network (RNN, e.g., long short-term memory [LSTM]), and a vision transformer. The CNN can extract spatial features (of a neighborhood within an image) whereas the LSTM can summarize temporal features. In addition, a transformer with an attention mechanism can capture sparse spatial relations residing an image or between frames in a video clip. The inputs of the model are short facial videos, while the outputs are the micro-expressions gleaned from the videos. The deep learning models are trained and tested with publicly available facial micro-expression datasets to recognize different micro-expressions (e.g., happiness, fear, anger, surprise, disgust, sadness). The results of our proposed models are compared with that of literature-reported methods tested on the same datasets. The proposed hybrid models perform the best.

关键词： Facial micro-expression Human-machine interaction Long short-term memory (LSTM) Convolutional neural network (CNN) vision transformer Deep learning

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Real-time Bangla License Plate Recognition System for Low Resource video-based applications

Real-time Bangla License Plate Recognition System for Low Re...

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22nd IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Ashrafee, Alif Khan, Akib Mohammed Irbaz, Mohammad Sabik Al Nasim, Md Abdullah Islamic Univ Technol Dept Comp Sci & Engn Gazipur Bangladesh Pioneer Alpha Ltd Machine Learning Team Dhaka Bangladesh

ISBN: (纸本)9781665458245

Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a two-stage detection pipeline paired with vision API that provides real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP(0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).

关键词： Computer vision image recognition conferences Pipelines Focusing Licenses Streaming media

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Evaluating the Impact of Lossy Compression on ADAS Deep Learning Models using Fisheye Cameras 26

Evaluating the Impact of Lossy Compression on ADAS Deep Lear...

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26th Irish machine vision and image processing conference, IMviP 2024

作者： Simha, Srinidhi Mukanahallipatna Molloy, Dara Fahy, Darren Valeo Vision Systems Ireland University of Galway Ireland

ISBN: (纸本)9781837242672

The increasing deployment of Advanced Driver Assistance Systems (ADAS) alongside the continual rise in camera sensor resolution has led to high bandwidth, and generally high cost, computation, and intra-vehicle communication. While the sensor bandwidth impacts the vehicle architecture, it also affects the data collection, storage, deep learning model training, and validation infrastructures. However, if the bandwidth was low, while still achieving the goal of high accuracy ADAS perception, the time and cost associated with creating and deploying the system would be greatly reduced. This study investigates the influence of lossy compression on multi-task deep learning models for real-time perception in ADAS employing fisheye cameras. We leverage a large-scale dataset and train a representative multi-task ADAS perception model for pedestrian, kerb, line, and soiling classification. The testing dataset is subjected to compression using the popular H.264 video codec at varying compression ratios. Through rigorous evaluation, we analyse the effects of compression on model performance, providing insights into the feasibility of employing lossy compression techniques in ADAS applications. Our results reveal that lossy compression could be deployed in automotive perception applications and that a compression ratio of up to 98% (720Mb/s to 12Mb/s), could be utilised with negligible performance degradation. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Advanced driver assistance systems

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Perceptual Learning Technology Based on Computer vision and machine Learning Algorithms 14

Perceptual Learning Technology Based on Computer Vision and ...

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14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Wang, Tianjian School of Computer Science and Technology Wuhan Qingchuan University Hubei Wuhan430204 China

ISBN: (纸本)9798331531935

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security. © 2025 IEEE.

关键词： image segmentation

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Fashion images Classification using machine Learning, Deep Learning and Transfer Learning Models 7

Fashion Images Classification using Machine Learning, Deep L...

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7th International conference on image and Signal processing and their applications, ISPA 2022

作者： Samia, Bougareche Soraya, Zehani Malika, Mimi Biskra University Dept. of Electrical Engineering Biskra Algeria Mostaganem University Dept. of Electrical Engineering Mostaganem Algeria

ISBN: (纸本)9781665480420

Fashion is the way we present ourselves which mainly focuses on vision, has attracted great interest from computer vision researchers. It is generally used to search fashion products in online shopping malls to know the descriptive information of the product. The main objectives of our paper is to use deep learning (DL) and machine learning (ML) methods to correctly identify and categorize clothing images. In this work, we used ML algorithms (support vector machines (SVM), K-Nearest Neirghbors (KNN), Decision tree (DT), Random Forest (RF)), DL algorithms (Convolutionnal Neurals Network (CNN), AlexNet, GoogleNet, LeNet, LeNet5) and the transfer learning using a pretrained models (VGG16, MobileNet and RestNet50). We trained and tested our models online using google colaboratory with Tensorflow/Keras and Scikit-Learn libraries that support deep learning and machine learning in Python. The main metric used in our study to evaluate the performance of ML and DL algorithms is the accuracy and matrix confusion. The best result for the ML models is obtained with the use of ANN (88.71%) and for the DL models is obtained for the GoogleNet architecture (93.75%). The results obtained showed that the number of epochs and the depth of the network have an effect in obtaining the best results. © 2022 IEEE.

关键词： image classification

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