LEADER 07333nam 22008055 450 001 9910483784903321 005 20251226202602.0 010 $a3-642-03641-4 024 7 $a10.1007/978-3-642-03641-5 035 $a(CKB)1000000000772868 035 $a(SSID)ssj0000317466 035 $a(PQKBManifestationID)11292422 035 $a(PQKBTitleCode)TC0000317466 035 $a(PQKBWorkID)10289012 035 $a(PQKB)11472629 035 $a(DE-He213)978-3-642-03641-5 035 $a(MiAaPQ)EBC3064467 035 $a(PPN)139950974 035 $a(BIP)27369294 035 $a(EXLCZ)991000000000772868 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aEnergy Minimization Methods in Computer Vision and Pattern Recognition $e7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, Proceedings /$fedited by Daniel Cremers, Yuri Boykov, Andrew Blake, Frank R. Schmidt 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (X, 494 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v5681 300 $aIncludes index. 311 08$a3-642-03640-6 327 $aDiscrete Optimization and Markov Random Fields -- Multi-label Moves for MRFs with Truncated Convex Priors -- Detection and Segmentation of Independently Moving Objects from Dense Scene Flow -- Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation -- Bipartite Graph Matching Computation on GPU -- Pose-Invariant Face Matching Using MRF Energy Minimization Framework -- Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction -- General Search Algorithms for Energy Minimization Problems -- Partial Differential Equations -- Complex Diffusion on Scalar and Vector Valued Image Graphs -- A PDE Approach to Coupled Super-Resolution with Non-parametric Motion -- On a Decomposition Model for Optical Flow -- A Schrödinger Wave Equation Approach to the Eikonal Equation: Application to Image Analysis -- Computing the Local Continuity Order of Optical Flow Using Fractional Variational Method -- A Local Normal-Based Region Term for Active Contours -- Segmentation and Tracking -- Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernels -- Tracking as Segmentation of Spatial-Temporal Volumes by Anisotropic Weighted TV -- Complementary Optic Flow -- Parameter Estimation for Marked Point Processes. Application to Object Extraction from Remote Sensing Images -- Three Dimensional Monocular Human Motion Analysis in End-Effector Space -- Robust Segmentation by Cutting across a Stack of Gamma Transformed Images -- Shape Optimization and Registration -- Integrating the Normal Field of a Surface in the Presence of Discontinuities -- Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration without Correspondence -- Geodesics in Shape Space via Variational Time Discretization -- Image Registration under Varying Illumination:Hyper-Demons Algorithm -- Hierarchical Vibrations: A Structural Decomposition Approach for Image Analysis -- Inpainting and Image Denoising -- Exemplar-Based Interpolation of Sparsely Sampled Images -- A Variational Framework for Non-local Image Inpainting -- Image Filtering Driven by Level Curves -- Color Image Restoration Using Nonlocal Mumford-Shah Regularizers -- Reconstructing Optical Flow Fields by Motion Inpainting -- Color and Texture -- Color Image Segmentation in a Quaternion Framework -- Quaternion-Based Color Image Smoothing Using a Spatially Varying Kernel -- Locally Parallel Textures Modeling with Adapted Hilbert Spaces -- Global Optimal Multiple Object Detection Using the Fusion of Shape and Color Information -- Statistics and Learning -- Human Age Estimation by Metric Learning for Regression Problems -- Clustering-Based Construction of Hidden Markov Models for Generative Kernels -- Boundaries as Contours of Optimal Appearance and Area of Support. 330 $aOverthelastdecades,energyminimizationmethods havebecomeanestablished paradigm to resolve a variety of challenges in the ?elds of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed very l- ited theoretical understanding of the respective methods, most state-of-the-art methods are nowadays based on the concept of computing solutions to a given problem by minimizing respective energies. This volume contains the papers presented at the 7th International Conf- ence on Energy Minimization Methods in Computer Vision and Pattern Rec- nition (EMMCVPR 2009), held at the University of Bonn, Germany, August 24-28, 2009. These papers demonstrate that energy minimization methods have become a mature ?eld of research spanning a broad range of areas from discrete graph theoretic approaches and Markov random ?elds to variational methods and partial di'erential equations. Application areas include image segmentation and tracking, shape optimization and registration, inpainting and image deno- ing, color and texture modeling, statistics and learning. Overall, we received 75 high-quality double-blind submissions. Based on the reviewer recommendations, 36paperswereselectedforpublication,18asoraland18asposterpresentations. Both oral and poster papers were attributed the same number of pages in the conference proceedings. Furthermore, we were delighted that three leading experts from the ?elds of computer vision and energy minimization, namely, Richard Hartley (C- berra, Australia), Joachim Weickert (Saarbruc ¨ ken, Germany) and Guillermo Sapiro(Minneapolis,USA)agreedtofurtherenrichtheconferencewithinspiring keynote lectures. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v5681 606 $aComputer vision 606 $aPattern recognition systems 606 $aComputers 606 $aAlgorithms 606 $aImage processing$xDigital techniques 606 $aData mining 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aHardware Performance and Reliability 606 $aAlgorithms 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aData Mining and Knowledge Discovery 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aComputers. 615 0$aAlgorithms. 615 0$aImage processing$xDigital techniques. 615 0$aData mining. 615 14$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$aHardware Performance and Reliability. 615 24$aAlgorithms. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aData Mining and Knowledge Discovery. 676 $a006.6 676 $a006.37 701 $aCremers$b Daniel$01758361 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483784903321 996 $aEnergy minimization methods in computer vision and pattern recognition$94196557 997 $aUNINA