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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

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An Innovative Way of Empirical Analysis of Various Structures used in DL Algorithms for the Better Practices 4

An Innovative Way of Empirical Analysis of Various Structure...

引用

4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kumar, Himanshu Jasim, Laith Gehlot, Anita Devi, Guntoju Kalpana Yadav, Kanchan Kumar, Neelam Sanjeev Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Electronics & Communication Engineering Dehradun248007 India Cmr Technical Campus Department of Cse Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai Vadapalani26 India

ISBN: (纸本)9798350360165

the importance of deep learning is growing with artificial intelligence. It operates quickly. Entangled nerve fibres (CNNs), a well-known deep learning method, have shown impressive results in painting, classification, and semantic division. the actuator function-a crucial part of CNNs -was looked at in the context of low velocities in this book study, Engineering Vehicle Rear System Programme. the text begins with a comprehensive analysis of all four conventional activation functions, including the well recognised sigmoid function, and a summary of their respective benefits. It then discusses the potential benefits. Swish, FTS, and Relu- are the three new operating systems that are offered with SoftPlus. the research then includes a combination of new activators and extra activation functions. It operates at the TensorFlow framework level. On the CIFAR-10 dataset, more image recognition comparisons are carried out. According to the results, using a combination of novel activation functions produces better recognition accuracy than using just one new activation function. Moreover, the hybrid technique shows higher convergence speeds and shorter average processing times, indicating its effectiveness in real-world applications. © 2024 IEEE.

关键词： Deep learning

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Facial Expression recognition In-the-Wild with Deep Pre-trained Models 17th

Facial Expression Recognition In-the-Wild with Deep Pre-tra...

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17th European conference on computer vision, ECCV 2022

作者： Li, Siyang Xu, Yifan Wu, Huanyu Wu, Dongrui Yin, Yingjie Cao, Jiajiong Ding, Jingting Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ant Group Hangzhou China

ISBN: (纸本)9783031250743

Facial expression recognition (FER) is challenging, when transiting from the laboratory to in-the-wild situations. In this paper, we present a general framework for the Learning from Synthetic Data Challenge in the 4th Affective Behavior Analysis In-the-Wild (ABAW4) competition, to learn as much knowledge as possible from synthetic faces with expressions. To cope with four problems in training robust deep FER models, including uncertain labels, class imbalance, mismatch between pretraining and downstream tasks, and incapability of a single model structure, our framework consists of four respective modules, which can be utilized for FER in-the-wild. Experimental results on the official validation set from the competition demonstrated that our proposed approach outperformed the baseline by a large margin. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Defect detection system based on deep learning optimization 4

Defect detection system based on deep learning optimization

引用

4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Tang, Haitong Liang, Jiajun Zhou, Xiang Xue, Hao Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9781665463997

this system combines machine vision and deep learning, and deeply investigates key technologies such as vision sensing, platform control, camera calibration, image processing, human-computer interaction technology, and cloud database, and designs and realizes the combination of deep learning and traditional vision inspection system. the traditional vision inspection system includes image acquisition module, camera calibration module, and defect detection module. Our team added a defect classification module and a cloud data visualization module, which classify the damage and defects of the screen to determine the fault type and facilitate the repair strategy. © 2022 IEEE.

关键词： computer vision

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Fast Food image recognition using Transfer Learning 4

Fast Food Image Recognition using Transfer Learning

引用

4th international conference on Cognitive Computing and Information processing, CCIP 2022

作者： Rajesh, Arnav A Raghu, Madhumita Sangeetha, J. M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456487

Food recognition is a relatively difficult task when compared to traditional image recognition due to the close similarities between different categories of food. We tackle this problem using a Convoluted Neural Network model with and without weights that are pre trained on a much larger dataset. this allows us to utilize a much smaller dataset to fine-Tune the weights in order to achieve a higher accuracy in food image recognition. We have compared the accuracy of different Convoluted Neural Network (i.e. VGG16 and AlexNet) models with and without the incorporation of Transfer Learning to correctly classify Fast Food images. © 2022 IEEE.

关键词： Stochastic systems

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Application of an Improved YOLOv9 algorithm for stem node detection in High-Resolution global sugarcane images 4

Application of an Improved YOLOv9 algorithm for stem node de...

引用

4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Chen, Weiqi Xie, Zhongjian Chen, Xinwei Zhang, Yaya Wan, Zhuxuan College of Electronic Information Guangxi Minzu University Nanning China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

this study aims to address the problem of poor stem node detection and recognition accuracy in high-resolution global sugarcane images, which is crucial for accurately identifying sugarcane stem node locations during automated sugarcane cutting. Since the sugarcane stem node occupies a very small area of the entire image, only about 0.2%, the feature blurring problem caused by scaling the original image to fit the network input size requirements often results in the loss of important information. In order to overcome this problem, this study conducts an in-depth study on the problem of small target feature loss caused by high-resolution images and proposes an improved algorithm STMFF-YOLOv9 (small target multiple feature fusion YOLOv9) based on YOLOv9-c. the algorithm uses a multi-scale stitching strategy to enhance the feature acquisition of small objects in image data training, designs an FDS (fusion down sampling) module with an improved feature fusion network downsampling process to reduce the loss of small target features, and implements a SE channel attention mechanism to recalibrate the fused channel features, thereby strengthening the focus on useful features. Experimental results showed that STMFF-YOLOv9 significantly improves the detection accuracy of sugarcane stem nodes in global images. Compared to YOLOv9-c, it achieves an 8.3% increase in precision, a 13.1% increase in recall rate, a 10.3% increase in mAP0.5, and a 9% increase in mAP0.5:0.95. Compared to other models, STMFF-YOLOv9 also demonstrates superior detection performance, effectively proving its capability to detect global sugarcane stem nodes. © 2025 SPIE.

关键词： image enhancement

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Deep Learning Technique for Dermoscopy image processing Model for Early Melanoma Detection 4

Deep Learning Technique for Dermoscopy Image Processing Mode...

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Gopikrishna, V. thinakaran, K. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Kuthambakkam Tamil Nadu Thandalam600124 India

ISBN: (纸本)9798350385793

the impact of melano ma has grown over the past three decades, making early detection more important for reducing mortality rates. In light of this, a dermoscopy image of a tumor and/or skin pigmentation coupled with an automatic, trustworthy system capable of detecting the presence of melanoma can be a powerful tool in the field of medical diagnostics. When it comes to cancer-related mortality, skin cancer consistently places near the top world wide. Fewer lives will be lost to skin cancer if it can be diagnosed and treated early. Automatic detection of skin lesions using dermoscopic photographs is still a tough and complicated task, despite the increased awareness of me lanoma and the medical community's efforts to develop a therapy. Using computers to evaluate images of skin lesions is crucial for early detection of skin cancer. Skin cancer has surpassed other forms of cancer as the most common cause of cancer death in people in recent years. Accurate melanoma diagnosis requires combining dermatological knowledge with computer vision techniques. To aid in the early diagnosis of melanoma, novel diagnostic techniques are urgently needed. Recent technological developments have enhanced a variety of computer-aided diagnostics for early detection of melanoma skin cancer. Four processes pre-processing, segmentation, feature extraction, and classification are required for accurate melanoma diagnosis. this research presents a brief survey on melanoma image segmentation, feature extraction and selection and classification models. this research helps numerous research scholars, academicians to understand the working of available models and their limitations and to design innovative and accurate solutions for accurate melanoma detection. © 2024 IEEE.

关键词： image segmentation

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A Framework for Masked-image recognition System in COVID-19 Era 4th

A Framework for Masked-Image Recognition System in COVID-19 ...

引用

4th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2021

作者： Prakash, Vijay Garg, Lalit Fomiceva, Elena Pineda, Sergio Vega Santos, Alex Navia Bawa, Seema Thapar Institute of Engineering and Technology Patiala India University of Malta Msida Malta

ISBN: (纸本)9783031070044

Face recognition techniques have been widely developed and used for many years. Several approaches and models are adopted and successfully used to perform face recognition in airports, supermarkets, banks, etc. However, with the emergence of the COVID-19 pandemic, the whole world came across the requirement to use face masks. the mask’s partial covering of the face makes some well-known face recognition algorithms perform poorly or even fail. this paper has developed a real-time framework to detect, recognize, and identify people to authenticate them before accessing an app, device, or location. the newly created framework offers a unique set of capabilities, including the ability for users to select from various authentication methods based on their preferences or circumstances. the application’s face recognition section uses cutting-edge AI and computer vision algorithms to offer the user accurate face detection and recognition, even when the face is partially hidden behind a mask. © 2022, Springer Nature Switzerland AG.

关键词： Face recognition

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Object Detection with YOLO Model on NAO Humanoid Robot 10th

Object Detection with YOLO Model on NAO Humanoid Robot

引用

10th Biennial international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Biswas, Sougatamoy Nandy, Anup Naskar, Asim Kumar Natl Inst Technol Rourkela Rourkela Odisha India

ISBN: (纸本)9783031451690;9783031451706

Object detection is an important part in the field of robotics as it enables robots to understand their surroundings. the NAO humanoid robot is extensively used in human-robot interaction research. In this study a novel approach combining the VGG16 network and You Only Look Once (YOLO) algorithm is used for object detection using the NAO robot. YOLOv7 is selected for its best balanced information retention, quick inference, accurate localization, and identification of objects as compared to several bounding box algorithms. VGG16 network is adopted as a feature extractor to optimize the performance of object detection for NAO low-resolution camera images. Once feature extraction is completed then it's output layer is combined with our fine tuned YOLOv7 model for object detection. the fine-tuned YOLOv7 model is proposed with some pre-processing techniques such as image augmentation, angle movement, and scale resizing for the performance improvement. the efficiency of the proposed model is compared with the performance of other state-of-the-art models.

关键词： Object detection computer vision Humanoid robots Object recognition

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A Robust Object recognition Using Modified YOLOv5 Neural Network 10

A Robust Object Recognition Using Modified YOLOv5 Neural Net...

引用

10th international conference on Signal processing and Integrated Networks, SPIN 2023

作者： Balmik, Archana Barik, Subhasish Nandy, Anup National Institute of Technology Rourkela Department of Computer Science & Engineering Rourkela India

ISBN: (纸本)9781665490993

Human beings can recognize and detect the object in any image or video with ease, but when it comes to machines dealing with the same task, it's quite challenging. In the past years, there has been significant progress in the area of computer vision. However, researchers are still working on enhancing the performance of object recognition algorithms in terms of accuracy and speed. this paper presents one of the greatest CNN representatives, You Only Look Once (YOLO), which tackles object detection in the simplest and most efficient way. this study primarily focuses on object recognition with modified YOLOv5. Here, we have implemented the model on a dataset comprising five objects. We have analyzed various preprocessing and feature extraction techniques to enhance the performance of the proposed model. the highest mean average precision (mAP) achieved by the modified YOLOv5 is 90.7%. the recognition performance of the modified YOLOv5 is compared with the YOLOv4 algorithm. It is also compared with various conventional object detection algorithms outside the YOLO family, such as Support vector machine (SVM), deep Convolutional neural network (CNN), and EfficientNet. the results proved to be promising as compared to the other state-of-the-art techniques. © 2023 IEEE.

关键词： Object recognition

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A Comparative Analysis of Texture Analysis Methods for image Retrieval 15

A Comparative Analysis of Texture Analysis Methods for Image...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhabliya, Dharmesh Vijayalakshmi, V.J. Sharma, Amit Kumar Sood, Gourav Latha, B. Meena, Y.R. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Teerthanker Mahaveer University College of Engineering Uttar Pradesh Moradabad India Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research for Business and Technology punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

Texture evaluation is a middle aspect of many computer vision obligations, consisting of pattern c, object recognition, and photogrammetry. this paper specializes in a comparative evaluation of popular texture analysis techniques utilized in photograph retrieval. 4 primary strategies are discussed here: local Binary patterns (LBP), Histogram of orientated Gradients (HOG), Scale Invariant feature transform (SIFT), and shape Tensor (ST). Each of those strategies quantifies a few details of the picture and converts it right into a characteristic vector. LBP is an easy and broadly used algorithm that computes an image's gray-scale depth. It utilizes a round community around every pixel to construct a texture descriptor. HOG is an extension of LBP that computes distortions in neighborhood gray-scale versions and orientations. SIFT extracts greater texture info than LBP or HOG by looking at variations between adjoining pixels. A structure tensor is an image-processing approach that computes 2D-order statistical moments to generate function vectors. the paper examines the performance of these techniques for numerous forms of texture distributions. It discovered that SIFT and ST executed pleasant basic. But, depending at the sort of software, a few methods are probably greater suitable than others. © 2024 IEEE.

关键词： Characteristic Evaluation Obligations Photogrammetry Popularity Variations

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