Motion Segmentation From Tracked Features

Luca Zappella

ISBN 10: 3844300600 / ISBN 13: 9783844300604

Published by LAP LAMBERT Academic Publishing Jan 2011, 2011

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Motion segmentation is an essential process for many computer vision algorithms. During the last decade, a large amount of works has been trying to tackle this challenging problem, however, performances of most of them still fall far behind human perception. In this work the motion segmentation problem is studied, analyzing and reviewing the most important and newest techniques. A classification of all these techniques into different categories according to their main principle and features is proposed, and their strengths and weaknesses are pointed out. One of the most promising technique emerged from the review, the Local Subspace Affinity, is studied in its details highlighting and commenting its critical points. Some improvements to this technique are proposed and validated with tests on synthetic and real sequences. Finally further research directions are suggested.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch.

Seller Inventory # 9783844300604

Bibliographic Details

• Title: Motion Segmentation From Tracked Features
• Publisher: LAP LAMBERT Academic Publishing Jan 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3844300600
• ISBN 13: 9783844300604
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 107 grams

About this title

Synopsis

Motion segmentation is an essential process for many computer vision algorithms. During the last decade, a large amount of works has been trying to tackle this challenging problem, however, performances of most of them still fall far behind human perception. In this work the motion segmentation problem is studied, analyzing and reviewing the most important and newest techniques. A classification of all these techniques into different categories according to their main principle and features is proposed, and their strengths and weaknesses are pointed out. One of the most promising technique emerged from the review, the Local Subspace Affinity, is studied in its details highlighting and commenting its critical points. Some improvements to this technique are proposed and validated with tests on synthetic and real sequences. Finally further research directions are suggested.

About the Author

L. Zappella is a PhD candidate at the University of Girona (Spain). X. Lladó is lecturer at the University of Girona (Spain). J. Salvi is professor at University of Girona (Spain). Authors' research interests are in the field of image processing, computer vision, motion analysis, structure from motion and 3D reconstruction.