Abstract book (pdf) - ICPR 2010

ThAT8 Upper Foyer
Image Analysis; Scene Understanding; Shape Modeling; Tracking and Surveillance; Vision Sensors
Poster Session
Session chair: Gimel’farb, Georgy (Univ. of Auckland)
09:00-11:10, Paper ThAT8.2
Sparse Embedding Visual Attention Systems Combined with Edge Information
Zhao, Cairong, Nanjing Univ. of Science and Tech.
Liu, ChuanCai, Nanjing Univ. of Science and Tech.
Lai, Zhihui, Nanjing Univ. of Science and Tech.
Yang, Jingyu, Nanjing Univ. of Science and Tech.
The general computational models of visual attention are to obtain multi-scale feature maps in terms of visual properties
like intensity, color and orientation, and then combine them to get one saliency map. But due to the lack of object edge information
and reasonable feature combination strategy, the visual saliency map of the image is a blur map. Being aware
of these, we propose a new scheme for saliency extraction. In this paper, we firstly put forward a sparse embedding feature
combination strategy, inspired by sparse representation. The strategy is used to combine the salient regions from the individual
feature maps based on a novel feature sparse indicator that measures the contribution of each map to saliency. Then
we combine traditional visual attention with edge information. Results on different scene images show that our method
outperforms other traditional feature combination strategies.
09:00-11:10, Paper ThAT8.4
LLN-Based Model-Driven Validation of Data Points for Random Sample Consensus Methods
Zhang, Liang, Communications Res. Centre Canada
Wang, Demin, Communications Res. Center Canada
This paper presents an on-the-fly model-driven validation of data points for random sample consensus methods (RANSAC).
The novelty resides in the idea that an analysis of the outcomes of previous random model samplings can benefit subsequent
samplings. Given a sequence of successful model samplings, information from the inlier sets and the model errors is used
to provide a validness of a data point. This validness is used to guide subsequent model samplings, so that the data point
with a higher validness has more chance to be selected. To evaluate the performance, the proposed method is applied to
the problem of the line model fitting and the estimation of the fundamental matrix. Experimental results confirm that the
proposed algorithm improves the performance of RANSAC in terms of the estimate accuracy and the number of samplings.
09:00-11:10, Paper ThAT8.5
Estimating 3D Human Pose from Single Images using Iterative Refinement of the Prior
Daubney, Ben Christopher, Swansea Univ.
Xie, Xianghua, Swansea Univ.
This paper proposes a generative method to extract 3D human pose using just a single image. Unlike many existing approaches
we assume that accurate foreground background segmentation is not possible and do not use binary silhouettes.
A stochastic method is used to search the pose space and the posterior distribution is maximized using Expectation Maximization
(EM). It is assumed that some knowledge is known a priori about the position, scale and orientation of the person
present and we specifically develop an approach to exploit this. The result is that we can learn a more constrained prior
without having to sacrifice its generality to a specific action type. A single prior is learnt using all actions in the Human
Eva dataset [9] and we provide quantitative results for images selected across all action categories and subjects, captured
from differing viewpoints.
09:00-11:10, Paper ThAT8.6
Human-Area Segmentation by Selecting Similar Silhouette Images based on Weak-Classifier Response
Ando, Hiroaki, Chubu Univ.
Fujiyoshi, Hironobu, Chubu Univ.
Human-area segmentation is a major issue in video surveillance. Many existing methods estimate individual human areas
from the foreground area obtained by background subtraction, but the effects of camera movement can make it difficult
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