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The two-volume set LNCS 9279 and 9280 constitutes the refereed proceedings of the 18th International Conference on Image Analysis and Processing, ICIAP 2015, held in Genoa, Italy, in September 2015. The 129 papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in the following seven topical sections: video analysis and understanding, multiview geometry and 3D computer vision, pattern recognition and machine learning, image analysis, detection and recognition, shape analysis and modeling, multimedia, and biomedical applications.

Computer Science. --- Image Processing and Computer Vision. --- Pattern Recognition. --- Artificial Intelligence (incl. Robotics). --- Algorithm Analysis and Problem Complexity. --- Computer Graphics. --- Computer science. --- Computer software. --- Artificial intelligence. --- Computer graphics. --- Computer vision. --- Optical pattern recognition. --- Informatique --- Logiciels --- Intelligence artificielle --- Infographie --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- Applied Physics --- Engineering & Applied Sciences --- Algorithms. --- Image processing. --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Automatic drafting --- Graphic data processing --- Graphics, Computer --- Computer art --- Graphic arts --- Electronic data processing --- Engineering graphics --- Image processing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorism --- Algebra --- Arithmetic --- Informatics --- Science --- Digital techniques --- Foundations --- Artificial Intelligence. --- Software, Computer --- Computer systems --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Artificial intelligence --- Pattern recognition systems --- Image analysis --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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The two-volume set LNCS 9279 and 9280 constitutes the refereed proceedings of the 18th International Conference on Image Analysis and Processing, ICIAP 2015, held in Genoa, Italy, in September 2015. The 129 papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in the following seven topical sections: video analysis and understanding, multiview geometry and 3D computer vision, pattern recognition and machine learning, image analysis, detection and recognition, shape analysis and modeling, multimedia, and biomedical applications.

Complex analysis --- Mathematical statistics --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- beeldverwerking --- factoranalyse --- complexe analyse (wiskunde) --- grafische vormgeving --- algoritmen --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)

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This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds, optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis, image classification, action recognition, and motion tracking.

Pattern recognition. --- Computational intelligence. --- Statistics . --- Computer science—Mathematics. --- Computer mathematics. --- Artificial intelligence. --- Mathematical statistics. --- Pattern Recognition. --- Computational Intelligence. --- Statistics and Computing/Statistics Programs. --- Mathematical Applications in Computer Science. --- Artificial Intelligence. --- Probability and Statistics in Computer Science. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computer mathematics --- Mathematics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Riemannian manifolds --- Machine learning --- Computer vision --- optimization --- Geometry, Riemannian

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This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting,  3D brain image analysis,image classification, action recognition, and motion tracking.

Pattern recognition. --- Computational intelligence. --- Statistics . --- Computer science—Mathematics. --- Computer mathematics. --- Artificial intelligence. --- Mathematical statistics. --- Pattern Recognition. --- Computational Intelligence. --- Statistics and Computing/Statistics Programs. --- Mathematical Applications in Computer Science. --- Artificial Intelligence. --- Probability and Statistics in Computer Science.

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• Reference Manager
• EndNote
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This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting,  3D brain image analysis,image classification, action recognition, and motion tracking.

Pattern recognition. --- Computational intelligence. --- Statistics . --- Computer science—Mathematics. --- Computer mathematics. --- Artificial intelligence. --- Mathematical statistics. --- Pattern Recognition. --- Computational Intelligence. --- Statistics and Computing/Statistics Programs. --- Mathematical Applications in Computer Science. --- Artificial Intelligence. --- Probability and Statistics in Computer Science.