SPIE Template | Electronics Seminar Topic
SPIE Template
Template matching is a classical problem in a scene
analysis: given a reference image of an object, decide whether that object
exists in a scene image under analysis, and find its location if it does. The
template matching process involves cross-correlating the template with the
scene image and computing a measure of similarity between them to determine the
displacement. Since the evaluation of the correlation is computationally
expensive, there has been a need for low-cost correlation algorithms for
real-time processing.
A large number of correlation-type algorithms have been
proposed. One of the approaches is to use an image pyramid for both the
template and the scene image, and to perform the registration by a top-down
search. Other fast matching techniques use two pass algorithms; use a
sub-template at a coarsely spaced grid in the first pass, and search for a
better match in the neighborhood of the previously found positions in the
second pass. A. Rosenfeld and A. Kak.
Digital Image Processing (2nd Edition, Vol. 2 ed.),,
Academic Press, Orlando (1982).Afterwards, Jane You presented a wavelet based
high performance hierarchical scheme for image matching which includes dynamic
detection of interesting points, adaptive thresholding selection and a guided
searching strategy for best matching from coarse level to fine level.
In order to improve the accuracy of matching and at the same
time to reduce the computation load, In this paper, we proposed a robust image
matching approach which decreases a large amount of unnecessary searches in
contrast to conventional scheme and can achieve a better matching accuracy.
Discrete wavelet transform is done firstly on a reference image and a scene
image,
and low frequency parts of them is extracted, then we use
harris corner detection to detect the interesting point in low frequency parts
of them to determined the matching candidate region of scene image in reference
image, SIFT is used to extracting feature on the matching candidate region and
scene image, The extracted features are matched by k-d tree and bidirectional
matching strategy to enhance the accuracy of matching. Experiment show that,
the algorithm can improve the accuracy of matching and at the same time to
reduce the computation load.
