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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.

Scale ambiguity of perspective projection means that image coordinates alone cannot determine absolute scene size and distance. In an ideal camera with focal length $f$ and centered image coordinates,Replacing $(X,Y,Z)$ by $(sX,sY,sZ)$ for any positive $s$ leaves $(u,v)$ unchanged. A geometrically similar scene twice as large and twice as far away therefore produces the same projection.^[ref]

For example, a $0.10\mathrm{m}$ object at depth $1\mathrm{m}$ and a $1\mathrm{m}$ object at depth $10\mathrm{m}$ subtend the same image height under this model. Extra metric information, such as a known object length or a measured camera baseline, supplies a scale. In stereo vision, scaling the reconstructed scene and the assumed baseline together preserves the image observations; measuring the baseline breaks that freedom. Camera calibration of focal length alone does not provide an absolute length for an otherwise unknown scene.

Stereo vision estimates three-dimensional structure from images taken at different viewpoints. After camera calibration and alignment into a common image geometry, a point's horizontal displacement between the two views is its stereo disparity $d$. For parallel ideal cameras with camera baseline $B$ and focal length $f$ in pixel units, similar triangles giveFor $f=800$ pixels, $B=0.10\mathrm{m}$ and $d=40$ pixels, the inferred depth is $2\mathrm{m}$. Doubling the depth halves the stereo disparity, making distant structure harder to measure accurately.^[ref]

A practical apparatus uses two rigidly mounted cameras, a calibration target and synchronized exposures. Matching the same physical feature is essential: repeated textures, hidden surfaces and motion can give false correspondences. The measurement is therefore geometric inference from matched observations, not an intrinsic depth label stored in each pixel.^[ref]