In this paper, an object extraction algorithm from complex scenes is presented. Firstly, Textons are modeled by the joint
distribution of filter responses. This distribution is represented by Texton (cluster centre) frequencies. Secondly,
classification of a novel image proceeds by mapping the image to a Texton distribution and comparing this distribution
to the learnt models. So the detection of possible object regions is performed. During the verification stage, the
knowledge about the scene and the geometry of the objects is represented by means of t graph, and especially, the
knowledge about the surrounding of the object is used in order to support the detection of individual objects. Finally,
Bayes nets are selected to verify those possible objects as a useful tool. The test on the dataset in building scenes shows
that the proposed algorithm has a better performance, compared with the similar methods.
This paper presents a line feature matching algorithm. Firstly, it extracts the set of line features in the image, and represents
an object using attributed relational graph (ARG). By defining relation vectors between the adjacent features, the graph can
describe the structural information of an object. Secondly, the one-to-one correspondences between model features and
image features is established by two processes - coarse match and refined match through the analysis of matching ordering
and matching number of relation vectors. Finally, the object examples in the image are extracted. Test showed that the
proposed algorithm had superior performance to the present line feature matching algorithms, which is robust to shape
deformation, or input noise, and decreased the computational cost.