Models and capabilities of computer vision
Models and capabilities of computer vision.
Machine learning models can be applied to visual input from cameras, movies, or photos in most computer vision solutions.
Image classification
Image classification involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution, you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.
Object detection
Machine learning models for object detection are taught to classify individual objects in a picture and determine their location using a bounding box. A traffic monitoring solution, for example, might use object detection to locate different types of vehicles.
Semantic segmentation
Individual pixels in an image are categorised according to the entity to which they belong using semantic segmentation, a sophisticated machine learning technique. For example, a traffic monitoring solution might use "mask" layers to emphasize distinct vehicles using specific colours over traffic photos.
Image analysis
You can design solutions that integrate machine learning models with advanced image analysis techniques to extract data from photos, such as "tags" that can be used to categorize the image or descriptive captions that describe the situation depicted in the image.
Face detection, analysis, and recognition
Face detection is a type of object detection that focuses on locating human faces in images. This can be used with face geometry analysis and classification algorithms to infer data like as age and emotional condition, and even recognize people based on their facial traits.
Optical character recognition (OCR)
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