22 research outputs found
Fast Contour Matching Using Approximate Earth Mover's Distance
Weighted graph matching is a good way to align a pair of shapes represented by a set of descriptive local features; the set of correspondences produced by the minimum cost of matching features from one shape to the features of the other often reveals how similar the two shapes are. However, due to the complexity of computing the exact minimum cost matching, previous algorithms could only run efficiently when using a limited number of features per shape, and could not scale to perform retrievals from large databases. We present a contour matching algorithm that quickly computes the minimum weight matching between sets of descriptive local features using a recently introduced low-distortion embedding of the Earth Mover's Distance (EMD) into a normed space. Given a novel embedded contour, the nearest neighbors in a database of embedded contours are retrieved in sublinear time via approximate nearest neighbors search. We demonstrate our shape matching method on databases of 10,000 images of human figures and 60,000 images of handwritten digits
Contour matching using ant colony optimization and curve evolution
Shape retrieval is a very important topic in computer vision. Image retrieval consists
of selecting images that fulfil specific criteria from a collection of images. This thesis
concentrates on contour-based image retrieval, in which we only explore the
information located on the shape contour. There are many different kinds of shape
retrieval methods. Most of the research in this field has till now concentrated on
matching methods and how to achieve a meaningful correspondence. The matching
process consist of finding correspondence between the points located on the designed
contours. However, the huge number of incorporated points in the correspondence
makes the matching process more complex. Furthermore, this scheme does not
support computation of the correspondence intuitively without considering noise
effect and distortions. Hence, heuristics methods are convoked to find acceptable
solution. Moreover, some researches focus on improving polygonal modelling
methods of a contour in such a way that the resulted contour is a good approximation
of the original contour, which can be used to reduce the number of incorporated
points in the matching. In this thesis, a novel approach for Ant Colony Optimization
(ACO) contour matching that can be used to find an acceptable matching between
contour shapes is developed. A polygonal evolution method proposed previously is
selected to simplify the extracted contour. The main reason behind selecting this
method is due to the use of a stopping criterion which must be predetermined. The
match process is formulated as a Quadratic Assignment Problem (QAP) and resolved
by using ACO. An approximated similarity is computed using original shape context
descriptor and the Euclidean metric. The experimental results justify that the
proposed approach is invariant to noise and distortions, and it is more robust to noise
and distortion compared to the previously introduced Dominant Point (DP)
Approach. This work serves as the fundamental study for assessing the Bender Test
to diagnose dyslexic and non-dyslexic symptom in children