Titlebook: Subspace Methods for Pattern Recognition in Intelligent Environment; Yen-Wei Chen,Lakhmi C. Jain Book 2014 Springer-Verlag Berlin Heidelbe

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1860-949X s research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extrac

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Active Shape Model and Its Application to Face Alignment,e best match position between the model and the data in a new image. It has been successfully applied to many problems and we apply ASM to the face recognition. We represent all shapes with a set of landmarks to form a Point Distribution Model (PDM) respectively. After landmarks alignment and Princi

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HERTZ

Independent Component Analysis and Its Application to Classification of High-resolution Remote Sensdependent as possible. It has been successfully applied to many problems, such as blind source separation. We apply ICA to high-resolution remote sensing images to obtain an efficient representation of color information. The three independent components are in opponent-color model by which the respo

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Local Structure Preserving Based Subspace Analysis Methods and Applications,ts the intrinsic attributes of samples. In this chapter, inspired by the idea of local structure preserving, we propose two novel subspace methods for face recognition and image clustering tasks. The first is named Supervised Kernel Locality Preserving Projections (SKLPP) for face recognition task,

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Sparse Representation for Image Super-Resolution,eal with the image super-resolution problem with sparse coding, which is based on the well reconstruction of any local image patch by a sparse linear combination of an appropriately chosen over-complete dictionary. Therein the chosen LR (Low-resolution) and HR (High-resolution) dictionaries have to

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