Dr. Limin Zhang Profile

Dr. Limin Zhang

at Shenyang Institute Of Automation

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 | 12 December 2018 Paper

A novel ellipse detection method for real-time applications

Limin Zhang, Feng Zhu, Yingming Hao, Wang Pan

Proceedings Volume 10846, 1084626 (2018) https://doi.org/10.1117/12.2505381

KEYWORDS: Detection and tracking algorithms, Distance measurement, Machine vision, Computer vision technology, Digital image processing

Read Abstract +

The detection of ellipses in digital image data is an important task in vision-based systems, since elliptical shapes are very common in nature and in man-made objects. Ellipse detection in real images is technically a very challenging problem in detection effectiveness and execution time. We propose an improved ellipse detection method for real-time performance on real world images. We extract arcs from the edge mask and classify them in four classes according to edge direction and convexity. By developing arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. Our method has been tested on three real images datasets and compared with two state-of-the-art methods. Our method performs superior than the compared methods. The results also show that the proposed method is suitable for real-time applications.

Proceedings Article | 1 November 2016 Paper

An improved real-time visual tracking method for space non-cooperative target

Limin Zhang, Feng Zhu, Yingming Hao

Proceedings Volume 10157, 1015733 (2016) https://doi.org/10.1117/12.2247306

KEYWORDS: Optical tracking, 3D modeling, 3D acquisition, Statistical modeling, Visual process modeling

Read Abstract +

In order to enable the non-cooperative rendezvous, capture, and removal of large space debris, robust and fast tracking of the non-cooperative target is needed. This paper proposes an improved algorithm of real-time visual tracking for space non-cooperative target based on three-dimensional model, and it does not require any artificial markers. The non-cooperative target is assumed to be a 3D model known and constantly in the field of view of the camera mounted on the chaser. Space non-cooperative targets are regarded as less textured manmade objects, and the design documents of 3D model are available. Space appears to be black, so we can assume the object is in empty space and only the object is visible, and the background of the image is dark. Due to edge features offer a good invariance to illumination changes or image noise, our method relies on monocular vision and uses 3D-2D correspondences between the 3D model and its corresponding 2D edges in the image. The paper proposes to remove the sample points that are susceptible to false matches based on geometrical distance due to perspective projection of the 3D model. To allow a better robustness, we compare the local region similarity to get better matches between sample points and edge points. Our algorithm is proved to be efficient and shows improved accuracy without significant computational burden. The results show potential tracking performance with mean errors of < 3 degrees and < 1.5% of range.

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