Computer vision is a fascinating field of study that involves teaching computers to interpret and understand images and videos, much like humans do.
With the ever-increasing availability of visual data, computer vision has become a crucial technology for a wide range of applications, from self-driving cars to facial recognition and medical imaging.
Challenges on Data-Driven Science will cover the following tasks:
Image classification: The task of assigning a label to an image based on its content. For example, identifying whether an image contains a cat or a dog.
Object detection: The task of identifying the location and boundaries of objects within an image. This can be used in applications such as self-driving cars, where the vehicle needs to detect other cars, pedestrians, and traffic signals.
Semantic segmentation: The task of dividing an image into different regions and assigning each region a label. This can be used in applications such as medical imaging, where doctors need to identify different organs or tissues in an image.
Pose estimation: The task of estimating the position and orientation of an object in 3D space. This can be used in robotics applications, where robots need to grasp objects and manipulate them in a precise manner.
Optical character recognition (OCR): The task of recognizing text in an image and converting it into machine-readable text. This can be used in applications such as document scanning and translation services.
Face recognition: The task of identifying and verifying the identity of a person based on their facial features. This can be used in applications such as security and surveillance.
and more.
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