Computer Vision
This dataset contains sheets of handwritten Telugu characters separated in boxes. It contains vowel, consonant, vowel-consonant and consonant-consonant pairs of Telugu characters. The purpose of this dataset is to act as a benchmark for Telugu handwritting related tasks like character recognition. There are 11 sheet layouts that produce 937 unique Telugu characters. Eighty three writers participated in generating the dataset and contributed 913 sheets in all. Each sheet layout contains 90 characters except the last which contains 83 characters where the last 10 are english numerals 0-9.
- Categories:
1713 Views
A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.
- Categories:
17457 Views
A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India.
- Categories:
1227 Views
Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.
- Categories:
1793 Views
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.
- Categories:
1678 Views
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.
- Categories:
386 Views
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.
- Categories:
4380 Views
Starfish color image from Berkely dataset.
- Categories:
433 Views
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.
- Categories:
671 Views
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.
- Categories:
7730 Views