We describe a document image segmentation algorithm to classify a scanned document into different regions
such as text/line drawings, pictures, and smooth background. The proposed scheme is relatively independent
of variations in text font style, size, intensity polarity and of string orientation. It is intended for use in an
adaptive system for document image compression. The principal parts of the algorithm are the generation of
the foreground and background layers and the application of hierarchical singular value decomposition (SVD)
in order to smoothly fill the blank regions of both layers so that the high compression ratio can be achieved.
The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated
using several test images and showed superior performance compared to other techniques.
In recent years, various gesture recognition systems have been studied for use in television and video games.
In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated. 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) and
motion analysis using image frame differences (motion-based approach)(for example, see).
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
Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In
this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of
processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained
jointly, rather than independently. In this way, common features can be shared across multiple classes for better
generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest
(ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and
two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a
large logo database demonstrate the effectiveness and efficiency of our proposed method.