Dr. Jun Li Profile

Dr. Jun Li

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 | 14 August 2019 Paper

An improved self-supervised framework for feature point detection

Yunhui Wu, Jun li

Proceedings Volume 11179, 1117927 (2019) https://doi.org/10.1117/12.2540147

KEYWORDS: Detection and tracking algorithms, Convolution, Convolutional neural networks, Image retrieval

Read Abstract +

The robust image feature point is a critical component of image matching. In order to detect feature points that are robust to illumination changes and viewpoint changes, an improved self-supervised learning framework for feature point detector is proposed. Firstly, feature point detector is trained in simple synthetic datasets. Then, the labeled dataset is generated by applying the Homographic Adaptation to automatically label the unlabeled area image. Finally, the full convolution network is trained with the labeled dataset. In this paper, the convolutional neural network in the selfsupervised learning framework is improved, mainly by increasing the number of layers of the neural network, from the original 8 layers to 11 layers. Experiments on the HPatches dataset show that the improved self-supervised feature point detector has achieved good results

Proceedings Article | 21 June 2019 Paper

Automatic and accurate full-view registration method for 3D scanning system

Pei Xu, Feifei Gu, Zhan Song, Juan Zhao, Jun Li

Proceedings Volume 11056, 110563M (2019) https://doi.org/10.1117/12.2525655

KEYWORDS: Clouds, 3D scanning, Structured light, 3D image processing, Cameras, Image registration, Feature extraction, 3D modeling, Calibration, Reconstruction algorithms

Read Abstract +

In a structured light-based 3D scanning system, the overall 3D information of to-be-measured objects cannot be retrieved at one time automatically. Currently the 3D registration algorithms can be divided into the auxiliary objects-based method and the feature points-based method. The former requires extra calibration objects or positioning platforms, which limits its application in free-form 3D scanning task. The latter can be conducted automatically, however, most of them tried to recover the motion matrix from extracted 2D features, which has been proved to be inaccurate. This paper proposed an automatic and accurate full-view registration method for 3D scanning system. Instead of using the 3D information of detected feature points to estimate the coarse motion matrix, 3D points reconstructed by the 3D scanning system were utilized. Firstly, robust SIFT features were extracted from each image and corresponding matching point pairs are achieved from two adjacent left images. Secondly, re-project all of the 3D point clouds onto the image plane of each left camera and corresponding 2D image points can be obtained. Filter out correct matching points from all 2D reprojection points under the guidance of the extracted SIFT matching points. Then, the covariance method was adopted to estimate the coarse registration matrix of adjacent positions. This procedure was repeated among every adjacent viewing position of the 3D scanning system. Lastly, fast ICP algorithm was performed to conduct fine registration of multi-view point clouds. Experiments conducted on real data have verified the effectiveness and accuracy of the proposed method.

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