Abstract—This paper presents a new localization method which uses two classical algorithms from computer science and computer vision. Described method was successfully applied to the problem of cell localization and the results show that it can be used as a part of a whole autonomous segmentation process. Furthermore, methods from spatial statistics were employed to model the spatial distribution of the localized objects. In-depth discussion of these methods including density plots and the Ripley’s K function is presented along with the results for test set used in the experiments.
Index Terms—Object localization, Spatial Analysis,Segmentation
E. B. Author is with the Computer Science and Electronics Engineering Department in University of Essex, Colchester, United Kingdom(corresponding author to provide phone: +44 7767304727; e-mail:gebost@essex.ac.uk).
B. B. Author is with EFL at Colchester Institute, Colchester, United Kingdom (e-mail: eserbetul@gmail.com).
Cite: Erkan Bostanci and Betul Bostanci, "Object Localization and Spatial Analysis Using Computer Vision," International Journal of Machine Learning and Computing vol. 1, no. 2, pp. 120-124 , 2011.