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Communicating radar using frequency-shift keying and fractional Fourier transform for automotive applications

Communicating radar using frequency-shift keying and fractio...

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IET International Radar Conference 2018, IRC 2018

作者： Striano, Pasquale Ilioudis, Christos V. Clemente, Carmine Soraghan, John J. Center for Excellence in Signal and Image Processing University of Stratclyde Glasgow United Kingdom

Modern radar applications, such as automotive, integration of radar and communications, have become an important requirement. Systems able to provide joint radar and communications would exploit the same hardware while providing enhanced capabilities to the carrying platform, such as road situation awareness and content sharing. In this article, a novel framework for automotive radars based on the fractional Fourier transform (FrFT) is introduced. The aim of the proposed approach is to preserve radar performance while integrating a communication link. Simulation results demonstrate the potential of the proposed framework for automotive applications. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Automobiles

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Deep Learning Based Novel Cascaded Approach for Skin Lesion Analysis 7th

Deep Learning Based Novel Cascaded Approach for Skin Lesion ...

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7th International Conference on Computer Vision and image processing, CVIP 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. The computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. This research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. The code is available at https://***/shubhaminnani/skin/lesion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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Laser Scar Classification in Retinal Fundus images Using Wavelet Transform and Local Variance 1st

Laser Scar Classification in Retinal Fundus Images Using Wav...

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1st International Symposium on Computer Vision and Machine Intelligence in Medical image Analysis, ISCMM 2019 in conjunction with the 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019

作者： Raut, Rashmi Sapate, Visharad Rokde, Abhay Pachade, Samiksha Porwal, Prasanna Kokare, Manesh Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811387975

Diabetic retinopathy (DR) affects the vision of the person and may eventually lead to blindness. In the initial stage of the disease, patients are treated with a laser to restrict its progression. Such laser treatment leaves behind scars on the retina and patients are advised to undergo screening regularly to track further complications. This paper presents a novel retinal background characterization approach that explores the potential of discrete wavelet transform and rotational-invariant variance features for texture classification of retinal images with and without laser marks. For this experiment, different classifiers, namely, support vector machine, naive Bayes, neural network, and random forest classifiers are tested. We used two publicly available datasets, namely, LMD-DRS and LMD-BAPT. In all cases, the proposed approach obtained the sensitivity, specificity, and accuracy values higher than 68.9%, 70.2%, and 69.4%, respectively. It was found that all performance measures achieve over 87.5, 89.4, and 86.7% for the classification task using random forest classifier. These promising results suggest that the proposed technique can discriminate retinal images having laser marks and without laser marks, and has the potential to be an important constituent in computerized screening solution for retinal images. © Springer Nature Singapore Pte Ltd. 2020

关键词： Discrete wavelet transforms

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Automatic Field of View Extraction with Variable Enhancement of Color Fundus images 14

Automatic Field of View Extraction with Variable Enhancement...

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14th IEEE India Council International Conference, INDICON 2017

作者： Kulkarni, Sushma Kamble, Ravi Kokare, Manesh Dept. of Electronics and Telecom. Engg. Center of Excellence in Signal and Image Processing SGGS Institute of Engineering and Technolgy Nanded431606 India

ISBN: (纸本)9781538643181

Most retinal images suffers from large size with inadequate contrast and brightness. Automated field of view (FOV) extraction and image enhancement significantly increases the performance of image analysis for diabetic retinopathy (DR) detection. This paper presents a simple and automated FOV extraction technique using only one horizontal and vertical scan lines. The FOV image further enhanced using the guided filter with variable enhancement factor. The proposed method has been applied on five publicly available databases. The accuracy of FOV extraction is obtained 100% for Diaretdb0 and ONHSD databases, 99.91% for messidor, 99.32% for E-optha and 98.87% for Diaretdb1 database. The experimental results shows that, proposed method gives the good enhancement on five different databases compared with the existing state-of-the-art methods. Overall, the proposed method can be used as a effective preprocessing tool for accurate detection of lesions in retinal image analysis. © 2017 IEEE.

关键词： Database systems

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2nd International Conference on Computer Vision and image processing, CVIP 2017

作者： Baheti, Bhakti Kutty, Krishnan Gajre, Suhas Talbar, Sanjay Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology NandedMaharashtra India Tata Consultancy Services PuneMaharashtra India

ISBN: (纸本)9789811078972

We present a generic vehicle detection approach using a single query image of vehicle to find similar objects in the test image. The proposed method is without any prior training or segmentation of the test image. The approach is based on computing local self-similarity (LSS) descriptors from query and test images that capture local internal geometric layout within the image. Descriptors from query and test image are matched as two-stage process in sliding window framework. In order to exploit usefulness of LSS for generic object detection, we make following contributions: (i) we present few novel ideas to discard the non-informative descriptors to reduce computational expense in feature matching. (ii) We propose a deformation tolerant version of sliding window-based matching framework rather than point-to-point matching. (iii) We also show that selection of landmark points from the query not only makes the algorithm faster but also improves the performance of detection. We evaluate our results on UIUC car dataset, and results clearly outperform earlier training free methods with 91% accuracy. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Object detection

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Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment

Eff-UNet: A Novel Architecture for Semantic Segmentation in ...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Bhakti Baheti Shubham Innani Suhas Gajre Sanjay Talbar SGGSIET Center of Excellence in Signal and Image Processing Nanded India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Since the last few decades, the number of road causalities has seen continuous growth across the globe. Nowa-days intelligent transportation systems are being developed to enable safe and relaxed driving and scene understanding of the surrounding environment is an integral part of it. While several approaches are being developed for semantic scene segmentation based on deep learning and Convolutional Neural Network (CNN), these approaches assume well structured road infrastructure and driving environment. We focus our work on recent India Driving Lite Dataset (IDD), which contains data from unstructured driving environment and was hosted as an online challenge in NCVPRIPG 2019. We propose a novel architecture named as Eff-UNet which combines the effectiveness of compound scaled EfficientNet as the encoder for feature extraction with UNet decoder for reconstructing the fine-grained segmentation map. High level feature information as well as low level spatial information useful for precise segmentation are combined. The proposed architecture achieved 0.7376 and 0.6276 mean Intersection over Union (mIoU) on validation and test dataset respectively and won first prize in IDD lite segmentation challenge outperforming other approaches in the literature.

关键词： image segmentation Semantics Roads Feature extraction Decoding image resolution Computer architecture

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Edge Detection in SAR images using phase stretch transform 2

Edge Detection in SAR images using phase stretch transform

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2nd IET International Conference on Intelligent signal processing 2015, ISP 2015

作者： VIlioudis, Christos Clemente, Carmine Asghari, Mohammad H Jalali, Bahram Soraghan, John J. Center for Excellence in Signal and Image Processing University of Strathclyde Glasgow United Kingdom Department of Electrical Engineering University of California Los Angeles Los AngelesCA90095 United States

ISBN: (纸本)9781785611360

In this work a novel approach to edge detection on Synthetic Aperture Radar (SAR) images is introduced. The proposed method uses an optics inspired transform which emulates the diffraction of an image through a medium with nonlinear dispersive properties. The experimental results show that the output of the introduced Phase Stretch Transform (PST) in conjunction with further morphological operations can be effectively used for image edge detection.

关键词： Edge detection

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

引用

2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Brain

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Modified ResNet for Volumetric Segmentation of Liver and Its Tumor from 3D CT

Modified ResNet for Volumetric Segmentation of Liver and Its...

引用

International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020

作者： Mourya, Gajendra Kumar Bhatia, Dinesh Gogoi, Manashjit Talbar, S.N. Baid, Ujjwal Dudante, Prasad Department of Biomedical Engineering North-Eastern Hill University Meghalaya Shillong India Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India

ISBN: (纸本)9789811685415

Liver tumor segmentation is the first step to investigate severity of liver disorder. The conventional automatic segmentation methods highly depend on hand-crafted features and priori information, which are tedious and time consuming for medical images. Fully convolutional network (FCN) of the deep learning model can extract features automatically and hierarchically learning concerning layers depth. In this article, we demonstrated the application of modified ResNet for the automatic segmentation of liver and tumor segmentation from 3D CT medical imaging datasets. The FCN ResNet architecture is modified while changing the levels at encoder and decoder ends to overcome the limitation of the conventional segmentation technique. The network input and output image dimension kept the same for training and testing for the designed model. LiTS and 3Dirac data use for training and testing the model. Multifold training datasets strategy utilized. Results were evaluated quantitatively and achieved DICE 0.96 and 0.81 for liver and tumor, respectively. The modified technique will assist to interventional radiologist workflow and faster the diagnostic and therapeutic procedures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computerized tomography

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Detection of Pathological Myopia and Optic Disc Segmentation with Deep Convolutional Neural Networks

Detection of Pathological Myopia and Optic Disc Segmentation...

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IEEE Region 10 International Conference TENCON

作者： Ujjwal Baid Bhakti Baheti Prasad Dutande Sanjay Talbar Center of Excellence in Signal and Image Processing SGGS Institute of Engineering and Technology Nanded Maharashtra India

Pathological Myopia (PM) is one of the leading cause of blindness all over the world and is particularly prevalent in Asia. Pathological myopia is a severe case of myopia which is accompanied by degenerative changes in the retina and can lead to non recoverable vision loss. The accurate diagnosis of PM will enable timely intervention and better treatment planning. Current methods for the detection of pathological myopia are manual and subjective. In this study, we have developed a Convolutional Neural Network (CNN) based system to estimate probability/risk of the patient to be diagnosed with PM from his retinal images. We have also segmented optic disc from the retinal images with novel Residual UNet architecture. Validation data results are evaluated from the online server and achieved 0.9973 and 0.901 for the task of PM classification and optic disc segmentation respectively.

关键词： image segmentation Optical imaging Pathology Computer architecture Retina Adaptive optics Optical computing

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