Image Analysis

This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Image Analysis and Recognition, ICIAR 2017, held in Montreal, QC, Canada, in July 2017.

Biomedical imaging is a broad and crucial field, encompassing various techniques for capturing images that have diagnostic and therapeutic applications. These are pivotal in early disease detection, treatment planning, monitoring treatment effectiveness, and advancing our understanding of human physiology and pathology. The widespread use of biomedical images has increased our need for advanced image processing and analysis algorithms. As imaging technologies evolve, becoming more complex and producing larger volumes of data, the development of efficient, accurate, and reliable algorithms becomes essential. This Special Issue has gathered two topical reviews and eight original research articles on the algorithms and software tools used for image processing and analysis in different application fields. This Special Issue presents a body of work that enriches the state-of-the-art in the vast field of biomedical imaging and beyond.

The availability of new high resolution satellite data brings with it the need for new image analysis methods. Traditional pixel-oriented algorithms do not give credit to the spatial coherence of high resolution imagery. In particular, for detection and classification of man-made structures, object-based procedures are much more appropriate. The use of object features such as scale, compactness, orientation and texture, in addition to spectral characteristics, extends the possibilities of remote sensing satellite image analysis considerably.

Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies.