Abstract book (pdf) - ICPR 2010

16:00-16:20, Paper ThCT1.2
Combining Geometry and Local Appearance for Object Detection
Pascual García-Tubío, Manuel, Vienna Univ. of Tech.
Wildenauer, Horst, Vienna Univ. of Tech.
Szumilas, Lech, Ind. Research Inst. for Automation & Measurement
In this paper we address the problem of object detection in cluttered scenes. Local image features and their spatial configuration
act as representation of object classes which are learned in a discriminative fashion. Recent contributions in the
area of object detection indicate the importance of using geometrical properties for representing object classes. Prompted
by this, we devised an approach tailored to control the importance of the features and their spatial alignment. We quantitatively
show that modeling the spatial distribution of local features and optimising the influence of both cues significantly
boosts object detection performance.
16:20-16:40, Paper ThCT1.3
Illumination and Expression Invariant Face Recognition using SSIM based Sparse Representation
Khwaja, Asim, The Australian National Univ.
Asthana, Akshay, Australian National Univ.
Goecke, Roland, Univ. of Canberra
The sparse representation technique has provided a new way of looking at object recognition. As we demonstrate in this
paper, however, the mean-squared error (MSE) measure, which is at the heart of this technique, is not a very robust measure
when it comes to comparing facial images, which differ significantly in luminance values, as it only performs pixel-bypixel
comparisons. This requires a significantly large training set with enough variations in it to offset the drawback of the
MSE measure. A large training set, however, is often not available. We propose the replacement of the MSE measure by
the structural similarity (SSIM) measure in the sparse representation algorithm, which performs a more robust comparison
using only one training sample per subject. In addition, since the off-the-shelf sparsifiers are also written using the MSE
measure, we developed our own sparsifier using genetic algorithms that use the SSIM measure. We applied the modified
algorithm to the Extended Yale Face B database as well as to the Multi-PIE database with expression and illumination
variations. The improved performance demonstrates the effectiveness of the proposed modifications.
16:40-17:00, Paper ThCT1.4
Improving Classification Accuracy by Comparing Local Features through Canonical Correlations
Dikmen, Mert, Univ. of Illinois at Urbana Champaign
Huang, Thomas, Univ. of Illinois at Urbana-Champaign
Classifying images using features extracted from densely sampled local patches has enjoyed significant success in many detection
and recognition tasks. It is also well known that generally more than one type of feature is needed to achieve robust
classification performance. Previous works using multiple features have addressed this issue either through simple concatenation
of feature vectors or through combining feature specific kernels at the classifier level. In this work we introduce a
novel approach for combining features at the feature level by projecting two types of features onto two respective subspaces
in which they are maximally correlated. We use their correlation as an augmented feature and demonstrate improvement in
classification accuracy over simple combination through concatenation in a pedestrian detection framework.
17:00-17:20, Paper ThCT1.5
A Robust Approach for Person Localization in Multi-Camera Environment
Sun, Luo, Tsinghua Univ.
Di, Huijun, Tsinghua Univ.
Tao, Linmi, Tsinghua Univ.
Xu, Guangyou, Tsinghua Univ.
Person localization is fundamental in human centered computing, since person should be localized before being actively serviced. This paper
proposed a robust approach to localize person based on the geometric constraints in multi-camera environment. The proposed algorithm has
several advantages: 1) no assumption on the positions and orientations of cameras except the cameras should have certain common field of
view; 2) no assumption on the visibility of particular body part (e.g., feet), except a portion of person should be observed in at least two views;
3) reliability in terms of tolerating occlusion, body posture change and inaccurate motion detection. It can also provide error control and be
further extended to measure person height. The efficacy of the approach is demonstrated on challenging real-world scenarios.
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