- We will send in 10–14 business days.
/images/books/13375948/9783838127576.jpg)
- Author: Schlemmer Michael
- Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
- Year: 2011
- Pages: 180
- ISBN-10: 3838127579
- ISBN-13: 9783838127576
- Format: 15.2 x 22.9 x 1 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Pattern Recognition for Feature-based and Comparative Visualization (e-book) (used book) | bookbook.eu
Reviews
Description
Pattern Recognition is widely spread as a basis for classification purposes. However, the common pattern recognition and classification methods are usually used on non-spatially-correlated data. For example, moment invariants are a very basic and popular tool for this purpose. Such methods cannot be easily applied on flow data, like wind streams and water flows, magma flows, blood flows for medical purposes, air flow simulations for the design of cars and airplanes. For this reason, this book explains how classic moment invariants have been transformed to facilitate pattern recognition on flow data. The properties of the "Flow Moment Invariants" are analyzed and used to perform Feature-based and Comparative Visualization of flow data.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 20d.08:16:08
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Schlemmer Michael
- Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
- Year: 2011
- Pages: 180
- ISBN-10: 3838127579
- ISBN-13: 9783838127576
- Format: 15.2 x 22.9 x 1 cm, softcover
- Language: English English
Pattern Recognition is widely spread as a basis for classification purposes. However, the common pattern recognition and classification methods are usually used on non-spatially-correlated data. For example, moment invariants are a very basic and popular tool for this purpose. Such methods cannot be easily applied on flow data, like wind streams and water flows, magma flows, blood flows for medical purposes, air flow simulations for the design of cars and airplanes. For this reason, this book explains how classic moment invariants have been transformed to facilitate pattern recognition on flow data. The properties of the "Flow Moment Invariants" are analyzed and used to perform Feature-based and Comparative Visualization of flow data.
Reviews