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

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A Survey on Few-Shot Techniques in the Context of Computer vision applications Based on Deep Learning 21st

A Survey on Few-Shot Techniques in the Context of Computer V...

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21st International conference on image Analysis and processing (ICIAP)

作者： San-Emeterio, Miguel G. Atos Res & Innovat Madrid 28037 Spain

ISBN: (纸本)9783031133244;9783031133237

This review article about Few-Shot Learning techniques is focused on Computer vision applications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of commonly used techniques and a discussion of the most used benchmarks for FSL computer vision applications. In addition, the paper features a few examples of recent publications in which FSL techniques are used for training models in the context of Human Behaviour Analysis and Smart City Environment Safety. These examples give some insight about the performance of state-of-the-art FSL algorithms, what metrics do they achieve, and how many samples are needed for accomplishing that.

关键词： Few-Shot Learning Deep Learning Computer vision Human Behaviour Analysis Smart City Environment Safety

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The Employment of AI and machine Learning methods for Mushroom images Classification using Sequential CNN Model 3

The Employment of AI and Machine Learning methods for Mushro...

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3rd International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Kukreti, Sanjeev Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The employment of AI and machine learning methods for mushroom classification has several potential uses and applications in the area of AI and related disciplines. It is possible to learn more about biodiversity and ecological health with the use of mushroom taxonomy. The use of AI models for mushroom identification and classification may help us better understand the ecology of a specific area. The work of mushroom picture classification may be both difficult and fascinating. Classifying mushrooms is crucial for a number of reasons, including determining which species are edible and which are dangerous, researching biodiversity, and ecological issues. This study uses a proposed Sequential Convolutional Neural Network (CNN) model for mushroom image classification. Pre-processing of data is done using the graphics processing unit (GPU). The Loss and Accuracy curves are then analysed to provide a visual representation of the Deep Learning model. The proposed model has an expected accuracy of 80%, which would facilitate further study into the categorization of mushrooms. © 2023 IEEE.

关键词： Artificial Intelligence Classification CNN Computer vision Deep Learning image processing Model Training Mushroom image Remote Sensing Sequential Model

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A machine Learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th International conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Recovering image Information from Speckle Noise by image processing 23

Recovering Image Information from Speckle Noise by Image Pro...

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6th International conference on machine vision and applications, ICMVA 2023

作者： Nie, Jianlin Hanson, Steen G. Takeda, Mitsuo Wang, Wei Xi'An Technological University Shaanxi Xi'an China DTU Fotonik Department of Photonics Engineering Technical University of Denmark RoskildeDk-4000 Denmark Utsunomiya University Utsunomiya Tochigi Japan School of Engineering and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom

ISBN: (纸本)9781450399531

As a kind of noise, speckle seriously affects the imaging quality of optical imaging system. However, the speckle image carries a large amount of information related to the physical characteristics of the object surface, which can be used as the basis to identify and judge hidden objects. In this paper, speckle noise removal in optical imaging is studied. The average is derived for the squared moduli of spectra of short-exposure speckle images to recover the amplitude information. At the same time, cross-spectrum function is used to recover the phase information. We use this method to process the images. Then, the simulation experiment analysis is carried out by varying two aspects: the stacking numbers and the different objects. The results show that this method can recover the feature information from the speckle image, thus verifying the feasibility of the method. © 2023 ACM.

关键词： image processing

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Impact of image size on Human Facial Expression Recognition: A Relative Study 10

Impact of image size on Human Facial Expression Recognition:...

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10th International conference on Communication and Signal processing, ICCSP 2024

作者： Swathi, A. Menon, Shashank Bhavana, V. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9798350353068

The recognition of facial emotions has received growing focus in recent years due to its importance and the significant role it plays in shaping the way humans interact with computers. This can be achieved using deep learning algorithms which are currently being used for various vision-based applications like self-driving vehicles. In this paper, we used the Convolutional Neural Network (CNN) for training and testing images with different facial expressions using the TensorFlow machine learning library. The application is comprised of two stages: a recognizer for validation purposes and a data training model that is used to train the data. The Data training model uses CNN to train data. With the help of this algorithm, the system can recognize the six universal emotions, anger, disgust, joy, surprise, sadness, and fear, along with neutral. The experiment demonstrated that the outcomes of the training program were significantly influenced by the image size that is used for training. As a result, both the RGB and grayscale images yielded comparable results, whereas images with different sizes exhibited variations in performance. The RGB images achieved a training accuracy of 99.666%, a validation accuracy of 99.95%, and a test accuracy of 32.28%, while the combination of RGB and grayscale images attained a training accuracy of 98.99%, a validation accuracy of 92.76%, and a test accuracy of 72.56%. © 2024 IEEE.

关键词： Convolutional neural networks

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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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