Mr. Xin Lu Profile

Xin Lu

at Univ of Tokushima

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 17 January 2005 Paper

View interpolation and synthesis by KLT feature tracker

Xin Lu

Proceedings Volume 5675, (2005) https://doi.org/10.1117/12.587792

KEYWORDS: Image segmentation, Cameras, 3D modeling, Visual process modeling, 3D image processing, Visualization, Motion models, Detection and tracking algorithms, Data modeling, Optical tracking

Read Abstract +

This paper proposes a novel method synthesizing a series of continuous and realistic intermediate views between a pair of reference images without 3D knowledge. The synthesizing procedure consists of four main steps: segmentation, image pre-warping, view morphing and image post-warping. By KLT tracker the feature correspondence between a pair of reference images can be found accurately and robustly. Therefore, the fundamental matrix constructed from the correspondence has enough dependability. Based on fundamental matrix, the scanlines number and coordinates can be computed in order to pre-warp the reference image. Morphing the pre-warped virtual image between the pair of reference images across the positions of the virtual view point, a series of continuous and realistic intermediate views can be constructed. By post-warping the interpolated views, the virtual images can be obtained. And they are very similar to the actual ones which can be seen in real environment. View morphing relies on the correspondence of feature exclusively. Since KLT tracker automatically builds the robust correspondence of feature, virtual images are synthesized more accurately in our method than in other previous methods. Experimental Result shows that the higher quality correspondence of feature causes a realistic visual effect in morphing.

Proceedings Article | 30 August 2002 Paper

Dynamic object tracking against cluttered background

Xin Lu, Shunichiro Oe, Toshiyuki Kashiwagi, Tao Lu

Proceedings Volume 4925, (2002) https://doi.org/10.1117/12.481561

KEYWORDS: Optical tracking, Detection and tracking algorithms, Imaging systems, Cameras, Intelligence systems, Dynamical systems, Head, Visualization, RGB color model, Control systems

Read Abstract +

Visual tracking could be treated as target state representation and target state inference problem in an image sequence. Moreover, in cluttered and dynamic environments the better probabilities of accurate tracking depend on richer representation and more robust inference. Target state representation could be considered as color segmentation, contour detection and position mark. Target state inference could be treated as an evaluation from old states to new one in fuzzy logic at every step of an image sequence. This paper presents a special tracking system based on factored sampling model in order to resolve difficult and complicated visual tracking problem, such as a changing of target’s representation, a clutter of environments and an interaction of target and camera. This tracking system is applied to changeful target tracking by handling the related information to sample-set between every two time-steps in an image sequence and implemented in real time system at around 20Hz with 640*480 pixels image. Specially, color and position distributions of a target have been used in this system to estimate the target situation. The results show the robust, real-time system is able to track a target with enough accuracy to automatically control the camera’s pan, tilt and zoom in order to remain the object centered in the field of vision.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years