| 書目名稱 | Ridges in Image and Data Analysis |
| 編輯 | David Eberly |
| 視頻video | http://file.papertrans.cn/831/830282/830282.mp4 |
| 叢書名稱 | Computational Imaging and Vision |
| 圖書封面 |  |
| 描述 | The concept of ridges has appeared numerous times in the image processing liter- ature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific ap- plications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior by considering maxima or minima of the data. These critical points are relatively easy to compute. Numerical packages always provide support for root finding or optimization, whether it be through bisection, Newton‘s method, conjugate gradient method, or other standard methods. It has not been natural for scientists to consider critical behavior in a higher-order sense. The con- cept of ridge as a manifold of critical points is a natural extension of the concept of local maximum as an isolated critical point. However, almost no attention has been given to formalizing the concept. There is a need for a formal development. There is a need for understanding the computation issues that arise in the imple- mentations. The purpose of this book is to address both needs by providing a formal mathematical foundation and a computational fr |
| 出版日期 | Book 1996 |
| 關(guān)鍵詞 | Riemannian geometry; computer vision; data analysis; image analysis; image processing; manifold; modeling |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-94-015-8765-5 |
| isbn_softcover | 978-90-481-4761-8 |
| isbn_ebook | 978-94-015-8765-5Series ISSN 1381-6446 |
| issn_series | 1381-6446 |
| copyright | Springer Science+Business Media Dordrecht 1996 |
|Archiver|手機(jī)版|小黑屋|
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