Quadtree algorithm and morphological image processing are combined in the proposed method in this paper. A new method is proposed to improve the previous pattern mapping method for faster processing. The previous pattern mapping method is a pattern mapping method by storing the tree pattern by string form, which is a pointless data structure. In the proposed method the tree pattern is saved in a point data structure. Therefore, the pointer tree can be applied to the quadtree immediately without the transforming time, which was required in the previous pattern mapping method. In this paper, the pointless quad tree work is modified to pointer quad tree to reduce the processing time. The modified algorithm is applied to circuit detection, image restoration, image segmentation and cell counting.

The modern day tourists do not want to deal with the hassle of using a large number of travel guides and paper maps while travelling. They would prefer to be able to access required information via their mobile phones or Personal Digital Assistants (PDAs). We realise that the delivered information may be originally available in numerous information formats. To support the administrator of the tourist guides the programme is required to help sorting information from these different sources and to help inserting them into a system. Our goal with this project is to develop a software support for processing information import via a graphical user interface, to support the administrator in identifying and extracting the appropriate sight information from various resources. The interface also helps in transferring and storing the structured and unstructured data into the TIP database.

The relational database model was defined in the 1970‟s and is the dominating database type today.  The main difference between data from a relational database and a hierarchical data structure is that the relational database stores records in tables. The records have no particular order, but can include links in terms of relationships with other records. A hierarchical structure organizes data in the form of a tree structure  and  can for an example be found in organizational structures in which different levels involves different responsibilities.  If the data stored in a relational database is to be presented in a hierarchically, a conversion of the data structure is required. The intention of this paper is to describe how such a conversion can be performed.   To investigate the conversion methods, case studies has been conducted on the basis of a specific organization‟s hierarchical structure. Web based prototypes were developed in Silverlight to evaluate the conversion of a hierarchical structure, based on the organization that was represented in a relational database. Existing tools were used in order to extract data from a database and transfer data in a client-server architecture.  The result is a framework for the conversion of relational data into hierarchical structure and describes the process step by step. A conversion process includes the design of the database source, extraction and transfer of data to a web client and the algorithm for performing the conversion into a tree structure. / Relationsdatabaser definierades på 1970-talet och är den dominerande databastypen idag. Skillnaden mellan data i en relationsdatabas och en hierarkisk datastruktur är att relationsdatabasen sparar poster i tabeller. Poster i tabellerna behöver ingen inbördes ordning, men kan omfatta  kopplingar i form av relationer till andra poster. En hierarkisk struktur organiserar data i form av trädstruktur och återfinns till exempel i organisationsstrukturer där olika nivåer innefattar olika ansvarsområden.  Om data som sparats i en relationsdatabas skall visas upp hierarkiskt krävs en omvandling av datastrukturen. Syftet med uppsatsen är att redogöra för hur en sådan omvandling kan utföras.  För att utreda omvandlingsmetoder har fallstudier bedrivits utifrån en specifik organisations hierarkiska struktur. Webbaserade prototyper utvecklades i Silverlight för att utvärdera omvandling till hierarkisk struktur utifrån organisationen som fanns representerad i en relationsdatabas.  Till hjälp existerar verktyg för att extrahera data ur databas samt överföra data i klient-server arkitektur.  Resultatet är ett ramverk för omvandling av relationsdata till hierarkisk struktur  och beskriver processen steg för steg. En omvandlingsprocess omfattar utformning av databas för källa, extrahering och överföring av data till en webbklient samt  algoritm  för utförande av omvandling till trädstruktur.

This dissertation proposes two novel manifold graph-based ranking systems for Content-Based Image Retrieval (CBIR). The two proposed systems exploit the synergism between relevance feedback-based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Proposed systems first apply the active learning mechanism to construct users' relevance feedback log and extract high-level semantic features for each image. These systems then create manifold graphs by incorporating both the low-level visual similarity and the high-level semantic similarity to achieve more meaningful structures for the image space. Finally, asymmetric relevance vectors are created to propagate relevance scores of labeled images to unlabeled images via manifold graphs. The extensive experimental results demonstrate two proposed systems outperform the other state-of-the-art CBIR systems in the context of both correct and erroneous users' feedback.