Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT8.15
Invisible Calibration Pattern based on Human Visual Perception Characteristics
Takimoto, Hironori, Okayama Prefectural Univ.
Yoshimori, Seiki, Nippon Bunri Univ.
Mitsukura, Yasue, Tokyo Univ. of Agriculture and Tech.
Fukumi, Minoru, The Univ. of Tokushima
In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible
data is embedded, as plural feature points corresponding to between an original image and the scanned image for normalization
of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of
contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement
and detection method of an invisible calibration pattern based on characteristics of human visual perception. The
calibration pattern is embedded to blue intensity in an original image by adding high frequency component.
13:30-16:30, Paper ThBCT8.16
Boosting Gray Codes for Red Eyes Removal
Battiato, Sebastiano, Univ. of Catania
Farinella, Giovanni Maria, Univ. of Catania
Guarnera, Mirko, ST Microelectronics
Messina, Giuseppe, ST Microelectronics
Ravì, Daniele, ST Microelectronics
Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the red-eyes artifacts
have de-facto become a critical problem. The technique herein described makes use of three main steps to identify and remove
red-eyes. First, red eyes candidates are extracted from the input image by using an image filtering pipeline. A set of
classifiers is then learned on gray code features extracted in the clustered patches space, and hence employed to distinguish
between eyes and non-eyes patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness
reduction. The proposed method has been tested on large dataset of images achieving effective results in terms of hit rates
maximization, false positives reduction and quality measure.
13:30-16:30, Paper ThBCT8.17
A New Rotation Feature for Single Tri-Axial Accelerometer based 3D Spatial Handwritten Digit Recognition
Xue, Yang, South China Univ. of Tech.
Jin, Lianwen, South China Univ. of Tech.
A new rotation feature extracted from tri-axial acceleration signals for 3D spatial handwritten digit recognition is proposed.
The feature can effectively express the clockwise and anti-clockwise direction changes of the users‘ movement while writing
in a 3D space. Based on the rotation feature, an algorithm for 3D spatial handwritten digit recognition is presented.
First, the rotation feature of the handwritten digit is extracted and coded. Then, the normalized edit distance between the
digit and class model is computed. Finally, classification is performed using Support Vector Machine (SVM). The proposed
approach outperforms time-domain features with a 22.12% accuracy improvement, peak-valley features with a 12.03%
accuracy improvement, and FFT features with a 3.24% accuracy improvement, respectively. Experimental results show
that the proposed approach is effective.
13:30-16:30, Paper ThBCT8.18
Improved Mean Shift Algorithm with Heterogeneous NodeWeights
Yoon, Ji Won, Trinity Coll. Dublin
Wilson, Simon, Trinity Coll. Dublin
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean
shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running
MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation.
The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can
obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative
way to assign weights for large size datasets and noisy datasets.
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