Abstract book (pdf) - ICPR 2010

to enhance HoG to detect walking people as well as to discriminate between single walking subject, groups of people and
vehicles with a detection rate of 100%. Furthermore, the results revealed the potential of our method to be used in visual surveillance
systems for identity tracking over different camera views.
13:30-16:30, Paper WeBCT8.57
Learning-Based Vehicle Detection using Up-Scaling Schemes and Predictive Frame Pipeline Structures
Tsai, Yi-Min, National Taiwan Univ.
Huang, Keng-Yen, National Taiwan Univ.
Tsai, Chih-Chung, National Taiwan Univ.
Chen, Liang-Gee, National Taiwan Univ.
This paper aims at detecting preceding vehicles in a variety of distance. A sub-region up-scaling scheme significantly raises
far distance detection capability. Three frame pipeline structures involving object predictors are explored to further enhance
accuracy and efficiency. It claims a 140-meter detecting distance along proposed methodology. 97.1% detection rate with
4.2% false alarm rate is achieved. At last, the benchmark of several learning-based vehicle detection approaches is provided.
13:30-16:30, Paper WeBCT8.58
Dynamic Hand Pose Recognition using Depth Data
Suryanarayan, Poonam, The Pennsylvania State Univ.
Subramanian, Anbumani, HP Lab.
Mandalapu, Dinesh, HP Lab.
Hand pose recognition has been a problem of great interest to the Computer Vision and Human Computer Interaction community
for many years and the current solutions either require additional accessories at the user end or enormous computation
time. These limitations arise mainly due to the high dexterity of human hand and occlusions created in the limited view of
the camera. This work utilizes the depth information and a novel algorithm to recognize scale and rotation invariant hand
poses dynamically. We have designed a volumetric shape descriptor enfolding the hand to generate a 3D cylindrical histogram
and achieved robust pose recognition in real time.
13:30-16:30, Paper WeBCT8.59
A Hierarchical GIST Model Embedding Multiple Biological Feasibilities for Scene Classification
Han, Yina, Xi’an Jiaotong Univ.
Liu, Guizhong, Xi’an Jiaotong Univ.
We propose a hierarchical GIST model embedding multiple biological feasibilities for scene classification. In the perceptual
layer, spatial layout of Gabor features are extracted in a bio-vision guided way: introducing diagnostic color information,
tuning the orientations and scales of Gabor filters, as well as the spacial pooling size to a biological feasible value. In the
conceptual layer, for the first time, we attempt to build a computational model for the biological conceptual GIST by kernel
PCA based prototype representation, which is specific task orientated as biological GIST, and also in accordance with the
unsupervised learning assumption in the primary visual cortex and prototype similarity based categorization in human cognition.
Using around $200$ dimensions, our model is shown to outperform existing GIST models, and to achieve state-ofthe-art
performances on four scene datasets.
13:30-16:30, Paper WeBCT8.60
Road Network Extraction using Edge Detection and Spatial Voting
Sirmacek, Beril, Deutsches Zentrum fur Luft und Raumfahrt
Unsalan, Cem, Yeditepe Univ.
Road network detection from very high resolution satellite images is important for two main reasons. First, the detection
result can be used in automated map making. Second, the detected network can be used in trajectory planning for unmanned
aerial vehicles. Although an expert can label road pixels in a given satellite image, this operation is prone to errors. Therefore,
an automated system is needed to detect the road network in a given satellite image in a robust manner. In this study, we propose
a novel approach to detect the road network from a given panchromatic Ikonos satellite image. Our method has five
main steps. First, we apply a nonlinear bilateral filtering to smooth the given image. Then, we extract Canny edges and the
gradient information as local features. Using these local features, we generate a spatial voting matrix. This voting matrix in-
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