Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.36
Multibody Motion Classification using the Geometry of 6 Points in 2D Images
Nordberg, Klas, Linköping Univ.
Zografos, Vasileios, Linkoping Univ.
We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to
infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames
are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their
configuration in 3D space is quantified in terms of a geometric error derived from distances between the points and 6 corresponding
lines in the image. This leads to a simple motion inconsistency score that is derived from the geometric errors of
6 points, that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial
clusters are determined in the spatial domain and merged in motion trajectory domain based on the score. Each point is then
assigned to a cluster by assigning the point to the segment of the lowest score. Our algorithm has been tested with real image
sequences from the Hopkins155 database with very good results, competing with the state of the art methods, particularly
for degenerate motion sequences. In contrast the motion segmentation methods based on multi-body factorization, that assumes
an affine camera model, the proposed method allows the mapping from the 3D space to the 2D image to be fully projective.
13:30-16:30, Paper TuBCT8.37
Reflection Removal in Colour Videos
Conte, Donatello, Univ. di Salerno
Foggia, Pasquale, Univ. di Salerno
Percannella, Gennaro, Univ. di Salerno
Tufano, Francesco, Univ. degli Studi di Salerno
Vento, Mario, Univ. degli Studi di Salerno
This paper presents a novel method for reflection removal in the context of an object detection system. The method is based
on chromatic properties of the reflections and does not require a geometric model of the objects. An experimental evaluation
of the proposed method has been performed on a large database, showing its effectiveness.
13:30-16:30, Paper TuBCT8.38
A Compound MRF Texture Model
Haindl, Michael, Inst. of Information Theory and Aut.
Havlicek, Vojtech, Inst. of Information Theory and Aut.
This paper describes a novel compound Markov random field model capable of realistic modelling of multispectral bidirectional
texture function, which is currently the most advanced representation of visual properties of surface materials. The
proposed compound Markov random field model combines a non-parametric control random field with analytically solvable
wide sense Markov representation for single regions and thus allows to avoid demanding Markov Chain Monte Carlo methods
for both parameters estimation and the compound random field synthesis.
13:30-16:30, Paper TuBCT8.39
Shape Prototype Signatures for Action Recognition
Donoser, Michael, Graz Univ. of Tech.
Riemenschneider, Hayko, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Recognizing human actions in video sequences is frequently based on analyzing the shape of the human silhouette as the
main feature. In this paper we introduce a method for recognizing different actions by comparing signatures of similarities
to pre-defined shape prototypes. In training, we build a vocabulary of shape prototypes by clustering a training set of human
silhouettes and calculate prototype similarity signatures for all training videos. During testing a prototype signature is calculated
for the test video and is aligned to each training signature by dynamic time warping. A simple voting scheme over
the similarities to the training videos provides action classification results and temporal alignments to the training videos.
Experimental evaluation on a reference data set demonstrates that state-of-the-art results are achieved.
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