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Illumination Planning for Object Recognition Using Parametric Eigenspaces
December 1994 (vol. 16 no. 12)
pp. 1219-1227

Presents a novel approach to the problem of illumination planning for robust object recognition in structured environments. Given a set of objects, the goal is to determine the illumination for which the objects are most distinguishable in appearance from each other. Correlation is used as a measure of similarity between objects. For each object, a large number of images is automatically obtained by varying the pose and the illumination direction. Images of all objects together constitute the planning image set. The planning set is compressed using the Karhunen-Loeve transform to obtain a low-dimensional subspace, called the eigenspace. For each illumination direction, objects are represented as parametrized manifolds in the eigenspace. The minimum distance between the manifolds of two objects represents the similarity between the objects in the correlation sense. The optimal source direction is therefore the one that maximizes the shortest distance between the object manifolds. Several experiments have been conducted using real objects. The results produced by the illumination planner have been used to enhance the performance of an object recognition system.

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Index Terms:
object recognition; lighting; planning; brightness; correlation methods; correlation theory; data compression; image coding; eigenvalues and eigenfunctions; transforms; illumination planning; robust object recognition; parametric eigenspaces; structured environments; distinguishability; correlation; similarity measure; illumination direction; planning image set compression; Karhunen-Loeve transform; low-dimensional subspace; parametrized manifolds; minimum distance; optimal source direction; image compression; principal component analysis; appearance matching; parametric appearance representation; pose invariance
H. Murase, S.K. Nayar, "Illumination Planning for Object Recognition Using Parametric Eigenspaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 12, pp. 1219-1227, Dec. 1994, doi:10.1109/34.387485
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