In recent years, various gesture recognition systems have been studied for use in television and video games[1].
In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning
popularity of small mobile displays, gesture recognition systems capable of operating at much shorter ranges have
become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is
unknown to the user during operation, which imposes several restrictions on his/her actions.
To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture
recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are
prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two
recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and
motion analysis using image frame differences (motion-based approach)(for example, see[4]).
We evaluated this system using sample users and classified the resulting errors into three categories: errors that
depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user
actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture
recognition systems.
KEYWORDS: Associative arrays, Optical character recognition, Feature extraction, Image processing, Scanners, Document management, Electronic imaging, Current controlled current source, Detection and tracking algorithms, Windows XP
Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical
Character Recognition (OCR) systems. To shorten processing time, recognition is usually split into separate preclassification
and recognition stages. For high overall recognition performance, the pre-classification stage must both
have very high classification accuracy and return only a small number of putative character categories for further
processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The
associative matching (AM) method has often been used for fast pre-classification, because its use of a hash table and
reliance solely on logical bit operations to select categories makes it highly efficient. However, redundant certain level of
redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data
on each axis and therefore does not take account of the distribution of the data. We propose a modified associative
matching method that satisfies the performance criteria described above but in a fraction of the time by modifying the
hash table to reflect the underlying distribution of training characters. Furthermore, we show that our approach
outperforms pre-classification by clustering, ANN and conventional AM in terms of classification accuracy,
discriminative power and speed. Compared to conventional associative matching, the proposed approach results in a
47% reduction in total processing time across an evaluation test set comprising 116,528 Japanese character images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.