Image fusion can be performed at different levels:signal,pixel,feature and symbol levels.Almost all image fusion algorithms developed to date fall into pixel level.This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses.Particular emphasis is placed on multiscale-based methods.Some performance measures practicable for pixel-level image fusion are also discussed.At last,prospects of pixel-level image fusion are made.
Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.
