Efficient depth edge detection using structured light

Authors:
Jiyoung Park;Cheolhwon Kim;Juneho Yi;Matthew Turk
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Korea, Biometric Engineering Research Center;School of Information and Communication Engineering, Sungkyunkwan University, Korea, Biometric Engineering Research Center;School of Information and Communication Engineering, Sungkyunkwan University, Korea, Biometric Engineering Research Center;Computer Science Department, University of California, Santa Barbara, CA
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

Citing 6
Cited 1

Robust Affine Structure Matching for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Recognition of 3D Objects from Single Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularizing Phase-Based Stereo

ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging

ACM SIGGRAPH 2006 Courses
High-accuracy stereo depth maps using structured light

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Using structured light for efficient depth edge detection

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This research features a novel approach that efficiently detects depth edges in real world scenes. Depth edges play a very important role in many computer vision problems because they represent object contours. We strategically project structured light and exploit distortion of the light pattern in the structured light image along depth discontinuities to reliably detect depth edges. Distortion along depth discontinuities may not occur or be large enough to detect depending on the distance from the camera or projector. For practical application of the proposed approach, we have presented methods that guarantee the occurrence of the distortion along depth discontinuities for a continuous range of object location. Experimental results show that the proposed method accurately detects depth edges of human hand and body shapes as well as general objects.