Geum-Boon Lee

Two Texture Segmentation of Document Image Using Wavelet Packet Analysis

In this paper, we present a text segmentation method using wavelet packet analysis and k-means clustering algorithm. This approach assumes that the text and non-text regions are considered as two different texture regions. The text segmentation is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multiscale features, we compute the local energy and intensify the features before adapting the k-means clustering algorithm based on the unsupervised learning rule. The results show that our text segmentation method is effective for document images scanned from newspapers and journals.

Facial component features for facial expression identification using Active Appearance Models

Statistical methods of shape and appearance are powerful tools used in computer vision for near-correct interpretation of images. In this paper, we present a method for classifying facial expressions based on the extracted features of facial components. The face, the window to the inner self of an individual can be analyzed for outright expressions like sadness, happiness, anger, surprise, disgust and fear. The facial region is detected; pre-processing is done on the image by using Active Appearance Model (AAM) to extract the vital feature on the facial components. Six classes are formed based on the different expressions and the model from the AAM procedure used to compare with the query image/face. Mahalanobis distance algorithm is used to classify the image in question into the best-fit class. Japanese Female Facial Expression (JAFFE) public database is used to evaluate our method with over 200 images of still images used in our experiment. A higher classification rate observed.

Windows Security Patch Auto-Management System Based on XML

In recent days, damages to information systems and network due to worm and virus using vulnerabilities of windows security have been rapidly increasing. How to deal with the attack using vulnerabilities of windows program is to install patch appropriately and rapidly. This study suggests security patch auto-management system which installs security patch file automatically to clients through automatic downloading of the patch from MS download center based on XML as existing patch management system needs intervention of managers.