Titlebook: Image Fusion; Theories, Techniques H. B. Mitchell Book 2010 Springer-Verlag Berlin Heidelberg 2010 Change Detection.Image Fusion.Image Segm

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AGGER

Multi-resolution Analysisework for the multi-resolution analysis of an input image by decomposing an input image into a sequence of wavelet planes and a residual image. We start by giving a brief review of multi-resolution analysis. We then move on to the DWT and its use in image fusion. To make our discussion more concrete

Aboveboard

Image Sub-space Techniquesnput image into a lower dimensional space or sub-space. We shall concentrate on statistical sub-space methods which rely on a covariance matrix which is constructed from the input images. The techniques considered in this chapter include: principal component analysis (PCA), non-negative matrix facto

Affection

Ensemble Learning or classifiers from an ensemble of weak predictors or classifiers. In the context of image fusion, we use the term ensemble learning to denote the fusion of K input images ..,.?∈?{1,2, . . .,.}, where the .. are all derived from the same base image .*. The .. themselves highlight different features

Fantasy

concentrate

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Image Key Pointstortion. In practice, the key points are not perfectly invariant but they are a good approximation. To make our discussion more concrete we shall concentrate on two key point algorithms: SIFT and SURF and their use in spatial alignment.

ureter

Image Similarity Measuresmage patches, . and .. Image similarity measures play an important role in many image fusion algorithms and applications including retrieval, classification, change detection, quality evaluation and registration. For the sake of concreteness we shall concentrate on intensity based similarity measure

opalescence

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Markov Random Fieldsobabilities. Markov random field (MRF) theory thus provides a convenient and consistent way for modeling context dependent entities such as image pixels and correlated features. Contextual models are one way to model prior information and MRF theory can be applied to model a prior probability of con