Raoul Pascal Pein

User Centred Mobile Aided Learning System: Student Response System (SRS)

This research develops a new system based on the user centred concepts and applied on the latest mobile devices. XML, database, information retrieval and object oriented technologies are embedded into the system. It is found that the system demonstrates a strong impact on teaching and learning in class activities in comparison with traditional learning environments. Also, the system has achieved that It can be self independent as well as integrated with other learning management environments, such as blackboard, Smartboard as well as subject oriented learning management systems, which makes an additional contribution to the modern pedagogical applications, such as activity based learning.

An extensible query language for content based image retrieval based on Lucene

One of the most important bits of every search engine is the query interface. Complex interfaces may cause users to struggle in learning the handling. An example is the query language SQL. It is really powerful, but usually remains hidden to the common user. On the other hand the usage of current languages for Internet search engines is very simple and straightforward. Even beginners are able to find relevant documents. This paper presents a hybrid query language suitable for both image and text retrieval. It is very similar to those of a full text search engine but also includes some extensions required for content based image retrieval. The language is extensible to cover arbitrary feature vectors and handle fuzzy queries.

Using CBIR and semantics in 3D-model retrieval

This paper proposes a generic design for 3D model retrieval. It has been developed in cooperation with EADS, which deals with Computer Aided Design / Computer Aided Engineering (below CAD/CAE) data through the product life cycle. Sharing these models with users across the enterprise is a challenging task. CAD/CAE models may be hundreds of megabytes large and stored in proprietary formats. Browsing and previewing this data efficiently requires new tools. One way to leverage the collaboration in and outside of CAD domains is to offer through a repository an access to a neutral 3D data format. Also functionalities for semantic enrichment of 3D models, for the retrieval of context sensitive information and for 3D model retrieval based on 3D similarity search and CBIR techniques should be provided.

A Flexible Image Retrieval Framework

This paper discusses a framework for image retrieval. Most current systems are based on a single technique for feature extraction and similarity search. Each technique has its advantages and drawbacks concerning the result quality. Usually they cover one or two certain features of the image, e.g. histograms or shape information. The proposed framework is designed to be highly flexible, even if performance may suffer. The aim is to give people a platform to implement almost any kind of retrieval issues very quickly, whether it is content based or somehing else. The second advantage of the framework is the possibility to change retrieval characteristics within the program completely. This allows users to configure the ranking process as needed.

An Extensible Query Language for Content Based Image Retrieval

One of the most important bits of every search engine is the query interface. Complex interfaces may cause users to struggle in learning the handling. An example is the query language SQL. It is really powerful, but usually remains hidden to the common user. On the other hand the usage of current languages for Internet search engines is very simple and straightforward. Even beginners are able to find relevant documents. This paper presents a hybrid query language suitable for both image and text retrieval. It is very similar to those of a full text search engine but also includes some extensions required for content based image retrieval. The language is extensible to cover arbitrary feature vectors and handle fuzzy queries.

Multi-feature query language for image classification

Abstract Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) systems during the last decade, the solutions available are still not satisfying for generic purposes. The most severe issue seems to be the so-called “semantic gap”. It is feasible to define and use domain specific feature vectors on a low level and use this information for a similarity based retrieval. Yet, mapping these to higher level semantics remains difficult. This research investigates a domain-independent way of automatized image categorization. A CBIR query language is constructed to build query-like descriptors for each category to be learned. The proposed learning algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable and modifiable by expert users. A casestudy is presented to support these claims.