Abstract: The segmentation of mouth and lips is a fundamental
problem in facial image analyisis. In this paper we propose a method
for lip segmentation based on rg-color histogram. Statistical analysis
shows, using the rg-color-space is optimal for this purpose of a pure
color based segmentation. Initially a rough adaptive threshold selects
a histogram region, that assures that all pixels in that region are
skin pixels. Based on that pixels we build a gaussian model which
represents the skin pixels distribution and is utilized to obtain a
refined, optimal threshold. We are not incorporating shape or edge
information. In experiments we show the performance of our lip pixel
segmentation method compared to the ground truth of our dataset and
a conventional watershed algorithm.
Abstract: Real-time hand tracking is a challenging task in many
computer vision applications such as gesture recognition. This paper
proposes a robust method for hand tracking in a complex environment
using Mean-shift analysis and Kalman filter in conjunction with 3D
depth map. The depth information solve the overlapping problem
between hands and face, which is obtained by passive stereo measuring
based on cross correlation and the known calibration data of
the cameras. Mean-shift analysis uses the gradient of Bhattacharyya
coefficient as a similarity function to derive the candidate of the hand
that is most similar to a given hand target model. And then, Kalman
filter is used to estimate the position of the hand target. The results
of hand tracking, tested on various video sequences, are robust to
changes in shape as well as partial occlusion.
Abstract: This paper proposes a novel stereo vision technique
for top view book scanners which provide us with dense 3d point
clouds of page surfaces. This is a precondition to dewarp bound
volumes independent of 2d information on the page. Our method is
based on algorithms, which normally require the projection of pattern
sequences with structured light. We use image sequences of the
moving stripe lighting of the top view scanner instead of an additional
light projection. Thus the stereo vision setup is simplified without
losing measurement accuracy. Furthermore we improve a surface
model dewarping method through introducing a difference vector
based on real measurements. Although our proposed method is hardly
expensive neither in calculation time nor in hardware requirements
we present good dewarping results even for difficult examples.
Abstract: In modern human computer interaction systems
(HCI), emotion recognition is becoming an imperative characteristic.
The quest for effective and reliable emotion recognition in HCI has
resulted in a need for better face detection, feature extraction and
classification. In this paper we present results of feature space analysis
after briefly explaining our fully automatic vision based emotion
recognition method. We demonstrate the compactness of the feature
space and show how the 2d/3d based method achieves superior features
for the purpose of emotion classification. Also it is exposed that
through feature normalization a widely person independent feature
space is created. As a consequence, the classifier architecture has
only a minor influence on the classification result. This is particularly
elucidated with the help of confusion matrices. For this purpose
advanced classification algorithms, such as Support Vector Machines
and Artificial Neural Networks are employed, as well as the simple k-
Nearest Neighbor classifier.