Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT9.47
Comparative Analysis for Detecting Objects under Cast Shadows in Video Images
Villamizar Vergel, Michael, CSIC-UPC
Scandaliaris, Jorge, CSIC-UPC
Sanfeliu, Alberto, Univ. Pol. de Catalunya
Cast shadows add additional difficulties on detecting objects because they locally modify image intensity and color. Shadows
may appear or disappear in an image when the object, the camera, or both are free to move through a scene. This
work evaluates the performance of an object detection method based on boosted HOG paired with three different image
representations in outdoor video sequences. We follow and extend on the taxonomy from van de Sande with considerations
on the constraints assumed by each descriptor on the spatial variation of the illumination. We show that the intrinsic image
representation consistently gives the best results. This proves the usefulness of this representation for object detection in
varying illumination conditions, and supports the idea that in practice local assumptions in the descriptors can be violated.
13:30-16:30, Paper ThBCT9.48
Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation
Khalifa, Fahmi, Univ. of Louisville
El-Baz, Ayman, Univ. of Louisville
Gimel’Farb, Georgy, Univ. of Auckland
Abou El-Ghar, Mohamed, Univ. of Mansoura
A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its
background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual
appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and
a spatially invariant 2nd-order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images.
The visual appearances are described with marginal gray level distributions obtained by separating their mixture
over the image. The evolving contour interior is modeled by a 2nd-order translation and rotation invariant Markov-Gibbs
random field of object/background labels with analytically estimated potentials. Experiments with kidney CT images confirm
robustness and accuracy of the proposed approach.
13:30-16:30, Paper ThBCT9.49
Iterative Ramp Sharpening for Structure/Signature-Preserving Simplification of Images
Grazzini, Jacopo, Los Alamos National Lab.
Soille, Pierre, Ec. Joint Res. Centre
In this paper, we present a simple and heuristic ramp sharpening algorithm that achieves local contrast enhancement of
vector-valued images. The proposed algorithm performs pixel wise comparisons of intensity values, gradient strength and
directional information in order to locate transition ramps around true edges in the image. The sharpening is then applied
only for those pixels found on the ramps. This way, the contrast between objects and regions separated by a ramp is enhanced
correspondingly, avoiding ringing artifacts. It is found that applying this technique in an iterative manner on blurred
imagery produces sharpening preserving both structure and signature of the image. The final approach reaches a good
compromise between complexity and effectiveness for image simplfication, enhancing in an efficient manner the image
details and maintaining the overall image appearance.
13:30-16:30, Paper ThBCT9.50
Learning Naive Bayes Classifiers for Music Classification and Retrieval
Fu, Zhouyu, Monash Univ.
Lu, Guojun, Monash Univ.
Ting, Kai Ming, Monash Univ.
Zhang, Dengsheng, Monash Univ.
In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ
all audio features extracted from local windows for classification instead of just using a single song-level feature
vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions
of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior
performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared
to the alternative methods.
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