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This book explores novel representations and comparison tools for analyzing structures and irregularities in signals and images. In opposition to most methods, the ideas introduced here work at the pixel-level. Structures and irregularities are studied using approaches that stem from discrepancy theory, functional analysis and potential theory, and low-level signal processing. In particular we show that structure enhancing similarity measures or representations have advantages over usual tools for image comparison and analysis.

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Jean-Luc received a Master´s degree in applied math-computer science from the french INP-ENSEEIHT. During his last year he worked for T-Systems, Darmstadt, Germany, developing artificial intelligence softwares. He then worked for the Fuzzy Logic Lab, Linz, Austria submitting his thesis in 2012. He is now a postdoc in the math department at Drexel.

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