Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT8.38
An Adaptive Method for Efficient Detection of Salient Visual Object from Color Images
Brezovan, Marius, Univ. of Craiova
Burdescu, Dumitru Dan, Univ. of Craiova
Ganea, Eugen, Univ. of Craiova
Stanescu, Liana, Univ. of Craiova
Stoica, Cosmin, Univ. of Craiova
This paper presents an efficient graph-based method to detect salient objects from color images and to extract their color
and geometric features. Despite of the majority of the segmentation methods our method is totally adaptive and it do not
require any parameter to be chosen in order to produce a better segmentation. The proposed segmentation method uses a
hexagonal structure defined on the set of the image pixels ant it performs two different steps: a pre-segmentation step that
will produce a maximum spanning tree of the connected components of the visual graph constructed on the hexagonal
structure of an image, and the final segmentation step that will produce a minimum spanning tree of the connected components,
representing the visual objects, by using dynamic weights based on the geometric features of the regions. Experimental
results are presented indicating a good performance of our method.
09:00-11:10, Paper WeAT8.39
Robust Matching in an Uncertain World
Sur, Frédéric, INPL / INRIA Nancy Grand Est
Finding point correspondences which are consistent with a geometric constraint is one of the cornerstones of many computer
vision problems. This is a difficult task because of spurious measurements leading to ambiguously matched points
and because of uncertainty in point location. In this article we address these problems and propose a new robust algorithm
that explicitly takes account of location uncertainty. We propose applications to SIFT matching and 3D data fusion.
09:00-11:10, Paper WeAT8.41
Recursive Dynamically Variable Step Search Motion Estimation Algorithm for High Definition Video
Tasdizen, Ozgur, Sabanci Univ.
Hamzaoglu, Ilker, Sabanci Univ.
For High Definition (HD) video formats, computational complexity of Full Search (FS) Motion Estimation (ME) algorithm
is prohibitively high, whereas the Peak Signal-to-Noise Ratio obtained by fast search ME algorithms is low. Therefore, in
this paper, we propose Recursive Dynamically Variable Step Search (RDVSS) ME algorithm for real-time processing of
HD video formats. RDVSS algorithm dynamically determines the search patterns that will be used for each Macro block
(MB) based on the motion vectors of its spatial and temporal neighboring MBs. RDVSS performs very close to FS by
searching much fewer search locations than FS and it outperforms successful fast search ME algorithms by searching
more search locations than these algorithms. In addition, RDVSS algorithm can be efficiently implemented by a reconfigurable
systolic array based ME hardware.
09:00-11:10, Paper WeAT8.42
Spatial and Temporal Enhancement of Depth Images Captured by a Time-of-Flight Depth Sensor
Kim, Sung-Yeol, The Unviersity of Tennessee
Cho, Ji-Ho, Gwangju Insititute of Science and Tech.
Koschan, Andreas, The Unviersity of Tennessee
Abidi, Mongi, The Unviersity of Tennessee
In this paper, we present a new method to enhance depth images captured by a time-of-flight (TOF) depth sensor spatially
and temporally. In practice, depth images obtained from TOF depth sensors have critical problems, such as optical noise
existence, unmatched boundaries, and temporal inconsistency. In this work, we improve depth quality by performing a
newly-designed joint bilateral filtering, color segmentation-based boundary refinement, and motion estimation-based temporal
consistency. Experimental results show that the proposed method significantly minimizes the inherent problems of
the depth images so that we can use them to generate a dynamic and realistic 3D scene.
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