Interoperability specifications and an automatic validation tool as a key factor for the support of a repository ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Bully-victims: Prevalence, psychosocial adjustment, and responsiveness to intervention

In this thesis, a unique subgroup involved in the bullying phenomenon, the bully-victims, are identified and examined. Despite the increasing attention on the bully-victims in recent years, their prevalence, psychosocial adjustment, and response to anti-bullying programs has not been clearly determined. Three empirical studies were conducted in this thesis to examine the prevalence of bully-victims. Moreover, in study I, the psychosocial adjustment of bully-victims was compared with that of pure bullies, pure victims, and non-involved students. In study II, different forms of bullying and victimization were compared among pure bullies, pure victims, bully-victims, and non-involved students. In study III, the effectiveness of anti-bullying programs, in particular, the KiVa program, on bully-victims was demonstrated. Overall, bully-victims formed the smallest group comparing with pure bullies, pure victims, and non-involved students, and in general differed from pure bullies rather than pure victims in terms of subjective experience of maladjustment. They employed more verbal, physical, and cyberbullying perpetration, but not indirect bullying; and they were more victimized by verbal, physical, cyber, and indirect bullying. The KiVa anti-bullying program in Finland is effective in reducing the prevalence of bully-victims.