Computer Vision

This dataset contains approx. 5000 labeled images of marker on Smith Chart of Keysight VNA 9914a. Since smith chart contains "infinity" (although compensated in the device mathematically), the data from Smith Chart window pane is not apt for tuning the all-pole MW filters.

This dataset can be used to track the marker position while tuning the circuitary when considering Image Processing based Filter Tuning. Images are taken from various angles to ensure the robust behaviour and high detection accuracy. 

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this is dataset for our paper: "Large-scale Benchmark for Uncooled Infrared Image Deblurring", submitted for IEEE SIgnal Processing Letters.

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images of a zebra board providing crafted plumblines

images of scenes containing plumblines

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Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.

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The dataset consists of a large collection of images of about 11,000 different species of birds, with a total of 5 million images. This dataset represents a valuable resource for researchers, conservationists, and bird enthusiasts alike, allowing for a more comprehensive understanding of the diversity and distribution of avian species around the world. The data could be used for a wide range of applications, including species identification, biodiversity monitoring, and ecological research.

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This database includes 47933 RGB-D gesture videos (about 9G). Each RGB-D video represents one gesture only, and there are 249 gestures labels performed by 21 different individuals.

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AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.

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This is the largest database of hyperspectral face images containing hyperspectral image cubes of 78 subjects imaged in multiple sessions. The data was captured with the CRI's VariSpec LCTF (Liquid Crystal Tunable Filter) integrated with a Photon Focus machine vision camera. There are 33 spectral bands comering the 400 - 720nm range with a 10nm step. The noise level in the dataset is relatively lower because we adapted the camera exposure time to the transmittance of the filter illumination intensity as well as CCD sensitivity in each band.

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This dataset was collected in our lab using Kinect to emphasize three points: (1) Larger number of human activities. (2) Each subject performed all actions in a continuous manner with no breaks or pauses. Therefore, the start and end positions of body for the same actions are different. (3) Each subject performed the same actions four times while imaged from four different views: front view, left and right side views, and top view.

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SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset.  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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