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 is related to depth by , where is focal length in pixel units and is camera separation. similar triangles give the relation; a smaller disparity means greater distance. The relation requires corresponding scene points and calibrated geometry. Since , 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.
