Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection of Human Nudes
International Journal of Computer Vision - 1998 Marr Prize
Probabilistic Methods for Finding People
International Journal of Computer Vision
3-D Human Modeling and Animation
3-D Human Modeling and Animation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Augmented Privacy with Virtual Humans
Digital Human Modeling
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Interactive Visualization of Network Anomalous Events
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Hi-index | 0.01 |
In this paper, we explore a privacy algorithm that detects human private parts in a 3D scan data set. The analogia graph is introduced to study the proportion of structures. The intrinsic human proportions are applied to reduce the search space in an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression and the relative measurements of the height and area factors. The method is tested on 100 data sets from CAESAR database. Two surface rendering methods are studied for data privacy: blurring and transparency. It is found that test subjects normally prefer to have the most possible privacy in both rendering methods. However, the subjects adjusted their privacy measurement to a certain degree as they were informed of the context of security.