Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.

Unsupervised visual object categorization

The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.

Fiber-reinforced Composite as Oral Implant Material. Experimental studies of glass fiber and bioactive glass in vitro and in vivo

Fiber-reinforced composite as oral implant material: Experimental studies of glass fiber and bioactive glass in vitro and in vivo Department of Prosthetic Dentistry and Biomaterials Science, Institute of Dentistry, University of Turku, Turku, Finland 2008. Biocompatibility and mechanical properties are important variables that need to be determined when new materials are considered for medical implants. Special emphasis was placed on these characteristics in the present work, which aimed to investigate the potential of fiber-reinforced composite (FRC) material as an oral implant. Furthermore, the purpose of this study was to explore the effect of bioactive glass (BAG) on osseointegration of FRC implants. The biocompatibility and mechanical properties of FRC implants were studied both in vitro and in vivo. The mechanical properties of the bulk FRC implant were tested with a cantilever bending test, torsional test and push-out test. The biocompatibility was first evaluated with osteoblast cells cultured on FRC substrates. Bone bonding was determined with the mechanical push-out test and histological as well as histomorplanimetric evaluation. Implant surface was characterized with SEM and EDS analysis. The results of these studies showed that FRC implants can withstand the static load values comparably to titanium. Threaded FRC implants had significantly higher push-out strength than the threaded titanium implants. Cell culture study revealed no cytotoxic effect of FRC materials on the osteoblast-like-cells. Addition of BAG particles enhanced cell proliferation and mineralization of the FRC substrates The in vivo study showed that FRC implants can withstand static loading until failure without fracture. The results also suggest that the FRC implant is biocompatible in bone. The biological behavior of FRC was comparable to that of titanium after 4 and 12 weeks of implantation. Furthermore, addition of BAG to FRC implant increases peri-implant osteogenesis and bone maturation.

Development of a Filter Aid Proportioning, Mixing, and Feeding System for a Polishing Filter

The purpose of this work was to study the characteristics of the most commonly used filter aid materials and their influences on the design of proportioning, mixing, and feeding system for polishing filter family. Based on the literature survey and hands-on experience a system was designed with defined equipment and capital and operating costs. The system was designed to serve precoating and bodyfeeding applications and is easily extended to be used in multiple filter processes. Also a test procedure was carried out where influences of flux and filter cloths to accumulated cake were studied. Filter aid is needed in challenging conditions to improve filtration efficiency and cleaning, and thus extend the operating life of the filter media. Filter aid preparation and feeding system was designed for the use of two different filter aids; precoat and bodyfeed. Precoating is used before the filtration step initiates. If the solids in the filterable solution have a tendency to clog the filter bag easily, precoat is used on the filter bag to obtain better filtration efficiency and quality. Diatomite or perlite is usually used as a precoating substance. The intention is to create a uniform cake to the overall surface of the filter cloth, with predetermined thickness, 2 – 5 mm. This ensures that the clogging of the filter cloth is reduced and the filtration efficiency is increased. Bodyfeed is used if the solids in the filterable solution have a tendency to form a sticky impermeable filter cake. The cake properties are enhanced by maintaining the permeability of the accumulating cake by using the filter aid substance as bodyfeed during the filtration process.