
Scale Space and Variational Methods in Computer Vision
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
The 24 revised full papers presented together with 44 poster papers were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on denoising and enhancement, segmentation, image representation and invariants, shape analysis, and optical flow.
More details
Other editions
Additional editions

Content
- Title Page
- Preface
- Organization
- Table of Contents
- Part I: Denoising and Enhancement (O1 and O5)
- Fiber Enhancement in Diffusion-Weighted MRI
- Introduction
- Motivation for Morphological Scale Spaces on R3S2
- A Moving Frame of Reference for Scale Spaces on R3S2
- The Evolution Equations for Scale Spaces on DW-MRI
- Solving the Evolutions by Convolution on R3 S2
- Conclusion
- References
- Numerical Schemes for Linear and Non-linear Enhancement of DW-MRI
- Introduction
- The Euclidean Motion Group SE(3)
- Left-Invariant Derivatives
- Convection-Diffusion Processes
- Finite Difference Schemes for R3 S2 Diffusion
- Efficient Computation of Left-invariant Derivatives
- Numerical Contour Enhancement
- Perona-Malik Diffusion on R3 S2
- Enhancement of DTI of the Human Brain
- Conclusion
- References
- Optimising Spatial and Tonal Datafor Homogeneous Diffusion Inpainting
- Introduction
- Image Inpainting with Homogeneous Diffusion
- Optimising Spatial Data
- Probabilistic Sparsification
- Nonlocal Pixel Exchange
- Results
- Optimising Tonal Data
- Grey Value Optimisation
- Results
- Conclusion
- References
- Nonlocal Surface Fairing
- Introduction
- Nonlocal Means Image Denoising
- The Nonlocal Variational Fairing
- Non Local Surface Diffusion Flow (NL-SDF)
- Numerical Results
- Conclusions
- References
- Nonlocal Filters for Removing Multiplicative Noise
- Introduction
- Nonlocal Filters for Multiplicative Noise
- The Similarity Measure of Deledalle et al.
- Properties in the Presence of Additive Noise
- Properties in the Presence of Multiplicative Noise
- A New Similarity Measure for Multiplicative Noise
- Weight Definition of Our Nonlocal Filters
- Numerical Results
- References
- Volumetric Nonlinear Anisotropic Diffusion on GPUs
- Introduction and Motivation
- Contribution
- Prerequisites
- Inhomogeneous Diffusion
- Nonlinear Anisotropic Diffusion
- Surface Structure and the Hessian
- Tangent Space Projection of the Hessian
- Algorithm: Retrieving the Diffusion Tensor
- Implementation
- Discretization
- Results
- Conclusions
- References
- A Statistical Multiresolution Strategy for Image Reconstruction
- Introduction
- Statistical Multiresolution Estimation
- Existence of SMRE
- An a Priori Parameter Selection Method
- On the Choice of S
- Algorithmic Methodology
- Inexact Uzawa Algorithm
- Subproblems
- Numerical Results
- Denoising
- Deconvolution and Inpainting
- Conclusion
- References
- A Variational Approach for Exact Histogram Specification
- Introduction
- Variational Approach for Exact Histogram Specification
- Sorting Algorithms
- A Variational Approach
- Experimental Results
- Contrast Compression
- Histogram Equalization Inversion
- Conclusions
- References
- Joint ToF Image Denoising and Registration with a CT Surface in Radiation Therapy
- Introduction
- A Joint Registration and Denoising Approach
- Numerical Minimization Algorithm
- Validation and Application of the Model
- Discussion and Conclusion
- References
- Either Fit to Data Entries or Locally to Prior: The Minimizers of Objectives with Nonsmooth Nonconvex Data Fidelity and Regularization
- Introduction
- Motivation
- Notations
- Outline of the Paper
- Preliminaries
- The Objective F Is Not Too Bad
- (Local) Minimizers Are Strict
- Either Fidelity or Prior
- Strict Minimizers Solve Exactly Linear Systems
- Local Stability of Strict Minimizers
- A Special Case
- Numerical Examples
- Concluding Notes
- References
- A Study on Convex Optimization Approaches to Image Fusion
- Introduction
- Convex Optimization Models
- Equivalent Convex Formulations
- Variational Image Decompositions
- Global and Exact Optimization
- Duality Based Algorithms
- Experiments
- Fusing Binary Images
- Applications to Medical Imaging and Remote Sensing
- Conclusion and Acknowledgements
- References
- Efficient Beltrami Flow in Patch-Space
- Introduction
- The Beltrami Framework
- Operating in Patch-Space
- Implementation and Results
- Parameter Optimization
- Reducing Time Complexity
- Residual Noise
- Non-gaussian Denoising
- Conclusions
- Future Work
- References
- A Fast Augmented Lagrangian Method for Euler's Elastica Model
- Introduction
- Augmented Lagrangian Method for Euler's Elastica Model
- The Existing Algorithm
- The Proposed Algorithm
- Numerical Solutions for Subproblems
- Notations
- Sub-problems
- u-sub Problem.
- p-sub Problem.
- n-sub Problem.
- h-sub Problem.
- Numerical Examples
- Conclusion and Future Work
- References
- Deblurring Space-Variant Blur by Adding Noisy Image
- Introduction
- Regularization
- Translation Based Operator Space
- Summary of the Trajectory Methods
- Noise Contributors in Image Restoration
- The Relative Immunity to Noise of the Auxiliary System
- Numerical Examples
- Summary and Conclusions
- References
- Fast Algorithms for p-elastica Energy with the Application to Image Inpainting and Curve Reconstruction
- Introduction
- Review of ALM for Euler's Elastica Model
- Proposed Algorithms
- Method 1
- Method 2
- Numerical Results
- Image Inpainting
- Curve Reconstruction
- Conclusion
- References
- The Beltrami-Mumford-Shah Functional
- Introduction
- Inhomogeneous Diffusion
- Isotropic Diffusion
- Inhomogeneous Diffusion
- TV and MAP
- The Mumford-Shah Functional
- The Ambrosio-Tortorelli Functional
- The Beltrami Framework
- The Beltrami-Mumford-Shah Functional
- Results
- Numerical Implementation
- Results
- Summary and Conclusions
- References
- An Adaptive Norm Algorithm for Image Restoration
- Introduction
- Description of the HQ-Algorithm for L1-TV Regularization
- The Adaptive Norm Algorithm (ANA)
- The Coherence Matrix Construction
- Experiments and Results
- Conclusions
- References
- Variational Image Denoising with Adaptive Constraint Sets
- Introduction
- Problem
- Approach
- A Quasi-Variational Inequality
- Existence of Solutions
- Algorithm
- Adaptive Anisotropic TV Minimization for Image Denoising
- Anisotropic TV with a Single Direction
- Anisotropic TV with Double Directions
- Anisotropic Spatio-temporal TV Minimization
- Experiments
- Anisotropic TV Minimization with Double Directions
- Adaptive Motion-Based TV Minimization for Image Sequences
- Conclusion
- References
- Simultaneous Denoising and Illumination Correction via Local Data-Fidelity and Nonlocal Regularization
- Introduction
- The Proposed Model
- Some Model Assumptions
- The Local Fidelity Term
- Nonlocal TV
- The Proposed Cost Functional
- Algorithm: Augmented Lagrangian Method and EM
- Numerical Experiments
- Parameters and Initial Values Selection
- Experimental Results
- Conclusion
- References
- Anisotropic Non-Local Means with Spatially Adaptive Patch Shapes
- Introduction
- An Overview of the NLM
- From Patches to Shapes: Beyond the Rare Patch Effect
- Aggregation of Shape-Based Estimates
- Classical Methods
- Uniformly weighted aggregation (UWA).
- Variance-based decision, Weighted Average (WAV).
- SURE-Based Methods
- Minimizer of the risk estimates (MRE).
- Exponentially Weighted Aggregation (EWA).
- Regularizing the Risk Maps with Anisotropic Diffusion
- Numerical and Visual Results
- Conclusion
- References
- Part II: Segmentation (O2)
- Entropy-Scale Profiles for Texture Segmentation
- Introduction
- Methodology
- Image Model
- Image Decomposition
- Entropy Profile
- Texture Segmentation
- Experimental Results
- Conclusion
- References
- Non-local Active Contours
- Introduction
- Non-local Active Contours
- Pairwise Patch Interaction
- Pairwise Interaction Energy
- Non-local Active Contour Energy
- Experimental Results and Comparisons
- Hybrid Region/Edge Based Active Contours
- Gray-Level and Color Features
- Gabor Features
- Conclusion
- References
- From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree
- Introduction
- A Modified Energy and Its Minimizer
- Randomized Hierarchy Tree
- Experimental Results and Discussion
- References
- A Continuous Max-Flow Approach to Minimal Partitions with Label Cost Prior
- Introduction
- Previous Works
- Convex Relaxation Approaches
- Convex Relaxed MDL Approach
- Continuous Max-Flow Approach
- Continuous Max-Flow Formulation
- Equivalent Primal-Dual Model
- Equivalent Dual Model
- Fast Continuous Max-Flow Algorithm
- Numerical Experiments
- Conclusions
- References
- Robust Edge Detection Using Mumford-Shah Model and Binary Level Set Method
- Introduction
- Formulation of the Model
- Description of the Algorithm
- Numerical Experiments
- Concluding Remarks
- References
- Bifurcation of Segment Edge Curves in Scale Space
- Introduction
- Gaussian Scale Space
- Segmentation Using Second-Order Derivatives
- Scale Space Hierarchy
- Segment Edge Curve Bifurcation
- Conclusions
- References
- Efficient Minimization of the Non-local Potts Model
- Efficient Minimization of the Non-Local Potts Model.
- Introduction
- Non-local Potts Model
- Minimization
- Applications
- Multi-label Segmentation
- Stereo
- Conclusion
- References
- Sulci Detection in Photos of the Human Cortex Based on Learned Discriminative Dictionaries
- Introduction
- Learning Discriminative Dictionaries
- Minimization Algorithm
- Segmentation with Discriminative Dictionaries
- Results
- Conclusion
- References
- An Efficient and Effective Toolfor Image Segmentation, Total Variations and Regularization
- Introduction
- Relationship to Continuous Models
- The Methodology
- Experimental Results
- Denoising by Modifying the Ratio between the Separation and Deviation Penalties
- Increasing Deviation for a Selected Color
- Comparison of Image Segmentation with Separation-deviation to the Normalized Cut Approach
- Conclusions
- References
- Supervised Scale-Invariant Segmentation (and Detection)
- Introduction
- Work's Novelty and Related Methods
- Remark and Outline
- Scale Space Theory and Pixel-Based Segmentation
- Scale Space and Gaussian Derivatives
- Supervised Pixel Classification
- Supervised Scale-Invariant Segmentation
- Additional Remarks
- Illustrative Experiments
- Classifiers and Features
- Shapes
- Textures
- Discussion and Conclusion
- References
- A Geodesic Voting Shape Prior to Constrain the Level Set Evolution for the Segmentation of Tubular Trees
- Introduction
- Background
- Minimal Paths
- Geodesic Voting for Segmentation of Tree Structures
- Active Contours without Edges
- From the Voting Tree to the Tubular Tree
- Results and Discussion
- Conclusion
- References
- Amoeba Active Contours
- Introduction
- Amoeba Active Contour Filtering
- Space-Continuous Analysis
- Experiments
- Conclusion
- References
- A Hybrid Scheme for Contour Detectionand Completion Based on Topological Gradient and Fast Marching Algorithms -Application to Inpainting and Segmentation
- Introduction
- A 2D Algorithm Based on the Minimal Paths and Fast Marching Methods
- Minimal Paths
- Multiple Minimal Paths
- Main Algorithm
- Numerical Experiments
- Numerical Results for 2D Segmentation
- Numerical Results for a New Way of 2D Inpainting
- Conclusions and Perspectives
- References
- A Segmentation Quality Measure Based on Rich Descriptors and Classification Methods
- Introduction
- Segment Score
- Feature Generation
- Feature Generation Using a Discriminative Online Classifier.
- Junctions-based features.
- Directly Estimated Segment Score
- Enhanced, Indirect Estimation of the Segment Score
- Segment Search
- Selecting a Base Hypothesis
- Generating New Hypotheses
- Experiments
- Data and Implementation Details
- Score Validity
- Segmentation Results
- Discussion
- References
- Framelet-Based Algorithm for Segmentation of Tubular Structures
- Introduction
- Framelet-Based Algorithm
- Framelet-Based Algorithm for Segmentation
- Initializing and Refining the Range
- Numerical Examples
- Conclusions and Future Work
- References
- Weakly Convex Coupling Continuous Cuts and Shape Priors
- Introduction
- Overview, Related Work
- Contribution, Organization
- Variational Models
- Segmentation by Continuous Cuts
- MRF Based Shape Priors
- Variational Shape Priors
- Ising Shape Prior
- Hierarchical Part Based Shape Prior
- Coupling Convex Models
- Variational Approach
- Shape Constrained Cuts
- Experiments and Discussion
- Setup
- Results
- Conclusions and Further Work
- References
- Part III: Image Representation and Invariants (O3)
- Wasserstein Barycenter and Its Application to Texture Mixing
- Introduction
- Previous Work on Texture Synthesis and Mixing
- Contributions
- Wasserstein Distance and Its Approximation
- Wasserstein Distance
- Sliced Wasserstein Distance
- Barycenter in Wasserstein Space
- Wasserstein Barycenter
- Gradient Descent Algorithm
- Computing the Projection on a Distribution
- Texture Synthesis and Mixing
- Multiscale Oriented Decompositions
- First Order Statistical Mixing
- Higher Order Statistical Mixing
- Conclusion
- References
- Theoretical Foundations of Gaussian Convolution by Extended Box Filtering
- Introduction
- Conventional Box Filtering
- Extended Box Filter
- Experiments
- Qualitative Gain
- Runtime
- Summary
- References
- From High Definition Image to Low Space Optimization
- Introduction
- Coresets for Dictionaries
- k-Dictionary Queries
- Coreset for a Single k-Dictionary Query
- Coreset for all k-Dictionary Queries
- Example Application: Approximating the Optimal Dictionary
- The k-Dictionary Problem
- Coreset for the k-Dictionary Problem
- Experimental Results
- Synthetic Data
- Coresets for High-Definition Images
- Conclusions and Further Work
- References
- Measuring Ge odesic Distancesvia the Uniformization Theorem
- Introduction
- Introduction to Conformal Mapping
- Construction of a Discrete Harmonic Map
- Fast Marching on the Conformal Map
- Controlling the Conformal Factor
- Bounding the Conformal Factor
- Conclusions
- References
- Polyakov Action on (?,G)-Equivariant Functions Application to Color Image Regularization
- Introduction
- Color Images as (,G)-Equivariant Functions on Principal Bundles
- The General Construction
- Interpretation of the (,G)-Equivariance for Color Images
- The Group (I-R+,).
- The Group DC(3).
- The Group SO(3).
- Minimization of the Polyakov Action Related to the Graph of (,G)-Equivariant Functions: A Case Study
- The Case (,G) Is the Natural Representation of (I-R+,) on I-R3
- Riemannian Geometry of (I-R+,).
- The Induced Metric h.
- Minimization with Respect to the Embedding .
- The Case (,G) Is the Trivial Representation of (I-R+,) on I-R3
- The Induced Metric h.
- Minimization with Respect to the Embedding .
- The Case (,G) Is the Natural Representation of DC(3) on I-R3
- Riemannian Geometry of DC(3).
- The Induced Metric h.
- Minimization with Respect to the Embedding .
- The Case (,G) Is the Natural Representation of SO(3) on I-R3
- Riemannian Geometry of SO(3).
- The Induced Metric h.
- Minimization with Respect to the Embedding .
- Experiments
- Conclusion
- References
- Curvature Minimization for Surface Reconstruction with Features
- Introduction
- An Overview of the Proposed Method
- Global Minimization for Surface Reconstruction via Graph Cuts
- Feature Sensitive Local Minimization
- Numerical Experiments
- Acknowledgement.
- References
- Should We Search for a Global Minimizerof Least Squares Regularized with an $\ell$0 Penalty to Get the Exact Solution of an under Determined Linear System?
- Introduction
- Notations and Definitions
- Content of the Paper
- Local Minimizers
- Minimizers of Fd Solve Linear Programming Problems
- Strict Minimizers
- Necessary and Sufficient Conditions
- Stability of Strict (local) Minimizers
- Global Minimizers
- Exact Recovery
- Originals with an M-Length Support
- An Assumption on A That Holds for a.e. A
- Necessary and Sufficient Conditions
- Numerical Toy-Example
- Concluding Remarks
- References
- Weak Statistical Constraints for Variational Stereo Imaging of Oceanic Waves
- Introduction
- The Variational Framework
- Multi-Image Setup and Graph Surface Representation
- Proposed Energy Functional
- Energy Minimization. Optimality Condition
- Numerical Solution.
- Weak Enforcement of Wave Height Distributions
- Applications
- Conclusion
- References
- Novel Schemes for Hyperbolic PDEs Using Osmosis Filters from Visual Computing
- Introduction
- Diffusion Filters and Osmosis
- Osmosis Schemes for HDEs
- Numerical Experiments
- Conclusion
- References
- Fast PDE-Based Image Analysis in Your Pocket
- Introduction
- Models and Solvers
- Diffusion Filtering
- Variational Optic Flow
- Implementation on an Android Phone
- Android Basics
- Image Processing with Android
- Retrieval of Camera Data.
- YUV Conversion.
- Software Design
- Optimisations
- Experiments
- Our Interactive Camera Applications
- Performance Analysis
- Linear Diffusion.
- Nonlinear Isotropic Diffusion.
- Conclusions and Outlook
- References
- A Sampling Theorem for a 2D Surface
- Introduction
- Geometric Recovery of Surface Coordinates
- Geometrical Representation
- Mathematical Model
- Sampling Rate Determination
- Experimental Results
- Conclusion and Future Works
- References
- Quadrature Nodes Meet Stippling Dots
- Introduction
- Quadrature Errors in RKHSs
- Discrepancies
- Least Squares Functionals for Bandlimited Functions
- Efficient Minimization Algorithm on S2
- Numerical Results on S2
- Conclusions
- References
- Part IV: Shape Analysis (O4)
- Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching
- Introduction
- Problem Formulation
- Invariance
- Choice of the Metric
- Choice of the Descriptor
- Correspondence as a Graph Labeling Problem
- Hierarchical Matching
- Probabilistic Matching and Shape Prototypes
- Results
- Conclusions
- References
- A Correspondence-Less Approach to Matching of Deformable Shapes
- Introduction
- Background
- Partial Matching
- Discretization and Numerical Aspects
- Results
- Conclusions
- References
- Hierarchical Matching of Non-rigid Shapes
- Introduction
- Non-Rigid Correspondence in a Brief
- Problem Formulation
- Mathematical Background
- Choice of Metric
- Choice of Descriptors
- Integer Quadratic Programming
- Hierarchical Formulation
- Results
- Conclusions
- References
- Photometric Heat Kernel Signatures
- Introduction
- Background
- Photometric Heat Kernel Signatures
- Numerical Implementation
- Results
- Conclusions
- References
- Human Activity Modeling as Brownian Motion on Shape Manifold
- Introduction
- Background
- A Infinite Dimensional Shape Manifold
- Connection on Manifold
- Dynamics of Human Activity on a Shape Manifold
- Flat Connection on a Shape Manifold
- Stochastic Analysis in a Euclidean Space
- Human Activity as a Brownian Motion on Manifold
- Human Activity as a Piecewise Brownian Motion on Manifold
- Conclusion
- References
- 3D Curve Evolution Algorithm with Tangential Redistribution for a Fully Automatic Findingof an Ideal Camera Path in Virtual Colonoscopy
- Introduction
- The Colon Segmentation and the Initial Trajectory Guess
- Finding the Optimal Camera Trajectory
- References
- Distance Images and Intermediate-Level Vision
- Introduction
- Global Distance Information Signaled Locally
- Mathematical Formulation
- Edge Producing Model
- Density Scale Space
- Diffusion Map Embedding
- Summary and a Psychophysical View
- Acknowledgments.
- References
- Shape Palindromes: Analysis of Intrinsic Symmetries in 2D Articulated Shapes
- Introduction
- Model
- Symmetry Analysis
- Extrinsic Symmetry Characterization
- Intrinsic Symmetry Characterization
- Numerical Implementation
- Fourier Analysis
- Dynamic Programming
- Computational Complexity
- Results
- Conclusions
- References
- Kernel Bundle EPDiff: Evolution Equations for Multi-scale Diffeomorphic Image Registratio n
- Introduction
- Content and Outline
- The LDDMM Framework
- Kernel, Momentum and LDDKBM
- Kernel and Momentum
- The Kernel Bundle and LDDKBM
- EPDiff and KB-EPDiff
- Euler-Lagrange Equations
- Scale Conservation and KB-EPDiff
- KB-EPDiff for Landmarks: An Example
- Experiments
- Hand Outlines
- KB-EPDiff across Scales
- Conclusion
- References
- Deformable Shape Retrieval by Learning Diffusion Kernels
- Introduction
- Background
- Diffusion Geometry
- Heat Diffusion
- Diffusion Distances
- Invariance
- Distance Distributions
- Optimal Diffusion Kernels
- Discretization
- Interpolation Operators
- Results
- Conclusions
- References
- Part V: Optical Flow (O6)
- Stochastic Models for Local Optical Flow Estimation
- Introduction
- Stochastic Luminance Function and Conservation Constraints
- Notations - Conventions
- Stochastic Luminance Function
- Isotropic Uncertainties.
- Anisotropic Uncertainties.
- Uncertainty Models for Luminance Conservation
- Uncertainty Estimation
- Estimation of
- Estimation of
- Application of the Proposed Luminance Models
- Experimental Results
- Conclusion
- Expectation of a Function of a Stochastic Process
- References
- Optic Flow Scale Space
- Introduction
- Variational Optic Flow as Whittaker-Tikhonov Regularisation
- Towards Regularisation in a Spatially Varying Norm
- Analysing the Matrix-Weighted Norm
- Generalisation of the Matrix-Weighted Norm
- Optic Flow Scale Space
- Numerical Realisation
- Scale Selection
- Experiments
- Conclusion
- References
- Group-Valued Regularization Framework for Motion Segmentation of Dynamic Non-rigid Shapes
- Introduction
- Problem Formulation
- Diffusion-Based Regularization
- Numerical Considerations
- Initial Correspondence Estimation
- Diffusion of Lie Group Elements
- A Patch-Based Data Term
- Visualizing Lie Group Clustering on Surfaces
- Results
- Conclusion
- References
- Wavelet-Based Fluid Motion Estimation
- Introduction
- Optical Flow Background
- Non-linear Data Model
- Classical Multi-resolution Strategy
- The Aperture Problem and Usual Regularization Schemes
- Wavelet Formulation
- Wavelet Decomposition
- Wavelet Data Term
- Multiscale Estimation
- Regularizations
- Wavelet Properties
- Polynomial Approximation on a Truncated Basis
- High-Order Regularization
- Results
- Synthetic PIV Sequence
- Real PIV Sequence
- Conclusion
- References
- Multiscale Weighted Ensemble Kalman Filter for Fluid Flow Estimation
- Introduction
- Stochastic Lucas-Kanade Estimator
- Luminance Variation with Uncertainties
- Data Model with Uncertainties and Local Estimation
- Multiresolution Analysis and Uncertainty Estimation
- Monte Carlo Implementation of Stochastic Filtering with the Weighted-Ensemble Kalman Filter
- Stochastic Filtering, Filtering Distribution
- Linear Gaussian Models and the Kalman Filter
- Particle Implementation of the Nonlinear Filtering
- Ensemble Kalman Filtering
- Weighted EnKF
- WEnKF Assimilation of SLK Observations
- Multiscale SLK-WEnKF Filtering
- Experimental Results and Comparisons
- Conclusion
- References
- Over-Parameterized Optical Flow Using a Stereoscopic Constraint
- Introduction
- Background
- The Variational Framework
- Epipolar Geometry
- Estimation of the Fundamental Matrix
- A Flow Model Based on Local Homographies
- Euler-Lagrange Equations
- Minimization with respect to ai.
- Minimization with respect to vAT.
- Implementation
- Experimental Results
- Conclusions
- References
- Robust Optic-Flow Estimation with Bayesian Inference of Model and Hyper-parameters
- Introduction
- Short Overview of Optic-Flow Estimation
- Data and Prior Models
- Standard Optic-Flow Estimation
- A Bayesian Framework for Model and Hyper-parameter Selection
- Bayesian Formulation of the Optic-Flow Estimation Problem
- Bayesian Inference for Robust Optic-Flow Estimation
- Experiments
- Fluid Motion Image Sequence
- Computer Vision Scenes
- Conclusion
- References
- Regularization of Positive Definite Matrix Fields Based on Multiplicative Calculus
- Introduction
- Theory
- Multiplicative Differentiation
- Multiplicative Integration
- Linear Functions and Linear Mappings
- Taylor Expansions
- Critical Points
- Differential Equations
- The Non-commutative Case
- Multiscale Representation of Positive Definite Matrix Fields
- Conclusion and Discussion
- References
- Author Index
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.