Flash memory is becoming a major database storage in building embedded systems or portable devices because of its non-volatile, shock-resistant, power-economic nature, and fast access time for read operations. Flash memory, however, should be erased before it can be rewritten and the erase and write operations are very slow as compared to main memory. Due to this drawback, traditional database management schemes are not easy to apply directly to flash memory database for portable devices. Therefore, we improve the traditional schemes and propose a new scheme called Flash Two Phase Locking (F2PL) scheme for efficient transaction processing in a flash memory database environment. F2PL achieves high transaction performance by exploiting the notion of the Alternative Version Coordination which allows previous version reads and efficiently handles slow write/erase operations in lock management processes. We also propose a simulation model to show the performance of F2PL. Based on the results of the performance evaluation, we conclude that F2PL scheme outperforms the traditional schemes.

Flash memories are one of the best media to support portable computers' storage areas in mobile database environments. Their features include non-volatility, low power consumption, and fast access time for read operations, which are sufficient to present flash memories as major database storage components for portable computers. However, we need to improve traditional index management schemes based on B-Tree due to the relatively slow characteristics of flash operations, as compared to RAM memory. In order to achieve this goal, we propose a new index rewriting scheme based on a compressed index called F-Tree. FTree-based index management improves index operation performance by compressing pointers and keys in tree nodes and rewriting the nodes without a slow erase operation in node insert/delete processes. Based on the results of the performance evaluation, we conclude that the F-Tree-based scheme outperforms the traditional schemes.

In this paper, we propose a method of effectively segmenting character regions by using texture and depth features in 3D stereoscopic images. The suggested method is mainly composed of four steps. The candidate character region extraction step extracts candidate character regions by using texture features. The character region localization step obtains only the string regions in the candidate character regions. The character/background separation step separates characters from background in the localized character areas. The verification step verifies if the candidate regions are real characters or not. In experimental results, we show that the proposed method can extract character regions from input images more accurately compared to other existing methods.

Detecting target objects robustly in natural environments is a difficult problem in the computer vision and image processing areas. This paper suggests a method of robustly detecting target objects in the environments where reflection exists. The suggested algorithm first captures scenes with a stereo camera and extracts the line and corner features representing the target objects. This method then eliminates the reflected features among the extracted ones using a homographic transform. Subsequently, the method robustly detects the target objects by clustering only real features. The experimental results showed that the suggested algorithm effectively detects the target objects in reflection environments rather than existing algorithms.

Since the existing methods of segmenting target objects from various images mainly use 2-dimensional features, they have several constraints due to the shortage of 3-dimensional information. In this paper, we therefore propose a new method of accurately segmenting target objects from three dimensional stereoscopic images using 2D and 3D feature clustering. The suggested method first estimates depth features from stereo images by using a stereo matching technique, which represent the distance between a camera and an object from left and right images. It then eliminates background areas and detects foreground areas, namely, target objects by effectively clustering depth and color features. To verify the performance of the proposed method, we have applied our approach to various stereoscopic images and found that it can accurately detect target objects compared to other existing 2-dimensional methods.