Imaging, Vision and Learning Based on Optimization and PDEs

Imaging, Vision and Learning Based on Optimization and PDEs

IVLOPDE, Bergen, Norway, August 29 - September 2, 2016

Tai, Xue-Cheng; Bae, Egil; Lysaker, Marius

Springer International Publishing AG

11/2018

255

Dura

Inglês

9783319912738

15 a 20 dias

565

Descrição não disponível.
Part I Image Reconstruction from Incomplete Data: 1 Adaptive Regularization for Image Reconstruction from Subsampled Data: M. Hintermueller et al.- 2 A Convergent Fixed-Point Proximity Algorithm Accelerated by FISTA for the l_0 Sparse Recovery Problem: X. Zeng et al.- 3 Sparse-Data Based 3D Surface Reconstruction for Cartoon and Map: B. Wu et al.- Part II Image Enhancement, Restoration and Registration: 4 Variational Methods for Gamut Mapping in Cinema and Television: S. Waqas Zamir et al.- 5 Functional Lifting for Variational Problems with Higher-Order Regularization: B. Loewenhauser et al.- 6 On the Convex Model of Speckle Reduction: F. Fang et al.- Part III 3D Image Understanding and Classification: 7 Multi-Dimensional Regular Expressions for Object Detection with LiDAR Imaging: T.C. Torgersen et al.- 8 Relaxed Optimisation for Tensor Principal Component Analysis and Applications to Recognition, Compression and Retrieval of Volumetric Shapes: H. Itoh et al.- Part IV Machine Learning and Big Data Analysis: 9 An Incremental Reseeding Strategy for Clustering: X. Bresson et al.- 10 Ego-Motion Classification for Body-Worn Videos: Z. Meng et al.- 11 Synchronized Recovery Method for Multi-Rank Symmetric Tensor Decomposition: H. Liu.- Index.
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image processing;computer vision;machine learning pattern recognition;optimization;partial differential equations;calculus of variations;numerical analysis