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检索条件"任意字段=2023 International Conference on Image Processing and Computer Vision, IPCV 2023"

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Bone image Analysis For Early Detection of Osteoporosis 3

Bone Image Analysis For Early Detection of Osteoporosis

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

作者： Rekha, B.S.S.Kiran Reddy, A.Madhumitha Vaishnavi, Ch. Sreethiskesh, A. Babu, Chunduri Madhu B v Raju Institute of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350319125

Numerous studies believe that bone mineral density (BMD) is the only technique that fundamentally reveals bone strength, despite the fact that the latter provides important information regarding the risk of osteoporotic fracture. Analyzing macro- and micro-structural characteristics quantitatively may improve beyond simply estimating bone density. Bone structural information can be obtained using non-interfering, non-carnage procedures. According to many research, bone mineral densitometry (BMD), which also provides important information on osteoporosis, primarily measures bone strength. Numerous works in the field of photo-domain computer vision have introduced efficient strategies that benefit from different viewpoints on the same instance in other domains such as self-supervised and semi-supervised learning. In order to tackle these problems we are using a simple yet efficient method termed as multi-view ct network or mvctnet along with multimodal data fusing technique called Early Fusion. Utilizing cutting-edge methods and technologies will improve image analysis in multi-view CT scanning for osteoporosis detection, increasing the precision and effectiveness of osteoporosis detection. © 2023 IEEE.

关键词： Bone imaging Bone mineral density Bone quality Bone structure Computed tomography (CT) Dual X-ray absorptiometry radiography Early Fusion Technique image Fusing Machine learning Micro computed tomography Multimodal data fusion Osteoporosis Quantitative computed tomography

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Use of Self-Organizing Kohonen Maps for Quantization of Tomography images 4

Use of Self-Organizing Kohonen Maps for Quantization of Tomo...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mahesh Prasanna, K. Selvan, Mary Deepa, R. Suganthi, D. Maheswari, B. Uma Madan, Parul Vivekananda College of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Puttur574203 India Rmk College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Puduvoyal601206 India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600026 India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Tamil Nadu Chennai602105 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Tamil Nadu Chennai600119 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

In this work, a proof of concept is presented, which shows that EIT images reconstructed by EIDORS using methods based on the Finite Element Method can be transformed into less smooth versions using a combination of unsupervised classification and quantization. For that, Kohonen's Self-Organizing Maps were used. The objective is to group the image areas into well-defined regions by reducing the smoothing of the reconstructed image, thus allowing readings to be performed that are more strongly associated with the regions of interest. EIDORS is a reference tool that forms a comparative basis for research developments on the subject of EIT. The generation of this article is part of broader studies in the field of optimization of EIT tomographic images. Studies in the area of image reconstruction using Compressed Sensing combined with Computational Intelligence techniques stand out, with the aim of reducing computational cost, increasing processing speed and increasing the quality of the reconstructed image. © 2023 IEEE.

关键词： Tomography

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Deep Regression Network for the Single image Super Resolution of Multimedia Text image 5

Deep Regression Network for the Single Image Super Resolutio...

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5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Karthick, S. Muthukumaran, N. Anna University Tamil Nadu Chennai600025 India Sri Eshwar College of Engineering Department of ECE Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350338287

A key problem in low-level computer vision with several applications is super resolution of a picture. In order to create high resolution (HR) images with the necessary edge structures and texture information, Single image Super Resolution (SISR) transforms low resolution (LR) photos into HR images. Additional information is provided by HR pictures, which can be used for a variety of tasks like security and medical imaging. However, the rebuilt image suffers from some loss in detail areas, making it more difficult to achieve higher accuracy with lower error. Regression network-based super-resolution (RNSR), which converts LR images into HR images, was created to solve these problems. For SISR, a deep regression network with 50 layers is created. The suggested RNSR approach translates LR multimedia text images into HR images with 98% accuracy, 0.02% error, 97% precision, and 94% specificity, according to the simulation study. The regression network may produce high-quality photos based on the effectiveness of the suggested RNSR approach. © 2023 IEEE.

关键词： Medical imaging

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Feature Enhancement Algorithm for Recognition of Small Targets in Low Resolution images 6

Feature Enhancement Algorithm for Recognition of Small Targe...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Jingjing Research China Signify N.V Shanghai China

ISBN: (纸本)9798350325485

Small object detection is a challenging task in computer vision due to limited pixels, low resolution, and sparse feature information. Existing infrared-based methods for small object detection suffer from low accuracy and susceptibility to environmental disturbances. This paper proposes a novel approach using an enhanced lightweight YOLOv5 network for high-precision infrared small object detection. The method involves capturing depth data using a ToF sensor, converting it to a four-channel RGB image through pseudo-color encoding, and applying HSV conversion. By preprocessing the depth values and incorporating them into the lightweight YOLOv5 model, the proposed approach extracts infrared small object detection features and improves the network's efficiency. Experimental results demonstrate that the proposed method achieves a significant improvement in detection performance, with an average precision of 97.4% on an enhanced dataset compared to pseudocolor encoded images. © 2023 IEEE.

关键词： image enhancement

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Semantic Segmentation of Endoscopic images using Transformers - Towards Trading off Dice and Precision 19

Semantic Segmentation of Endoscopic Images using Transformer...

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19th IEEE international conference on Intelligent computer Communication and processing conference, ICCP 2023

作者： Slavescu, Radu Razvan Ulics, Daniel-Alexandru Slavescu, Kinga Cristina Technical University of Cluj-Napoca Department of Computer Science Bariţiu 28 Cluj-NapocaRO-400027 Romania Iuliu Hatieganu University of Medicine and Pharmacy 2nd Pediatrics Clinic Crisan 5 Cluj-NapocaRO-400027 Romania

ISBN: (纸本)9798350370355

Detecting polyps in endoscopic images is very important for early detection of colorectal cancer, hence the importance of proper training of practitioners. Preparing a good set of images for training them is a challenging task, due to lack of comprehensive image sets. To address this, we explored a method based on Deep Learning to automatically detect polyps in colonoscopy images and generate the corresponding masks, such a solution providing the ability of turning unlabeled data into an important learning resource. We explored different augmentations techniques and used a FCBFormer based model, which was trained using different hyperparameters, managing to obtain two main versions that are noteworthy, one that is using the AdaBelief optimizer and the weighted sum between the Binary Cross-Entropy loss and the Dice loss as the loss function, which leads to a value of 0.93 for Dice and 0.99 for Precision, outperforming the baseline FCBFormer architecture. On the other hand, employing the Adam optimizer and Binary CrossEntropy loss leads to better Recall and Precision values (0.95 and 0.998 respectively) at the expense of a slight reduction of the Dice score from 0.93 to 0.92. © 2023 IEEE.

关键词： Semantic Segmentation

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Sea View Extension for Semantic Segmentation in Cityscapes 9

Sea View Extension for Semantic Segmentation in Cityscapes

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9th international conference on Applied System Innovation, ICASI 2023

作者： Yue, Zongcheng Sham, Chiu-Wing Lo, Chun Yan Cheung, William Yiu, Chung Yim The University of Auckland School of Computer Science Auckland New Zealand The University of Auckland Department of Property Auckland New Zealand

ISBN: (纸本)9798350398380

Semantic segmentation in computer vision is a challenging area of research, aiming to accurately segment and categorize objects and regions within an image. One widely used dataset for this task is Cityscapes, which contains a variety of city-related object classes such as cars, pedestrians, bicycles, and buildings. However, the Cityscapes dataset does not include any aquatic view classes, which limits its potential for applications in coastal and marine environments. This paper presents a novel approach to extend the Cityscapes dataset with aquatic classes to address this limitation. Our proposed method involves the implementation of two state-of-the-art neural network models, one based on the Cityscapes dataset and the other on a common aquatic dataset. We then selectively extract the aquatic segmen-tation results from the corresponding model according to the aquatic label. We further generate a mask image for the sea class and merge it precisely with the resulting image from the Cityscapes-based model. Our method is evaluated by comparing the performance of the original Cityscapes-based model with the extended Cityscapes-based model on a set of test images that contain aquatic views. The results show that our approach can maintain the original model's high segmentation accuracy for all views except for aquatic areas while preserving the relevant parts of the marine model in terms of accuracy and area coverage. Additionally, our approach does not require retraining, thus saving computational resources and time. © 2023 IEEE.

关键词： Semantic Segmentation

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international conference on computer Technology and Information Science, CTIS 2023

International Conference on Computer Technology and Informat...

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1st international conference on computer Technology and Information Science, CTIS 2023

The proceedings contain 15 papers. The topics discussed include: quantum approximate optimization algorithm parameter prediction using a convolutional neural network;transferring four-dimensional atomic states at one step between separated cavities;quantum properties of nitrogen-vacancy center in diamond coupled to mechanical resonators;adopting multiple vision transformer layers for fine-grained image representation;optimization of ICANet lightweight human pose estimation based on HRNet;a depth discrimination method based on the scintillation of frequency domain;tracking multiple dynamic objects by a combination of laser ranging and UHF RFID phase information;effective machine learning-based skin disease diagnosis using PyTorch;and a port and starboard fuzziness suppressed algorithm based on vector directivity zero.

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Multiple Objects Identification for Autonomous Car using YOLO and CNN 7

Multiple Objects Identification for Autonomous Car using YOL...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Prakash, M. Janarthanan, M. Devi, D. Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

One of the most anticipated 21st-century technologies and one of the subjects of the current study that is gaining significant research attention is autonomous driving. By detecting and responding to the vehicle's immediate environment, autonomous driving tries to navigate roads without human assistance. It presents significant difficulties for computer vision and machine learning. processing several candidate object locations, which are frequently referred to as 'proposals,' is one difficulty. These choices only offer basic localization, which needs to be refined in order to obtain precise localization. However, solutions to these issues frequently come at the expense of efficiency, precision, or simplicity. R-CNN first introduced in 2013, and their upgraded versions Fast R-CNN and Faster R-CNN are recent examples of cutting-edge deep learning models that tackle the issue of object detection. YOLO, a paradigm for object detection created for speed and real-time use, was released in 2015. © 2023 IEEE.

关键词： Autonomous vehicles

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An effective image-based Improved Deep Neural Network Framework for Coal and Gangue Detection 3

An effective Image-based Improved Deep Neural Network Framew...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Praveena, M. Afsana, A. Devi, T. Durga Sudharsan, S. Arulkumar, V. Dr Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore641049 India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350322842

In coal power plants, gangue is a by-product that is not wanted, even though coal is the energy source utilized the most worldwide. As a consequence of this, the removal of gangue from coal during the pre-processing stage of the mining technique is an essential step. With the development of new computer technologies, a method may be discovered that will allow for an improvement in the efficiency of gangue separation. This study presents a method for identifying gangue using improved deep neural networks. The method involves building a coal-gangue filtration system based on the distinctions between gangue and coal in terms of its rough surface and grayscale feature. In addition, this tactic will make it possible for robots, rather than people, to identify the gangue. Suppose the technology described in this article is applied in the coal industry. In that case, it will lead to the computerization of the extraction of coal gangue, an increase in the raw coal processing efficiency, and an improvement in the quality of the coal. According to the findings of experiments, the strategy enhances the accuracy results of coal and gangue materials by as much as 98.87% compared to the currently used approaches. © 2023 IEEE.

关键词： Coal

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Analysis of Pneumonia detection in X-ray images using different filters based Convolution Neural Networks 21

Analysis of Pneumonia detection in X-ray images using differ...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Das, Suchismita Samanta, Subhankar Rout, Minakhi School of Computer Engineering Bhubaneswar India School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798350358230

With recent advances and diversity of medical image acquisition technologies and processing, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging and complicated tasks, such as segmentation and classification. Since early detection of any abnormality is crucial, the method will help in a timely and accurate diagnosis to launch appropriate treatment at the earliest possible stage. The proposed study aims to analyse and compare the results of medical images using a single Convolutional Neural Network by applying different image filters. The work emphasizes various filters and determines which one performs best with a particular CNN architecture. The model has been experimented with the publicly available Kaggle Covid-19 dataset comprising 21,165 images of 4 classes with different state-of-The-Art filters such as Adaptive Wiener, Median, Adaptive Median, Gaussian, and Mean filters. In this experiment, the Gaussian filter gave the best accuracy of 92.98% in the improper class classification. Whereas the model with no filter gave an accuracy of 91.94%. Which shows with proper image enhancement techniques image recognition can be further improved. © 2023 IEEE.

关键词： image enhancement

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