Computer Vision
The Contest: Goals and Organisation
The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.
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Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a lack of database for fouling images for performing image processing and machine learning algorithm.
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The Contest: Goals and Organization
The 2017 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.
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1854 Views
Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images.
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7597 Views
The Data Fusion Contest 2016: Goals and Organization
The 2016 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.
New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification:
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Wide varieties of scripts are used in writing languages throughout the world. In a multiscript and multi-language environment, it is necessary to know the different scripts used in every part of a document to apply the appropriate document analysis algorithm. Consequently, several approaches for automatic script identification have been proposed in the literature, and can be broadly classified under two categories of techniques: those that are structure and visual appearance-based and those that are deep learning-based.
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674 Views
Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma.
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We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)
It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.
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454 Views
The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits.
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1567 Views
This dataset was developed at the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology as part of the ongoing activities at the Center for Energy and Geo-Processing (CeGP) at Georgia Tech and KFUPM. LANDMASS stands for “LArge North-Sea Dataset of Migrated Aggregated Seismic Structures”. This dataset was extracted from the North Sea F3 block under the Creative Commons license (CC BY-SA 3.0).
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573 Views