= Computer vision
<Computer vision> infers information about a scene from <digital images> or video. Tasks include recognizing objects, <image segmentation> and estimating three-dimensional geometry. A <digital image> records <pixel> measurements after <image formation>; lighting, viewpoint and occlusion therefore affect what can be inferred. A dark <pixel> can indicate a dark surface, a shadow or a surface facing away from the light.
For example, in rectified <stereo vision> with parallel cameras, horizontal disparity $d$ is related to depth by $Z=fB/d$, where $f$ is <focal length> in <pixel> units and $B$ is <camera> separation. <similar triangles> give the relation; a smaller disparity means greater distance. The relation requires corresponding scene points and calibrated geometry. Since $|dZ/dd|=fB/d^2$, a fixed disparity error produces much greater depth error for distant points. <Camera calibration>, correspondence and uncertainty are thus part of the <computation>, rather than details that can be ignored after acquiring a picture.
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