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Automatic Mixed Precision and Distributed Data-Parallel Training for Satellite image Classification using CNN 15

Automatic Mixed Precision and Distributed Data-Parallel Trai...

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15th International conference on Machine vision (ICMV)

作者： Nuwara, Yohanes Kitt, Wong W. Juwono, Filbert H. Ollivierre, Gregory Asia Pulp & Paper Sinarmas Land Plaza MH Thamrin Jakarta Indonesia Curtin Univ Malaysia CDT 250 Bellevue WA 98009 USA Univ Southampton Malaysia Iskandar Puteri 79100 Johor Malaysia OmegaCrop 71-75 Shelton St London England

ISBN: (纸本)9781510666184;9781510666191

Deep learning models for computer vision in remote sensing such as Convolutional Neural Network (CNN) has benefitted acceleration from the usage of multiple CPUs and GPUs. there are several ways to make the training stage more effective in terms of utilizing multiple cores at the same time by processing different image mini-batches with a duplicated model called Distributed Data Parallelization (DDP) and computing the parameters in a lower precision floating-point number called Automatic Mixed Precision (AMP). We would like to investigate the impact of DDP and AMP training modes on the overall utilization and memory consumption of CPU and GPU, as well as the accuracy of a CNN model. the study is performed on the EuroSAT dataset, a Sentinel-2-based benchmark satellite image dataset for image classification of land covers. We compare training using 1 CPU, using DDP, and using both DDP and AMP over 100 epochs using ResNet-18 architecture. the hardware that we used are Intel Xeon Silver 4116 CPU with 24 cores and an NVIDIA v100 GPU. We find that although parallelization of CPUs or DDP takes less time to train on the images, it can take 50 MB more memory than using only a single CPU. the combination of DDP and AMP can release memory up to 160 MB and reduce computation time by 20 seconds. the test accuracy is slightly higher for both DDP and DDP-AMP at 90.61% and 90.77% respectively than without DDP and AMP at 89.84%. Hence, training using Distributed Data Parallelization (DDP) and Automatic Mixed Precision (AMP) has more benefits in terms of lower GPU memory consumption, faster training execution time, faster convergence towards solutions, and finally, higher accuracy.

关键词： Automatic Mixed Precision Convolutional Neural Network Distributed Data-Parallel graphics processing Unit Remote Sensing

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Neti Neti: In search of deity 12

Neti Neti: In search of deity

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Verma, Yashaswi Jawahar, C.V. Center for Visual Information Technology IIIT-Hyderabad 500032 India

ISBN: (纸本)9781450316606

A wide category of objects and scenes can be effectively searched and classified using the modern descriptors and classifiers. With the performance on many popular categories becoming satisfactory, we explore into the issues associated with much harder recognition problems. We address the problem of searching specific images in indian stone-carvings and sculptures in an unsupervised setup. For this, we introduce a new dataset of 524 images containing sculptures and carvings of eight different indian deities and three other subjects popular in the indian scenario. We perform a thorough analysis to investigate various challenges associated with this task. A new image-representation is proposed using a sequence of discriminative patches mined in an unsupervised manner. For each image, these patches are identified based on their ability to distinguish the given image from the image most dissimilar to it. then a rejection-based re-ranking scheme is formulated based on both similarity as well as dissimilarity between two images. this new scheme is experimentally compared with two baselines using state-of-the-art descriptors on the proposed dataset. Empirical evaluations demonstrate that our proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors. © 2012 ACM.

关键词： image representation

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Single View Homography Estimation for an Inclined Textured Planar Surface: Overcoming the Inverse and Ill-Posed Challenge! 14

Single View Homography Estimation for an Inclined Textured P...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Kumari, Pooja Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9798400716256

Homography estimation is a crucial step in many computer vision problems involving the planar transformation of an image from one view to another. Generally, from two image views of a scene with a planar patch, one can compute the homography matrix (H) using Direct Linear Transformation (DLT) or its variants. In this study, we work on the inverse scenario to solve an ill-posed problem, where given only a single image (IτP) of an inclined textured planar surface, we attempt to estimate both H and the orthogonal view (IτO) of the planar surface. We propose to solve this problem of homography Estimation using an optimization framework. To the best of our knowledge, there has barely been any work done on this inverse scenario of homography estimation from only a single image view. the research presented in this paper successfully achieves image rectification and homography computation by assuming a perspective transformation on an upright planar patch when viewed by a camera placed exactly in front of it. A cost function with suitable constraints is utilized to refine homography estimation. Experimental results reveal the efficiency of the proposed approach in the case of images of checkerboard and other textured planar surfaces (real-world images with lines and other prominent textures). © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

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15th International conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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Semantic Map Injected GAN Training for image-to-image Translation 12th

Semantic Map Injected GAN Training for Image-to-Image Transl...

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12th indian conference on computer vision, graphics, and image processing, ICVGIP 2021

作者： Kshatriya, Balaram Singh Dubey, Shiv Ram Sarma, Himangshu Chaudhary, Kunal Gurjar, Meva Ram Rai, Rahul Manchanda, Sunny Computer Vision Group Indian Institute of Information Technology Sri City Chittoor India Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India Bangalore India

ISBN: (纸本)9789811941351

image-to-image translation is the recent trend to transform images from one domain to another domain using a generative adversarial network (GAN). the existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Let us refer to the original training as the training for the translation of the input image into the target domain. the injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. the semantic map is only utilized at the training time and is not required at the test time. the experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID, and KID scores after injecting semantic training as compared to the original training. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Patel, Heena Upla, Kishor P. Sardar Vallabhbhai National Institute of Technology SuratGujarat India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

this paper addresses an approach for classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with very narrow but continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Due to the nature of data acquisition with contiguous bands, the HS images are very useful in classification and/or the identification of materials present in the captured geographical area. However, the low spatial resolution and large volume of HS images make the classification of those images as a challenging task. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for classification of HS image. Pre-processing procedure with autoencoder leads to obtain optimized weights in the initial layer of CNN model. Moreover, features are enhanced in the HS images by utilizing the autoencoder. the CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. the potential of the proposed approach has been verified by conducting the experiments on three recent datasets. the experimental results are compared with the results obtained in the geoscience and remote sensing society (GRSS) image Fusion Contest-2018 held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and other existing frameworks for HSI classification. the testing accuracy of classification for the proposed approach is better than that of the other existing deep learning based methods. © Springer Nature Singapore Pte Ltd, 2019.

关键词： Remote sensing

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Multiscale intuitionistic fuzzy roughness measure for color image segmentation 14

Multiscale intuitionistic fuzzy roughness measure for color ...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Dubey, Yogita K. Mushrif, Milind M. Nehare, Prajakta R. Department of Electronics and Telecommunication YCCE Nagpur India Department of Electronics and Communication SDMP Nagpur India

ISBN: (纸本)1595930361

In this paper, a method for color image segmentation using multiscale intuitionistic fuzzy roughness measure is proposed. the traditional roughness measure tends to over focus on the little important homogeneous regions but is not accurate enough to measure the homogeneity in an image. By applying the theories of scale space and using intuitionistic fuzzy representation for images, roughness is measured under multiple scales. Multiscale representation can tolerate the disturbance of trivial regions, and intuitionistic fuzzy representation deals with hesitancy in image boundary, therefore produces precise segmentation results. Copyright 2014 ACM.

关键词： Fuzzy sets

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the Hybrid Attention Single image Super Resolution Network with Conditional Random Field 15

The Hybrid Attention Single Image Super Resolution Network w...

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15th International conference on graphics and image processing, ICGIP 2023

作者： Wei, Jiayu Li, Yanfeng Chen, Houjin Sun, Jia Yao, Xu Beijing Jiaotong University China

ISBN: (纸本)9781510675018

Recently, Transformer-based methods have achieved excellent results in various computer vision tasks, including Single image Super-Resolution (SISR). In SwinIR, the mechanism of cross-window connection and local self-attention of Swin Transformer are introduced into the SISR task, achieving breakthrough improvements. However, the local self-attention mechanism of Swin Transformer has a limited spatial range of input pixels, which limits the ability of the super-resolution network to extract features in a wide range. Aiming at this problem, an enhanced CNN and Transformer hybrid module is designed for feature extraction by combining self-attention, spatial attention and channel attention. Taking advantage of their complementary strengths, the range of activated pixels is expanded while still maintaining a strong capability for local feature characterization. In addition, simply extending the activation pixel range without constraints is not conducive to reconstruction. Aiming at this problem, the Neural Window Fully-connected Conditional Random Fields (NeW FC-CRFs) are integrated for feature fusion. the shallow features are inputted into NeW FC-CRFs along with deep features, allowing for the utilization of multi-level information during the fusion process. In summary, we propose the Hybrid Attention Super Resolution Network with Conditional Random Field (HANCRF). Extensive experiments show that HANCRF achieves competitive results with a small number of parameters. © 2024 SPIE. All rights reserved.

关键词： Pixels

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Methods and system for segmentation of isolated nuclei in microscopic breast fine needle aspiration cytology images 10

Methods and system for segmentation of isolated nuclei in mi...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Garud, Hrushikesh Karri, Sri Phani Krishna Sheet, Debdoot Maity, Ashok Kumar Chatterjee, Jyotirmoy Mahadevappa, Manjunatha Ray, Ajoy Kumar Indian Institute of Technology Kharagpur Kharagpur721301 India Midnapur Medical College and Hospital Midnapur721101 India

computer vision systems for automated breast cancer diagnosis using Fine Needle Aspiration Cytology (FNAC) images are under development for a while now. Accurate segmentation of the nuclei in microscopic images is crucial for functioning of these systems, as most quantify and analyze nuclear features for diagnosis. this paper presents a nucleus segmentation system (NSS) involving pre-processing, pre-segmentation and refined segmentation stages. the NSS includes a novel pixel transformation step to create a high contrast grayscale representation of the input color image. the grayscale image gives NSS the capability-to disregard elements that mimic nuclear morphological and luminescence characteristics, and to minimize effects of non-specific staining of cytoplasm by Hematoxylin. Experimental results illustrate generalizability of the NSS to use multiple refined segmentation techniques and particularly achieve accurate nucleus segmentation using active contours without edges(F-score > 0.92). the paper also presents the results of experiments conducted to study the impact of image pre-processing steps on the NSS performance. the pre-processing steps are observed to improve accuracy and consistency across tested refined segmentation techniques. © Springer International Publishing AG 2017.

关键词： Cytology

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Table detection in document images using header and trailer patterns 12

Table detection in document images using header and trailer ...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Harit, Gaurav Bansal, Anukriti Indian Institute of Technology Jodhpur Rajasthan India

ISBN: (纸本)9781450316606

this paper presents a new approach to detect tabular structures present in document images and in low resolution video images. the algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. the performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side. © 2012 ACM.

关键词： Light trailers

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