1 edition of **Geometry-Driven Diffusion in Computer Vision** found in the catalog.

- 155 Want to read
- 22 Currently reading

Published
**1994**
by Springer Netherlands in Dordrecht
.

Written in English

- Functional analysis,
- Differential equations, partial,
- Computer science,
- Global differential geometry,
- Computer vision,
- Systems theory

This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations. The book is meant for computer vision scientists and students, with a computer science, mathematics or physics background. Appendices explain the terminology. Many illustrated applications are given, e.g. in medical imaging, vector valued (or coupled) diffusion, general image enhancement (e.g. edge preserving noise suppression) and modeling of the human front-end visual system. Some examples are given to implement the methods in modern computer-algebra systems. From the Preface by Jan J. Koenderink: ` I have read through the manuscript of this book in fascination. Most of the approaches that have been explored to tweak scale-space into practical tools are represented here. It is easy to appreciate how both the purist and the engineer find problems of great interest in this area. The book is certainly unique in its scope and has appeared at a time where this field is booming and newcomers can still potentially leave their imprint on the core corpus of scale related methods that still slowly emerge. As such the book is a very timely one. It is quite evident that it would be out of the question to compile anything like a textbook at this stage: this book is a snapshot of the field that manages to capture its current state very well and in a most lively fashion. I can heartily recommend its reading to anyone interested in the issues of image structure, scale and resolution. "

**Edition Notes**

Statement | edited by Bart M. Haar Romeny |

Series | Computational Imaging and Vision -- 1, Computational Imaging and Vision -- 1 |

Classifications | |
---|---|

LC Classifications | T385, TA1637-1638, TK7882.P3 |

The Physical Object | |

Format | [electronic resource] / |

Pagination | 1 online resource (xxii, 441 p.) |

Number of Pages | 441 |

ID Numbers | |

Open Library | OL27041041M |

ISBN 10 | 9048144612, 9401716994 |

ISBN 10 | 9789048144617, 9789401716994 |

OCLC/WorldCa | 851373920 |

Geometry-Driven Diffusion in Computer Vision. Book. Jan ; Bart M. ter Haar Romeny; [This book] is a jewel." - Applied Mechanics Review . A neural architecture for brightness perception is constructed in the tradition of filling-in theories. The model is developed to account for a wide v.

Niessen W J, ter Haar Romeny B M and Viergever M A Numerical analysis of geometry-driven diffusion equations Geometry-Driven Diffusion in Computer Vision (Computational Imaging and Vision) ed B M ter Haar Romeny (Dordrecht: Kluwer) pp Google Scholar. International Journal of Computer Vision, 39 () pp. N. Sochen, R. Kimmel and R. Malladi, "A General Framework for Low Level Vision", IEEE Trans. in Image Processing, Special Issue on Geometry Driven Diffusion, 7 () pp. Original Papers in Professional Journals Submitted; Y. Ferdman, C. Sagiv and N. Sochen.

Geometry-Driven Diffusion in Computer Vision. Geometry-Driven Diffusion in Computer Vision pp | Cite as. Linear Scale-Space I: Basic Theory. Authors; Authors and affiliations As Marr () phrased it in his book Vision, vision is an information processing task, in which an internal representation of information is of out-most. On Optimal Control Methods in Computer Vision and Image Processing Geometry-Driven Diffusion in Computer Vision - ; Kimia, B. Tannenbaum, A. Zucker, S. Exploring the shape manifold: the role of conservation laws Shape in Picture -

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Certainly the familiar phenomena that accompany sc ale changes in optical patterns are mentioned in the earliest written records. Geometry-Driven Diffusion in Computer Vision (Computational Imaging and Vision) th Edition by Bart M. Haar Romeny (Editor) ISBN This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial.

It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion : $ Geometry-Driven Diffusion in Computer Vision.

Editors (view affiliations) Bart M. ter Haar Romeny 3 Mentions; k Downloads; Part of the Computational Imaging and Vision book series (CIVI, volume 1) Log in to check access.

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Haar Romeny No preview available - Geometry-driven diffusion in computer vision. [Bart M ter Haar Romeny;] -- This book is a primer on geometry-driven, nonlinear diffusion as a new paradigm for vision. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints.

Computational Imaging and Vision Ser.: Geometry-Driven Diffusion in Computer Vision (, Hardcover). Geometry-Driven Diffusion in Computer Vision edited by Bart M. ter Haar Romeny This seminal book is a primer on geometry-driven, non-linear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial.

It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the varia. In computational vision we replace the mathematical derivatives by fuzzy derivatives or Gaussian Derivatives1: ∂L −→ ∂sL = ∂(L∗Gs) = L∗∂Gs Local image observations are therefore always done at a ﬁnite scale to be selected by the observer (programmer).

Figure 1: Gaussian Derivative Kernels. From top-to-bottom, left-to-right: Gs, Gs. Abstract. Computer vision deals with image understanding at various levels. At the low level, it addresses issues such us planar shape recognition and analysis.

Some classical results on differential invariants associated to planar curves are relevant to planar object recognition under different views and partial occlusion, and recent results concerning the evolution of planar shapes under curvature controlled diffusion have found applications in geometric shape decomposition.

The Bayesian Rationale for Energy Functionals, in Geometry-Driven Diffusion in Computer Vision, Bart Romeny editor, Kluwer Academic,pp. Summary: This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial.

It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations. Instructor: Prof. Ulas Bagci Class time: Tuesday/Thursday pm Class location: ENG1 Office hours: Tuesday/Thursday pm TA: @ COURSE GOALS: The course is introductory level computer vision course, suitable for graduate students.

It will cover the basic topics of computer vision, and introduce some fundamental approaches for computer vision. geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial.

It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations. The book is meant for computer vision scientists and students, with a computer science.

Journals & Books; Register Sign in. Geometry-Driven Diffusion in Computer Vision () 5. Marr. Vision () 6. R.A. HummelRepresentations based on zero-crossing in scale-space. Proceedings, IEEE Conference on Computer Vision and Pattern Recognition (), pp. Google Scholar.

CannyA computational approach to edge detection. Geometry-Driven Diffusion in Computer Vision. Book. Jan ; Bart M. ter Haar Romeny; Scale is a concept the antiquity of which can hardly be.

This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion : Paperback.

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Wolfram Science Technology-enabling science of the computational universe. References to this book. Geometry-Driven Diffusion in Computer Vision Bart M. Haar Romeny No preview available - Bibliographic information.

Title: Nonlinear Evolution Equations: Integrability and Spectral Methods Proceedings in nonlinear science: Editors: Antonio Degasperis, Allan P. Fordy, Muthusamy Lakshmanan.

Abstract This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations.

The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the.Intended for undergraduate and graduate students and all who are interested in computer vision, medical imaging, and human visual perception, this interactive book presents a tutorial approach to mastering the mathematics of computer vision and image analysis.

One purpose of the book is to bridge the gap between the world of human visual perception and the world of multi-scale computer vision.Geometry-Driven Diffusion in Computer Vision edited by Bart M.

ter Haar Romeny This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an em-phasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many.