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Data Segmentation and Model Selection for Computer Vision: A Statistical Approach ebook

by Alireza Bab-Hadiashar,David Suter


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A Statistical Approach. Show all. Table of contents (6 chapters). price for USA in USD (gross). ISBN 978-0-387-21528-0. 2D and 3D Scene Segmentation for Robotic Vision. Robust Regression Methods and Model Selection.

Alireza Bab-Hadiashar.

Alireza Bab-Hadiashar, David Suter.

and automatic model selection, plus 2D and 3D scene segmentation.

Download Data Segmentation and Model Selection for Computer Vision: A Statistical Approach or any other file from Books category.

This paper presents an extensive survey of model selection techniques for computer vision applications. A large number of existing model selection criteria and a new model selection criterion (SSC) are introduced and their performance for two important computer vision tasks: motion estimation and range segmentation are evaluated and compared. Various factors affecting the performance of different criteria are introduced and their effects are compared by virtue of conducting controlled.

Alireza Bab-Hadiashar, David Suter, Robust Optic Flow Computation, International Journal of Computer Vision, . 9 .  ., . 9-77, Aug. 1998. We exhibit a particular blend of algorithmics and statistics whose segmentation.

A Bab-Hadiashar, D Suter Robust segmentation of visual data using ranked unbiased scale estimate. A Bab-Hadiashar, N Gheissari. IEEE transactions on image processing 15 (7), 2006.

A Bab-Hadiashar, D Suter. International Journal of Computer Vision 29 (1), 59-77, 1998. Calibration of resolver sensors in electromechanical braking systems: A modified recursive weighted least-squares approach. R Hoseinnezhad, A Bab-Hadiashar, P Harding. Robust segmentation of visual data using ranked unbiased scale estimate. A Bab-Hadiashar, D Suter. Robotica 17 (6), 649-660, 1999. An overview to visual odometry and visual SLAM: Applications to mobile robotics. K Yousif, A Bab-Hadiashar, R Hoseinnezhad.

and Model Selection for Computer Vision : A Statistical Approach .

Data Segmentation and Model Selection for Computer Vision : A Statistical Approach. The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene.

Robust adaptive-scale parametric model estimation for computer vision. Bab-Hadiashar, Alireza and Hoseinnezhad, Reza 2008. Bridging Parameter and Data Spaces for Fast Robust Estimation in Computer Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, Issue. Hesami, R. Bab-Hadiashar, A. and Gheissari, N. 2005. Large-Object Range Data Acquisition, Fusion and Segmentation. Basah, Shafriza Nisha Hoseinnezhad, Reza and Bab-Hadiashar, Alireza 2008. Limits of Motion-Background Segmentation Using Fundamental Matrix Estimation.

This edited volume explores several issues relating to parametric segmentation including robust operations, model selection criteria and automatic model selection, plus 2D and 3D scene segmentation. Emphasis is placed on robust model selection with techniques such as robust Mallows Cp, least K-th order statistical model fitting (LKS), and robust regression receiving much attention. With contributions from leading researchers, this is a valuable resource for researchers and graduated students working in computer vision, pattern recognition, image processing and robotics.
Data Segmentation and Model Selection for Computer Vision: A Statistical Approach ebook
Author:
Alireza Bab-Hadiashar,David Suter
Category:
Computer Science
Subcat:
EPUB size:
1616 kb
FB2 size:
1834 kb
DJVU size:
1893 kb
Language:
Publisher:
Springer; 2000 edition (February 28, 2000)
Pages:
208 pages
Rating:
4.8
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