Prof. Baocai Yin Profile

Prof. Baocai Yin

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 | 25 March 2024 Paper

A spatio-temporal-frequency features fusing based lightweight human pose estimation network

Yuechao Sun, Dehui Kong, Jinghua Li, Baocai Yin

Proceedings Volume 13089, 1308919 (2024) https://doi.org/10.1117/12.3019904

Read Abstract +

Capturing the hidden relationships in 2D pose sequences is crucial for accurate 3D human pose estimation(HPE). Recent studies have shown that frequency domain information, independent of spatio-temporal information, has strong capabilities on representing the pose sequences. However, there are few works exploring more appropriate ways to fuse these different kinds of information. In this paper, we propose an alternating cyclic approach for fusing spatio-temporal information and frequency information to achieve accurate 3D human pose estimation. The designed alternating cyclic fusion network allows for a more comprehensive integration of different features, leading to improved accuracy. By leveraging feature splitting and time-frequency convolution, the existing features are processed more appropriately, and achieving model lightweighting. Experimental results demonstrate that our approach achieves comparable accuracy to state-of-the-art methods while significantly outperforming mainstream methods in terms of model lightweighting. In conclusion, the introduction of frequency domain information is of great significance for pose estimation tasks.

Proceedings Article | 9 October 2023 Paper

Image augment self-supervised network for hand-drawn sketch 3D reconstruction

Yang Ma, Dehui Kong, Jinghua Li, Baocai Yin

Proceedings Volume 12791, 127912B (2023) https://doi.org/10.1117/12.3005096

KEYWORDS: 3D modeling, Image enhancement, Education and training, 3D image reconstruction, Feature extraction, Semantics, Modeling, Image processing, Point clouds

Read Abstract +

3D reconstruction from single hand-drawn sketches can be considered as a task of single-view reconstruction, which faces great challenge of lifting the dimension of the geometric representation of objects. Most of the reconstruction networks are based on deep learning technology with supervised training which are used to suffer from dataset labeling, while selfsupervised sketch-based 3D reconstruction remains challenging. In this paper, we propose a self-supervised 3D reconstruction network for hand-drawn sketch (IASSReNet), which introduces image information as an auxiliary to address the ambiguity and sparsity of sketch. In order to obtain image information, an image generator is firstly designed to provide augmented information for the reconstruction through a sketch feature enhancement module. To integrate the information from sketch and image, we use a spatially corresponding feature transfer module to fuse their feature. Finally, silhouettes are obtained from the predicted 3D mesh, and similarity constraints are applied to the sketch contour to perform self-supervised training on the network. Experimental results on multiple datasets show that our method outperforms other unsupervised methods and is competitive with some supervised methods.

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