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Optical pattern recognition
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2019
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This work introduces a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Optical pattern recognition
- Resource Type:
- Others
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Others
This package contains: 1. SUFR-W, a dataset of “in the wild” natural images of faces gathered from the internet. The protocol used to create the dataset is described in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition 2. The full set of SUFR synthetic datasets, called the “Subtasks of Unconstrained Face Recognition Challenge” in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Human face recognition (Computer science) Optical pattern recognition
- Resource Type:
- Others
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Others
A dataset of five actors performing five different actions (drink, eat, jump, run and walk) on a treadmill from five different views (0, 45, 90, 135, and 180 degrees from the front of the actor/treadmill; the treadmill rather than the camera was rotated in place to acquire from different viewpoints). The dataset was filmed on a fixed, constant background.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Computer vision Optical pattern recognition Motion perception (Vision)
- Resource Type:
- Others