Welcome to the InfoSci Platform
Reference Hub9
Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation

Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation

Irene Yu-Hua Gu, Vasile Gui
Copyright: © 2006 |Pages: 27
ISBN13: 9781591407539|ISBN10: 1591407532|EISBN13: 9781591407553
DOI: 10.4018/978-1-59140-753-9.ch006
Cite Chapter Cite Chapter

MLA

Gu, Irene Yu-Hua, and Vasile Gui. "Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation." Advances in Image and Video Segmentation, edited by Yu-Jin Zhang, IGI Global, 2006, pp. 113-139. https://doi.org/10.4018/978-1-59140-753-9.ch006

APA

Gu, I. Y. & Gui, V. (2006). Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation. In Y. Zhang (Ed.), Advances in Image and Video Segmentation (pp. 113-139). IGI Global. https://doi.org/10.4018/978-1-59140-753-9.ch006

Chicago

Gu, Irene Yu-Hua, and Vasile Gui. "Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation." In Advances in Image and Video Segmentation, edited by Yu-Jin Zhang, 113-139. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-753-9.ch006

Export Reference

Mendeley
Favorite

Abstract

This chapter addresses image and video segmentation by using mean shift-based filtering and segmentation. Mean shift is an effective and elegant method to directly seek the local modes (or, local maxima) of the probability density function without the requirement of actually estimating it. Mean shift is proportional to the normalized density gradient estimate, and is pointing to the local stationary point (or, local mode) of the density estimate at which it converges. A mean shift filter can be related to a domain filter, a range filter or a bilateral filter depending on the variable setting in the kernel, and also has its own strength due to its flexibility and statistical basis. In this chapter a variety of mean shift filtering approaches are described for image/video segmentation and nonlinear edge-preserving image smoothing. A joint space-time-range domain mean shift-based video segmentation approach is presented. Segmentation of moving/static objects/background is obtained through inter-frame mode-matching in consecutive frames and motion vector mode estimation. Newly appearing objects/regions in the current frame due to new foreground objects or uncovered background regions are segmented by intra-frame mode estimation. Examples of image/video segmentation are included to demonstrate the effectiveness and robustness of these methods. Pseudo codes of the algorithms are also included.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.