Computer Vision

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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The data uploaded here shall support the paper 

Decision Tree Analysis of  ...

which has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authors

Julian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. Fellner

During review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.

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Dataset with images of soccer ball acquired by a humanoid robot competing in the RoboCup Humanoid Kidsize League.

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These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.

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Our complex street scene(CSS) containing strong light and heavy shadow scenes mainly comes from the Kitti dataset.  Our datasets are captured by driving around Karlsruhe's mid-size city, in rural areas, and on highways. We equipped a standard station wagon with two high-resolution color and grayscale video cameras. Up to 15 cars and 30 pedestrians are visible per image. We aim to verify the performance of the algorithm in specific and complex street scenes.

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Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).

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The AirMuseum dataset is intended for multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). It consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements.

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