Computer Vision
Simulated Disaster Victim dataset consists of images and video frames containing simulated human victims in cluttered scenes along with pixel-level annotated skin maps. The simulation was carried out in a controlled environment with due consideration towards the health of all the volunteers. To generate a real effect of a disaster, Fuller’s earth is used which is skin-friendly and does not cause harm to humans. It created an effect of disaster dust over the victims in different situations. The victims included one female and four male volunteers.
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This dataset is introduced in paper "Point-cloud-based place recognition using CNN feature extraction" by T. Sun, et al.
This dataset is for place recognition.
The dataset is collected using an omni stereo camera and a VLP-16 Velodyne LiDAR tied together and placed on a tripod.
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Drive testing is a common practice by mobile operators to evaluate the performance and coverage of their deployed mobile communication systems. Drive testing is, however, a very expensive practice. Furthermore, due to the complexity of future 5G mobile networks, accurate and efficient ways of optimizing and evaluating coverage are needed. The dataset contains measurements from a deployed LTE-A mobile communication system and corresponding satellite images.
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Starfish color image from Berkely dataset.
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Glaucoma is the leading cause of irreversible blindness in the world, and primary angle closure glaucoma (PACG) is one of the main subtypes. PACG patients have narrow chamber angle and can be diagnosed by goniscopy, which may cause discomfort and relies too much on personal experience. Anterior segment OCT is able to provide 3D scan of the anterior chamber and assist the ophthalmologists evaluate the condition of chamber angle. It’s faster and objective compare with goniscopy.
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Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.
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Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.
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For effectness verification of our proposed neural network, a total of 19,368 lab-made images of butterfly specimensspanning 48 sub-species areutilizedas testing samples, while 116,208 augmented images are employed for training.
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This dataset contains laser scans of PCBs as explained in "Fault Diagnosis in Microelectronics Attachment via Deep Learning Analysis of 3D Laser Scans". On the left and right image, we have a closer look at one circuit
module of a PCB , before and after die attachment. Notice the different types of glue annotated as A, B, C, D and E. On each circuit there are four glue deposits on each type where approximately the same quantity of glue has been placed. As explained
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test file
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