Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Quality parameter assessment on iris images

Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. Poor quality images may affect the recognition as they have few sufficient feature information. Moreover, existing quality measures focuses on parameters or factors than feature information. In this paper, iris quality assessment research is extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation and sharpness of an iris image which determines the iris size, amount of information and clearness of the features. A weighting method based on principal component analysis (PCA) is proposed to determine the influence each parameter has on the quality score. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters have the most influence in the quality of the image as they weighed more than other parameter