Overview of Computer Vision

Below is a high-level overview of the field of Computer Vision.

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Computer Vision Machine Learning Methods for acquiring, processing, analysing and reasoning about images or video sequences in order to extract meaningful/useful information that can be interpreted and acted upon as desired Example applications Robot vision & control Species identification Code reading (bar & QR codes) Optical Character Recognition (OCR) Photo enhancement Facial recognition Medical scan analysis/diagnosis Topographical modelling Navigation of autonomous vehicles / robots Creation of training/test data for Machine Learning Augmented reality Video indexing (e.g. video splitting, sequence cataloguing) Event detection Techniques used in Computer Vision Image manipulation Style/feature transfer (e.g. transferring the style associated with an artist/topic to another image) Image interpolation (e.g. morphing one image into another and producing intermediate images) Enhancement (e.g. brightness/contrast levels) Restoration, noise removal (e.g. inpainting) Stitching Filtering Image analysis Pattern recognition Colour/intensity analysis Pixel counting Edge detection Blob detection Image/video generation Scene reconstruction (e.g. generating faces/landscapes) Motion analysis Tracking movement Motion estimation Egomotion (estimating a camera's movement relative to a scene) Optical flow (determining the apparent movement of each point in an image) Object/feature recognition Detection Position extraction Size measurement Shape recognition Classification 3D pose estimation (transforming an object in a 2D image into an estimated 3D model, describing the object's position and orientation) Image search/retrieval (e.g. finding other images with similar features)