Computer Vision

The dataset is genrated by the fusion of three publicly available datasets: COVID-19 cxr image (https://github.com/ieee8023/covid-chestxray-dataset), Radiological Society of North America (RSNA) (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge), and U.S.  national  library  of  medicine  (USNLM) collected  Montgomery  country - NLM(MC) (http

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We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience. 

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The simulated InSAR building dataset contains 312 simulated SAR image pairs generated from 39 different building models. Each building model is simulated at 8 viewing-angles. The sample number is 216 of the train set and is 96 of the test set. Each simulated InSAR sample contains three channels: master SAR image, slave SAR image, and interferometric phase image. This dataset serves the CVCMFF Net for building semantic segmentation of InSAR images.

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The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.

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Results(including reported and extra results) for LSstab. Please refer to our paper "Efficient real-time video stabilization with a novel least squares formulation and parallel AC-RANSAC".

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This is a dataset of diabetic foot. We are preparing to publish this dataset.

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Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth.

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1909 Views

ALL-IDB (Acute Lymphoblastic Leukemia) Image Database for Image Processing

ALL-IDB dataset comprises of two subsets among them one subset has 260 segmented lymphocytes of them 130 belongs to the leukaemia and the remaining 130 belongs to the non leukaemuia class it requires only classification. second subset has around 108 non segmented blood images that belongs to the leukaemia and non leukaemia groups thus requires segmentation and classification.

 

 

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Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to erratic writing style of the individuals. Problem becomes more severe when the input image is doctor's prescription. Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately.

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Annotated image dataset of household objects from the RoboFEI@Home team

This data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.

The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.

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