Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.48
Accurate Dense Stereo by Constraining Local Consistency on Superpixels
Mattoccia, Stefano, Univ. of Bologna
Segmentation is a low-level vision cue often deployed by stereo algorithms to assume that disparity within superpixels
varies smoothly. In this paper, we show that constraining, on a superpixel basis, the cues provided by a recently proposed
technique, which explicitly models local consistency among neighboring points, yields accurate and dense disparity fields.
Our proposal, starting from the initial disparity hypotheses of a fast dense stereo algorithm based on scan line optimization,
demonstrates its effectiveness by enabling us to obtain results comparable to top-ranked algorithms based on iterative disparity
optimization methods.
13:30-16:30, Paper TuBCT8.49
On-Line Structure and Motion Estimation based on an Novel Parameterized Extended Kalman Filter
Haner, Sebastian, Lund Univ. of Tech.
Heyden, Anders, Lund Univ.
Estimation of structure and motion in computer vision systems can be performed using a dynamic systems approach,
where states and parameters in a perspective system are estimated. We present a novel on-line method for structure and
motion estimation in densely sampled image sequences. The proposed method is based on an extended Kalman filter and
a novel parameterization. We assume calibrated cameras and derive a dynamic system describing the motion of the camera
and the image formation. By a change of coordinates, we represent this system by normalized image coordinates and the
inverse depths. Then we apply an extended Kalman filter for estimation of both structure and motion. The performance of
the proposed method is demonstrated in both simulated and real experiments. We furthermore compare our method to the
unified inverse depth parameterization and show that we achieve superior results.
13:30-16:30, Paper TuBCT8.51
Discriminant and Invariant Color Model for Tracking under Abrupt Illumination Changes
Scandaliaris, Jorge, CSIC-UPC
Sanfeliu, Alberto, Univ. Pol. De Catalunya
The output from a color imaging sensor, or apparent color, can change considerably due to illumination conditions and
scene geometry changes. In this work we take into account the dependence of apparent color with illumination an attempt
to find appropriate color models for the typical conditions found in outdoor settings. We evaluate three color based trackers,
one based on hue, another based on an intrinsic image representation and the last one based on a proposed combination of
a chromaticity model with a physically reasoned adaptation of the target model. The evaluation is done on outdoor sequences
with challenging illumination conditions, and shows that the proposed method improves the average track completeness
by over 22% over the hue-based tracker and the closeness of track by over 7% over the tracker based on the
intrinsic image representation.
13:30-16:30, Paper TuBCT8.52
Using Local Affine Invariants to Improve Image Matching
Fleck, Daniel, George Mason Univ.
Duric, Zoran, George Mason Univ.
A method to classify tentative feature matches as inliers or outliers to a transformation model is presented. It is well known
that ratios of areas of corresponding shapes are affine invariants [6]. Our algorithm uses consistency of ratios of areas in
pairs of images to classify matches as inliers or outliers. The method selects four matches within a region, and generates
all possible corresponding triangles. All matches are classified as inliers or outliers based on the variance among the ratio
of areas of the triangles. The selected inliers are used to compute a homography transformation. We present experimental
results showing significant improvements over the baseline RANSAC algorithm for pairs of images from the Zurich Building
Database.
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