Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
3D Complex Scenes Segmentation from a Single Range Image Using Virtual Exploration
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Direct Least Squares Fitting of Ellipses
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
Eye Center Localization Using Adaptive Templates
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Real-Time Approach for Adaptive Object Segmentation in Time-of-Flight Sensors
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Fast eye localization based on pixel differences
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On the use of depth camera for 3D phenotyping of entire plants
Computers and Electronics in Agriculture
Journal of Visual Communication and Image Representation
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This paper presents a novel approach to depth camera based single-/multi-person eye localization for human-machine interactions. Intensity and depth image frames of a single depth camera are used as system input. Foreground objects are segmented respectively from the depth image by using a novel object segmentation technique based on 2-D histogram with Otsu's method. Contour analysis with ellipse fitting is performed to locate the potential face region on the detected object. Finally, an eye localization algorithm based on a predefined eye template and geometric features is applied on the extracted facial subimages, which is a hybrid solution combining appearance and feature based eye detection methods using SVM classification to gain robustness. Our goal is to realize a low-cost and robust machine vision system which is insensitive to low spatial resolution for eye detection and tracking based applications, e.g., driver drowsiness detection, autostereoscopic display for gaming/home/office use. The experimental results of the current work with ARTTS 3-D TOF database and with our own Kinect image database demonstrate that the average eye localization rate per face is more than 92% despite of illumination change, head pose, facial expression and spectacles. The performance can be further improved with the integration of an effective tracking algorithm.