International Conference on VLSI, Communication & Instrumentation |
Foundation of Computer Science USA |
ICVCI - Number 11 |
None 2011 |
Authors: P.Swathika, S.Vanitha Sivagami |
0d4e7d23-57a7-45f0-9f51-ba941c7ab3aa |
P.Swathika, S.Vanitha Sivagami . Object Segmentation and Classification using Multiple Shape Models from an image sequence. International Conference on VLSI, Communication & Instrumentation. ICVCI, 11 (None 2011), 24-29.
A method of segmenting, classifying the multiple shapes from an image sequence is presented. This paper presents the segmentation framework that allows multiple shapes to be segmented simultaneously in a seamless fashion. Shape models (SMs), contains features of a set of training shapes, represent a new object present in the image. It is based on clustering a set of training shapes in the original shape space (defined by the coordinates of the contour points) based on the similarity measure. This method uses the prior shape and the prior shape information. Two main goals in this paper are segmenting the image and classifying the image.