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LOCALIZING FEATURES WITH MASKING FOR SATELLITE AND DEBRIS CLASSIFICATION

LOCALIZING FEATURES WITH MASKING FOR SATELLITE AND DEBRIS CL...

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28th IEEE international conference on image processing Challenges (IEEE ICIP)

作者： Chaudhary, Shubham Jain, Parima Jakhetiya, Vinit Guntuku, Sharath C. Subudhi, Badri N. Indian Inst Technol Jammu Jagti India Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781665426015

In this work, we propose a localization and masking-based satellite and debris classification technique. SPAce-craft Recognition leveraging Knowledge of space environment (SPARK) dataset consists of 120K images where both RGB and corresponding Depth images are available. However, the depth images are noisy and inaccurate and significantly affect the classification task performance. To address this issue, we first create mask images of the RGB images which are used as input to the Convolutional Neural Network (CNN) for efficient classification of different satellites and debris. The depth images are first de-noised and hole filled using a simple morphological opening operation. Then masked images are calculated using both RGB and processed depth images. This masking operation provides two advantages: 1. it removes noise and fills the holes in the depth images and 2. it highlights satellites and debris while suppressing other information which does not contribute towards the classification task. We use the pre-trained EfficientNet B4 architecture and fine-tuned it with an edition of Global average pooling (GAP) and three dense layers. Our results show that the inclusion of the masking operation significantly improves the overall classification performance, achieving 97.76% accuracy on the validation data.

关键词： Location awareness Satellites image recognition conferences Classification algorithms Convolutional neural networks Sparks

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Development of Contactless Employee Management System with Mask Detection and Body Temperature Measurement using TensorFlow 6

Development of Contactless Employee Management System with M...

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6th international conference on Wireless Communications, signal processing and Networking (IEEE WiSPNET)

作者： Ulleri, Pranesh Mukund, S. Sudarshan, K. Zenith, Kiran Shibu, Sai N. B. Amrita Vishwa Vidyapeetham Dept Comp Sci Engn CSE Amritapuri India Amrita Vishwa Vidyapeetham Ctr Wireless Networks & Applicat WNA Amritapuri India

ISBN: (纸本)9781665440868

CoVID-19 pandemic signified the importance of social distancing and non-contact routines. Even after the pandemic, everyone should abide by the same protocols for a hygienic life. Institutions must put in place pre-emptive measures before people return. These include marking the presence of employees and monitoring their health status. This paper focuses on developing a contactless employee management system with sensor fusion and facial recognition technology. image processing capability in the system track employees entering the institution. Body temperature sensor collect the health status of each employee at the entrance. With the onboard cameras and deep learning algorithms, the system also makes sure if the employee is wearing a mask or not. The aim is to authenticate an employee and check whether they abide by the protocols of the institution. Ease of use, maintenance, and low-cost installation is the motivation behind the system design. The best beneficiaries are educational institutions and corporate offices. The system records employee health data and uses it for the contact tracing. This paper discusses the system design and algorithms of the non-invasive employee management system.

关键词： Raspberry Pi Deep Learning image processing Facial Recognition CoVID Protocol

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Infrared and ISAR image Fusion Based on Quadtree Decomposition and Bézier Interpolation 6

Infrared and ISAR Image Fusion Based on Quadtree Decompositi...

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6th international conference on signal and image processing, ICSIP 2021

作者： Han, Chunlei Yang, Di Lu, Yao Hou, Kaifa An, Wenbo Wang, Hongmei Xi'An Research Institute of Navigation Technology Xi'an China Northwestern Polytechnical University School of Astronautics Xi'an China

ISBN: (纸本)9780738133737

Inverse Synthetic Aperture Radar (ISAR) image obtained by stationary radar is the result of longitudinal and lateral two-dimensional high-resolution imaging of moving target. Infrared image is a thermal image corresponding to the temperature distribution of the scene, which formed by collecting infrared radiation emitted from the target and the surrounding. In order to make full use of the complementary information of infrared image and ISAR image, an algorithm that combines infrared image with ISAR image is researched. Firstly, this paper applies quadtree decomposition and Bézier interpolation to reconstruct the background of the ISAR image. Secondly, the salient features of the ISAR image are extracted by subtracting the reconstructed background from the original ISAR image. Finally, this algorithm fuses the salient features with the infrared image using the fusion rule proposed in this paper, through which to get the final fused image. Experimental results show that the algorithm can better preserve both the clear target and detailed information of infrared image and ISAR image in subjective evaluation. Compared with other fusion algorithms, experiments also verify the superiority of the algorithm in objective evaluation. © 2021 IEEE.

关键词： Interpolation

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PolSAR image Classification Based on Resblock Combined with Attention Model 6

PolSAR Image Classification Based on Resblock Combined with ...

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6th international conference on signal and image processing, ICSIP 2021

作者： Yang, Zhongao Zhang, Qingyuan Chen, Weidong Chen, Chang University of Science and Technology of China School of Microelectronics Anhui Hefei China University of Science and Technology of China Department of EEIS Anhui Hefei China

ISBN: (纸本)9780738133737

Deep learning technology can effectively extract the rich polarimetric information contained in polarimetric Synthetic aperture radar (PolSAR) data. However, the lack of labeled samples limits its performance. The majority of methods use iterative algorithms or need preprocessing on dataset to expand the labeled sample are time consuming. In this paper, an end-to-end method which can better utilize mid-level features and the input polarimetric features of CNN network is proposed. First, high-dimensional polarimetric features are extracted from raw data and sent into the CNN network to produce sufficient polarimetric information, then the Convolutional Block Attention Module (CBAM) is not only inserted into the network layer but also after the input data to achieve better classification performance and accelerate network convergence under tiny amounts of labeled samples. Meanwhile, a feature selection method is proposed based on the channel attention module to reduce the memory consumption and computational complexity while maintaining performance. The experiments tested on Flevoland dataset prove the effectiveness of proposed method compared with traditional method. © 2021 IEEE.

关键词： image classification

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A Comparative Analysis of image Dehazing using image processing and Deep Learning Techniques 6

A Comparative Analysis of Image Dehazing using Image Process...

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6th IEEE international conference on Communication and Electronics Systems, ICCES 2021

作者： Likhitaa, Palagiri Sai Anand, R. Amrita School of Engineering Bengaluru Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665435871

The haziness presence in atmosphere now poses to be a critical issue, especially in terms of visibility and visual quality. The major challenge faced in the present day scenario is to stitch a technique that detects the presence of haziness and to provide an efficient method to remove it as well. This process is known as image dehazing. Although different dehazing algorithms has been introduced, still better performances can be achieved which is addressed in this paper. This challenge is overcome by using a suitable classification algorithm to detect hazy images and then cleaning it to get corresponding clear image. In this method, two goals are presented and executed, Initial one be a comparative analysis between the machine learning and deep learning models is presented for the classification of hazy and clear images and then the hazy images are processed by a robust deep neural network for the removal process. In order to classify, two ML algorithms, namely, SVM and Logistic Regression are used and two CNN standard models Resnet, Vgg are also used and their performances are compared to obtain best classification algorithm for this problem. In classification model, standard CNN models outperforms Machine Learning models by a fair margin by proving to be more accurate. Similarly for dehazing also DCP image processing algorithm is implemented with a proposed CNN de-hazing network and their performances are also compared to prove the efficiency of proposed CNN model. ITS, HSTS, Indoor SOTS Dataset are used as inputs. The performance of CNN network is compared with an image processing technique called Dark Channel Prior (DCP) algorithm. Comparatively the CNN model outperforms the DCP algorithm by attaining higher image quality metrics. To evaluate the image quality, two metrics are compared, they are Structural-similarity-index (SSIM) and peak-signal-to-noise-ratio (PSNR). © 2021 IEEE.

关键词： image quality

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ENHANCED HYPERSPECTRAL image SUPER-RESOLUTION VIA RGB FUSION AND TV-TV MINIMIZATION

ENHANCED HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA RGB FUSION...

引用

IEEE international conference on image processing (ICIP)

作者： Vella, Marija Zhang, Bowen Chen, Wei Mota, Joao F. C. Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

ISBN: (纸本)9781665441155

Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model- or DL-based.

关键词： Deep learning super-resolution hyper-spectral imaging optimization total variation

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Junction Monitoring System for Emergency Vehicles and Density control Using image processing

Junction Monitoring System for Emergency Vehicles and Densit...

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2021 international conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021

作者： Nagarathinam, S. Dhivyapriya, R. Pavithra, C. Harshapradha, M. Saralesh Kumar, R. Kumaraguru College of Technology Department of ECE Tamil Nadu India

ISBN: (纸本)9781665428293

In today's world, everyone is confronted with one of the most important challenges of traffic congestion, which affects almost all major cities. The main cause of increased traffic bottlenecks is a large number of vehicles on the road and insufficient infrastructure. With the help of current traffic lights, it is essential to successfully regulate traffic flow. Because most traffic signals have a predetermined green light sequence, the green light sequence is established without regard for the presence of emergency vehicles. As a result, emergency vehicles such as ambulances, police cars, fire engines, and other emergency vehicles become trapped in traffic and are delayed in arriving at their destination. The goal of this project is to regulate traffic by determining traffic density on each side of the road and enabling a controlling option;in the event that an emergency vehicle arrives, its direction is determined from its siren signal, and the signals are enabled. The image was shot by the traffic junction's camera when it was free, and it serves as the reference image. The camera continuously captures real-time images of the traffic intersection. The microcontroller, on the other hand, generates the difference frame by comparing the real-time image to the reference image and calculating traffic density using Digital image processing algorithms. The density data is transferred to the server, and traffic lights are managed in accordance with it. When an emergency vehicle is recognized, traffic light sequences are automatically activated in such a way that a green signal is given in the direction where the vehicle is detected, and normal operation is assumed once it has passed through the signal. © 2021 IEEE.

关键词： image processing

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A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REMOTE SENSING SCENE

A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REM...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhang, Pei Bai, Yunpeng Wang, Dong Bai, Bendu Li, Ying Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated AeroSp Ground Ocean Big Shaanxi Prov Key Lab Speech Image Informat Proc Xian 710129 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Shaanxi Peoples R China

ISBN: (纸本)9781728176055

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a meta-learning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.

关键词： Remote sensing scene classification few-shot learning meta-learning

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image Interpolation Using Non-adaptive Scaling algorithms for Multimedia Applications—A Survey

Image Interpolation Using Non-adaptive Scaling Algorithms fo...

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international conference on Automation, signal processing, Instrumentation and Control, i-CASIC 2020

作者： Neetha, C.H. John Moses, C. Selvathi, D. Department of Electronics and Communication Engineering Sreyas Institute of Engineering and Technology Hyderabad Telangana500068 India Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi Tamil Nadu626005 India

ISBN: (纸本)9789811582202

image interpolation is a technology of maintaining the quality of digital images by introducing new high-quality pixels. The interpolation schemes are used in several image processing applications like computer graphics, reconstructing medical images, producing high resolution in still images, and in television pictures as well as processing remote sensing images. This proposed survey deals with the study of different non-adaptive image interpolation techniques used for multimedia applications. This study analyzes the characteristics of different non-adaptive image interpolation algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). From the review on interpolation schemes, this work identifies that several low-complexity conventional non-adaptive image interpolation schemes outperform the recently developed non-adaptive image interpolation algorithms in terms of PSNR. Further, it suggests that for any decisive applications, the conventional non-adaptive interpolation schemes can also be used to maintain the quality of picture. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Surveys

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DEEP NEURAL NETWORK-BASED NOISY PIXEL ESTIMATION FOR BREAST ULTRASOUND SEGMENTATION 29

DEEP NEURAL NETWORK-BASED NOISY PIXEL ESTIMATION FOR BREAST ...

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IEEE international conference on image processing (ICIP)

作者： Jin, Songbai Lu, Wenkai Monkam, Patrice Tsinghua Univ Dept Automat Easysignal Grp Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The success of modern deep learning algorithms for image segmentation heavily relies on the availability of high-quality labels for training. However, obtaining accurate labels is time-consuming and tedious, and requires expertise. If directly trained with dataset with noisy annotations, networks can easily overfit to noisy labels and result in poor performance, which might lead to serious misinterpretation. To this end, we propose a noisy pixel estimation approach based on deep neural network, which helps correct the noisy annotations resulting in better prediction performance. First, a deep neural network is trained to detect noisy pixels from image annotations. Then, the estimated noisy pixels are used to correct the noisy annotations. Finally, the corrected annotations are used to train the deep learning model. Our proposed framework is validated on the breast tumor segmentation task. The obtained experimental results show that our proposed method can improve the robustness of deep learning model under noisy annotations while achieving favorable performance against existing noisy label correction methods.

关键词： image segmentation weakly supervised learning noisy labels breast tumor segmentation

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