Abstract book (pdf) - ICPR 2010

to obtain a background image. We have achieved human-area segmentation requiring no background image by using
chamfer matching to match the results of human detection using Real AdaBoost with silhouette images. Although accuracy
in chamfer matching drops as the number of templates increases, the proposed method enables segmentation accuracy to
be improved by selecting silhouette images similar to the matching target beforehand based on response values from weak
classifiers in Real AdaBoost.
09:00-11:10, Paper ThAT8.7
Local Optical Operators for Subpixel Scene Analysis
Jean, Yves, City Univ. of NY
In this paper we present a scene analysis technique with subpixel filtering based on dense coded light fields. Our technique
computes alignment and optically projects analysis filters to local surfaces within the extent of a camera pixel. The resolution
gain depends on the local light field density not on the point spread function of the camera optics. Abstract An initial
structured light sequence is used in establishing each camera pixel’s footprint in the projector generated light field. Then
a sequence of basis functions embedded in the light field, with camera pixel support, combine with the local surface texture
and are integrated by a camera sensor to produce a localized response at the subpixel scale. We address optical modeling
and aliasing issues since the dense light field is under sampled by the camera pixels. Results are provided with objects of
planar and non-planar topology.
09:00-11:10, Paper ThAT8.8
Aesthetic Image Classification for Autonomous Agents
Desnoyer, Mark, Carnegie Mellon Univ.
Wettergreen, David, Carnegie Mellon Univ.
Computational aesthetics is the study of applying machine learning techniques to identify aesthetically pleasing imagery.
Prior work used online datasets scraped from large user communities like Flikr to get labeled data. However, online imagery
represents results late in the media generation process, as the photographer has already framed the shot and then picked
the best results to upload. Thus, this technique can only identify quality imagery once it has been taken. In contrast, automatically
creating pleasing imagery requires understanding the imagery present earlier in the process. This paper applies
computational aesthetics techniques to a novel dataset from earlier in that process in order to understand how the problem
changes when an autonomous agent, like a robot or a real-time camera aid, creates pleasing imagery instead of simply
identifying it.
09:00-11:10, Paper ThAT8.9
Removal of Moving Objects from a Street-view Image by Fusing Multiple Image Sequences
Uchiyama, Hiroyuki, Nagoya Univ.
Deguchi, Daisuke, Nagoya Univ.
Takahashi, Tomokazu, Gifu Shotoku Gakuen Univ.
Ide, Ichiro, Nagoya Univ.
Murase, Hiroshi, Nagoya Univ.
We propose a method to remove moving objects from an in-vehicle camera image sequence by fusing multiple image sequences.
Driver assistance systems and services such as Google Street View require images containing no moving object.
The proposed scheme consists of three parts: (i) collection of many image sequences along the same route by using vehicles
equipped with an omni-directional camera, (ii) temporal and spatial registration of image sequences, and (iii) mosaicing
partial images containing no moving object. Experimental results show that 97.3% of the moving object area could be removed
by the proposed method.
09:00-11:10, Paper ThAT8.10
Improving SIFT-Based Descriptors Stability to Rotations
Bellavia, Fabio, Univ. of Palermo
Tegolo, Domenico, Univ. of Palermo
Trucco, Emanuele
Image descriptors are widely adopted structures to match image features. SIFT-based descriptors are collections of gradient
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