Affiliations: Universidad Nacional del Centro, 7000 Tandil,
Argentina. E-mail: mdelfres@exa.unicen.edu.ar | Also CNEA-CONICET
Abstract: Automatic segmentation and classification of color images is a
problem of great practical interest in different areas. This paper presents an
algorithm for this purpose which is divided in three steps. Firstly, the
regions of interest are isolated from the rest of the image based on threshold
functions defined in the YUV and YIQ color spaces, producing a set of connected
components. Then, a set of features is computed to enable a quantitative
evaluation of the segmented objects. Finally, the image is classified by means
of a decision rule based on the analysis of the differences between the
computed measures and a set of ideally segmented images, according to
experts' assessment. The algorithm was applied to a decision
support tool for estrus detection in cattle. This approach constitutes a
valuable alternative to improve this process, as it may replace the visual
observation by the automatic analysis of pictures taken to cows in controlled
environments. Experimental results show that the segmentations obtained with
this method are highly satisfactory and they allow a precise classification of
the images with low computational complexity.