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Effect of Particle image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentationg± 13

Effect of Particle Image Velocimetry processing on CT Dicom ...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Suji, R Jenkin Godfrey, W Wilfred Dhar, Joydip ABV-IIITM Gwalior Madhya Pradesh Gwalior India

ISBN: (纸本)9781450398237

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. the results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline. © 2022 ACM.

关键词： Pipelines

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Machine learning content adaptive filters for image de-blurring 13

Machine learning content adaptive filters for image de-blurr...

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13th International conference on Interfaces and Human computer Interaction 2019, IHCI 2019, 12th International conference on Game and Entertainment Technologies 2019, GET 2019 and 13th International conference on computer graphics, Visualization, computer vision and image processing 2019, CGVCVIP 2019

作者： Mainali, Pradip OneSpan Belgium

ISBN: (纸本)9789898533913

this paper proposes a novel algorithm to recover blindly a sharp image from its degraded form by pixel pattern classification and filtering by using the deconvolution filters trained for the corresponding pixel pattern. Noise amplification is a well-known phenomenon that arises due to an ill-posed nature of the deconvolution, which is tackled better with the pattern classification, thereby state-of-the-art image de-blurring quality is demonstrated, yet at the very low complexity. For the computational efficiency, the pixel pattern classes are learnt in a multi-layer structure consisting of the coarser and the finer patterns, reducing the pattern search complexity in the dictionary by a factor of 6 without losing any quality. As compared to deep-learning based methods, the proposed approach provides a smaller memory footprint and lightweight implementation. For lightweight and real-time implementation, the paper also validates a simple class matching metric of l1-norm. the proposed method is suitable for embedded applications such as camera, TV systems, etc. © Copyright 2019 IADIS Press. All rights reserved.

关键词： Pixels

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Design and Development of 3-D Urban Geographical Information Retrieval Application Employing Only Open Source Instruments

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1st International conference on computer vision and image processing (CVIP)

作者： Khan, Ajaze Parvez Porwal, Sudhir Khare, Sangeeta Minist Def DEAL DRDO Dehra Dun Uttar Pradesh India

ISBN: (纸本)9789811021046;9789811021039

Numerous 3-D GIS are being developed today that are both commercially and freely available. A multicity web-based 3-D GIS (named as GLC3d) using open source and freely available software/packages exclusively has been developed. this paper presents the architecture, design, and overview of GLC3d. Open source tools and software's QGIS, Cesium, and MySQL are employed to develop this application. QGIS is utilized for data preparation such as raster, vector, and subsidiary data. MySQL is utilized as database store and data population. GLC3d is based on Cesium (a JavaScript library for creating 3-D globes and 2-D maps in a web browser) and WebGL (a JavaScript API for rendering interactive 3-D computer graphics and 2-D graphics) to display 3-D Globe on the web browser. 3-D visualization for the urban/city geodata is presented on an interactive 3-D Globe. Various city information are generated and incorporated in to the 3-D WebGIS for data representation and decision making.

关键词： GIS (Geographical Information System) WebGL (Web graphics Library) Cesium MySQL KML (Key Hole markup Language) GeoJSON WMS (Web Map Service) OGC (Open Geospatial Consortium) DTM (Digital Terrain Model)

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Defect Detection of Wood Grain images with image Gradient Applied to AE-based generation 6

Defect Detection of Wood Grain Images with Image Gradient Ap...

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6th IEEE International conference on Knowledge Innovation and Invention, ICKII 2023

作者： Si, Jongwook Kim, Sungyoung Kumoh National Institute of Technology Department of Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Department of Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350323535

Detecting and managing various types of defects that occur in the manufacturing process is important for product quality control. Detecting flaws in product presentation is an ongoing research topic in computer vision. Recently, deep learning technology has been used to improve defect detection performance compared to traditional computer vision methods. In this study, we proposed a new method for detecting defects on the surface of wooden boards using image reconstruction with an autoencoder-based model and image gradient filters in image processing using edge detection. Although the reconstruction performance of the autoencoder is significantly inferior to the newest technologies, this characteristic is used to remove noise from input images. We used the publicly available anomaly detection dataset, MV-Tec AD, for training and evaluation and presented experimental results focusing on the "Wood"category. the proposed method in this study demonstrated better detection performance with image processing. © 2023 IEEE.

关键词： image reconstruction

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Fast and Generalisable License Plate Re-identification using Neural Embedding of Fisher Vectors 11

Fast and Generalisable License Plate Re-identification using...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Abhinav Gupta, Shantanu Kozitsky, Vladimir Madhvanath, Sriganesh Univ Utah Salt Lake City UT 84112 USA Univ Wisconsin Madison WI USA MKS Instruments Andover MA USA eBay New York NY USA Conduent Labs Res Triangle Pk NC USA

ISBN: (纸本)9781450366151

We consider the license plate re-identification task, treated here as a one-shot image retrieval problem. Our objective is to learn a feature representation for license plate images, such that a single training image of a given license plate (referred to as a template image) is sufficient to perform nearest-neighbour retrieval with high accuracy at test time. Also, the feature representation should ideally be generalisable across datasets and should be extractable in real-time on resource-constrained embedded hardware or a moderately powerful cellphone. We evaluate representations from person re-identification (re-id) literature, learned from a trained deep convolutional network as well with those derived from a trained Fisher vector. While the convolutional network features perform better than the Fisher vector, we obtain comparable results from a hybrid model projecting the Fisher vector into a lower-dimensional space via two fully connected layers called f2nn using the triplet loss. the proposed hybrid model f2nn generates features which outperform and generalise better than convolutional features on datasets dissimilar to the training corpus. the model can be trained in stages and takes significantly less time to extract features. Further, it uses much smaller feature dimensions for license plate images resulting in faster re-identification, and is therefore well-suited for resource-constrained platforms such as mobile devices.

关键词： image Retrieval Feature Generation Fisher Vectors Neural Networks Triplet Loss Signature Matching Generalisation Dimensionality Reduction License Plate Re-identification Optical Character Recognition

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A Survey of Transformer-Based Few-Shot image Classification Techniques 6

A Survey of Transformer-Based Few-Shot Image Classification ...

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6th International conference on Natural Language processing (ICNLP)

作者： Song, Chaoqi Liu, Ying He, Jinglu Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Few-shot image classification is a critical issue in the field of computer vision, facing challenges related to data scarcity and model generalization. Transformer models, representing self-attention mechanisms, have made significant strides in recent years in the domain of few-shot classification. this paper commences with an introduction to the background and challenges of few-shot classification, along with a description of the principles and structure of the Transformer model. Subsequently, the paper categorizes Transformer-based few-shot image classification methods into meta-learning-based, metric-learning-based, fine-tuning-based, and feature-enhancement-based approaches, whose theoretical foundations of each method are expounded and the comparative analysis of representative algorithms are also provided. Furthermore, the paper delves into prospective research directions in this field.

关键词： Few-shot learning image Classification Survey Transformer

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Recent Trends in image processing and Pattern Recognition 1

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丛书名： Communications in computer and Information Science

1000年

作者： KC Santosh Aaisha Makkar Myra Conway Ashutosh K. Singh Antoine Vacavant Anas Abou el Kalam Mohamed-Rafik Bouguelia Ravindra Hegadi

ISBN: (数字)9783031530852

ISBN: (纸本)9783031530845

this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.;the 62 full papers included in this book were carefully reviewed and selected from 216 submissions. the papers are organized in the following topical sections:;Volume I:;Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

关键词： computer Imaging, vision, Pattern Recognition and graphics Artificial Intelligence computer System Implementation computer Applications

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Automatic synthesis of boolean expression and error detection from logic circuit sketches 6th

Automatic synthesis of boolean expression and error detectio...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Dhiman, Sahil Garg, Pushpinder Sharma, Divya Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur JodhpurRajasthan India

ISBN: (纸本)9789811300196

Automatic techniques to recognize and evaluate digital logic circuits are more efficient and require less human intervention, as compared to, traditional pen and paper methods. In this paper, we propose LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram. the key contributions in the proposed approach are: (i) a novel hierarchical framework to synthesize boolean expression from a hand drawn logic circuit diagram;and (ii) identification of anomalies in drawing. Extensive experimentation was performed through qualitative and quantitative analysis. Results were also compared with existing techniques proposed on the similar problem. Upon experimentation and analysis, our system proved to be more robust to user variability in design and yielded an accuracy of 95.2 %, which is a 4 % gain over others. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Error detection

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DHFormer: A vision Transformer-Based Attention Module for image Dehazing 1

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8th International conference on computer vision and image processing (CVIP)

作者： Wasi, Abdul Shiney, O. Jeba Chandigarh Univ Mohali India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effect of haze and generate haze-free images. Many conventional methods are constrained by their lack of awareness regarding scene depth and their incapacity to capture long-range dependencies. In this paper, a method that uses residual learning and vision transformers in an attention module is proposed. It essentially comprises two networks: In the first one, the network takes the ratio of a hazy image and the approximated transmission matrix to estimate a residual map. the second network takes this residual image as input and passes it through convolution layers before superposing it on the generated feature maps. It is then passed through global context and depth-aware transformer encoders to obtain channel attention. the attention module then infers the spatial attention map before generating the final haze-free image. Experimental results including several quantitative metrics demonstrate the efficiency and scalability of the suggested methodology.

关键词： Residual Learning Transmission Matrix vision Transformer Attention Module

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Exploring Pre-processing Approaches for Deep Learning-based Fingerprint Spoof Detection Mechanisms 6

Exploring Pre-Processing Approaches for Deep Learning-based ...

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6th International conference on Trends in Electronics and Informatics (ICOEI)

作者： Singh, Samridhi Selwal, Arvind Sharma, Deepika Cent Univ Jammu Dept Comp Sci & Informat Technol Jammu India

ISBN: (纸本)9781665483285;9781665483292

Fingerprint-based human authentication being most widely deployed systems are also exposed to many security threats. Among all, spoofing attacks is widely attempted that involves circumventing the sensor module of the system by presenting a fake replica of the original trait of a genuine user. A counter measuring mechanism is deployed in these systems that intelligently measure the vitality characteristics of a presented fingerprint image. these sub-modules are integrated besides sensing device of the system and popularly known as anti-spoofing methods or fingerprint spoof detection (FSD) mechanisms. the evaluation of data-driven enabled computer vision paradigm has facilitated researchers to design intelligent FSD. Pre-processing data to train these FSD models is required to improve the overall quality of the input images. this paper presents a focused review of pre-processing approaches used in deep learning-based FSD models. the proposed research study has clearly observed that image pre-processing is a vital step to significantly improve the overall accuracy and efficiency of FSD approaches.

关键词： Spoof attacks pre-processing fingerprint anti-spoofing normalization label encoding

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