Sedimentmiljön i Lumparnbukten, Åland

Summary in English: The sedimentary environment of Lumparn Bight, Åland

Site index curves for European aspen ( Populus tremula L.) growing on forest land of different soils in Sweden

Summary

The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions : the Mackenzie Basin case study

Selostus: Viljelyvyöhykkeiden ja kasvumallien soveltaminen ilmastonmuutoksen tutkimisessa: Mackenzien jokialue, Kanada

Ground beetle (Coleoptera, Carabidae) diversity in Finnish arable land

Appendix: Species of Carabidae and their total catch using pitfall-traps

Monitoring of Finnish arable land : changes in soil quality between 1987 and 1998

Selostus: Suomalaisten viljelymaiden tilan seuranta : muutoksia peltojen ominaisuuksissa aikavälillä 1987-1998

U.S. landowner behavior, land use and land cover changes, and climate change mitigation

Abstract

Land use response to agricultural policy and market movements on Finnish dairy-farms

Selostus: Tuki- ja hintamuutosten vaikutus maitotilojen pellon käyttöön

Effects of competing vegetation and post-planting weed control on the mortality, growth and vole damages to Betula pendula planted on former agricultural land

Abstract

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.