Abstract book (pdf) - ICPR 2010

Equal Error Rate (EER), and the combined verification performance of motion features and face reduces the error to 4.96%
using an adaptive score-level integration method. The more ambiguous motion-only performance is 17.1% EER.
10:20-10:40, Paper WeAT7.5
Robust Sign Language Recognition with Hierarchical Conditional Random Fields
Yang, Hee-Deok, Chosun Univ.
Lee, Seong-Whan, Korea Univ.
Sign language spotting is the task of detection and recognition of signs (words in the predefined vocabulary) and fingerspellings
(a combination of continuous alphabets that are not found in signs) in a signed utterance. The internal structures
of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously.
In this paper, a novel method for spotting signs and fingerspellings is proposed, which can distinguish signs, fingerspellings,
and nonsign patterns. This is achieved through a hierarchical framework consisting of three steps; (1) Candidate segments
of signs and fingerspellings are discriminated with a two-layer conditional random field (CRF). (2) Hand shapes of detected
signs and fingerspellings are verified by BoostMap embeddings. (3) The motions of fingerspellings are verified in order
to distinguish those which have similar hand shapes and differ only in hand trajectories. Experiments demonstrate that the
proposed method can spot signs and fingerspellings from utterance data at rates of 83% and 78%, respectively.
WeAT8 Upper Foyer
Image and Video Processing Poster Session
Session chair: Koch, Reinhard (Univ. of Kiel)
09:00-11:10, Paper WeAT8.1
Compressive Sampling Recovery for Natural Images
Shang, Fei, Beijing Inst. of Tech.
Du, Huiqian, Beijing Inst. of Tech.
Jia, Yunde, Beijing Inst. of Tech.
Compressive sampling (CS) is a novel data collection and coding theory which allows us to recover sparse or compressible
signals from a small set of measurements. This paper presents a new model for natural image recovery, in which the smooth
l0 norm and the approximate total-variation (TV) norm are adopted simultaneously. By using one-order gradient decrease,
the speed of algorithm for this new model can be guaranteed. Experimental results demonstrate that the principle of the
model is correct and the performance is as good as that based on TV model. The computing speed of the proposed method
is two orders of magnitude faster than that of interior point method and two times faster than that of the Nesta optimization
based on TV model.
09:00-11:10, Paper WeAT8.3
De-Ghosting for Image Stitching with Automatic Content-Awareness
Tang, Yu, The Univ. of Aizu
Shin, Jungpil, The Univ. of Aizu
Ghosting artifact in the field of image stitching is a common problem and the elimination of it is not an easy task. In this
paper, we propose an intuitive technique according to a stitching line based on a novel energy map which is essentially a
combination of gradient map which indicates the presence of structures and prominence map which determines the attractiveness
of a region. We consider a region is of significance only if it is both structural and attractive. Using this improved
energy map, the stitching line can easily skirt around the moving objects or salient parts based on the philosophy that
human eyes mostly notice only the salient features of an image. We compare result of our method to those of 4 state-ofthe-art
image stitching methods and it turns out that our method outperforms the 4 methods in removing ghosting artifacts.
09:00-11:10, Paper WeAT8.4
Content-Adaptive Automatic Image Sharpening
Kobayashi, Tatsuya, Nagoya City Univ.
Tajima, Johji, Nagoya City Univ.
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