High-Order Models in Semantic Image Segmentation Books

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High Order Models in Semantic Image Segmentation


High Order Models in Semantic Image Segmentation
  • Author : Ismail Ben Ayed
  • Publisher : Academic Press
  • Release : 2018-10
  • ISBN : 0128053208
  • Language : En, Es, Fr & De
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High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging. Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application Presents an array of practical applications in computer vision and medical imaging Includes code for many of the algorithms that is available on the book's companion website

Advances in Information Retrieval


Advances in Information Retrieval
  • Author : Leif Azzopardi
  • Publisher : Springer
  • Release : 2019-04-06
  • ISBN : 9783030157128
  • Language : En, Es, Fr & De
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This two-volume set LNCS 11437 and 11438 constitutes the refereed proceedings of the 41st European Conference on IR Research, ECIR 2019, held in Cologne, Germany, in April 2019. The 48 full papers presented together with 2 keynote papers, 44 short papers, 8 demonstration papers, 8 invited CLEF papers, 11 doctoral consortium papers, 4 workshop papers, and 4 tutorials were carefully reviewed and selected from 365 submissions. They were organized in topical sections named: Modeling Relations; Classification and Search; Recommender Systems; Graphs; Query Analytics; Representation; Reproducibility (Systems); Reproducibility (Application); Neural IR; Cross Lingual IR; QA and Conversational Search; Topic Modeling; Metrics; Image IR; Short Papers; Demonstration Papers; CLEF Organizers Lab Track; Doctoral Consortium Papers; Workshops; and Tutorials.

Progress in Systems Engineering


Progress in Systems Engineering
  • Author : Henry Selvaraj
  • Publisher : Springer
  • Release : 2014-08-12
  • ISBN : 9783319084220
  • Language : En, Es, Fr & De
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This collection of proceedings from the International Conference on Systems Engineering, Las Vegas, 2014 is orientated toward systems engineering, including topics like aero-space, power systems, industrial automation and robotics, systems theory, control theory, artificial intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image processing, and computer vision as well as its applications. The volume’s main focus is on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern achievements in systems engineering.

Energy Minimization Methods in Computer Vision and Pattern Recognition


Energy Minimization Methods in Computer Vision and Pattern Recognition
  • Author : Xue-Cheng Tai
  • Publisher : Springer
  • Release : 2015-01-07
  • ISBN : 9783319146126
  • Language : En, Es, Fr & De
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This volume constitutes the refereed proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015, held in Hong Kong, China, in January 2015. The 36 revised full papers were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on discrete and continuous optimization; image restoration and inpainting; segmentation; PDE and variational methods; motion, tracking and multiview reconstruction; statistical methods and learning; and medical image analysis.

Probabilistic Graphical Models for Computer Vision


Probabilistic Graphical Models for Computer Vision
  • Author : Qiang Ji
  • Publisher : Academic Press
  • Release : 2019-12-12
  • ISBN : 9780128034958
  • Language : En, Es, Fr & De
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Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction