@inproceedings {327, title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.

}, keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164}, author = {A Wong and D A. Clausi and P Fieguth} } @inproceedings {648, title = {Phase-based methods for Fourier shape matching}, booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2004}, address = {Montreal}, abstract = {

Interest in the problem of shape matching has been recently reignited by the huge proliferation of images on the Internet, particularly huge computer databases containing thousands or millions of images. Although many typical image database algorithms rely on a variety of shading, texture, and colour attributes, there are significant opportunities for the use of shape as a discriminator, particularly for binary or high-contrast images. Fourier shape descriptors have been studied extensively for shape comparison, however the descriptor phases have been mostly neglected - either ignored entirely or treated simplistically. This paper formalizes the use of phase in shape matching, and derives shape discriminators, applicable to both simple and complex shapes.

}, keywords = {binary images, complex shapes, fast Fourier transforms, Fourier shape descriptors, Fourier shape matching, high-contrast images, huge computer databases, image database algorithms, image matching, image retrieval, Internet, phase-based methods, shape discriminators, simple shapes, very large databases, visual databases}, doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326572}, author = {P Fieguth} } @inproceedings {534, title = {Morphological skeleton algorithm for PDP production line inspection}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Morphological skeletonization is an image processing technique that reduces complex, thick-lined images to a series of single pixel lines that accurately represent the original shapes. This procedure is especially useful to simplify automated applications requiring simple shape analysis and continuity checking by reducing the amount of redundant image data. In the semiconductor inspection field, skeletonization is a process that can be used to detect defects during plasma display panel (PDP) inspection. This paper introduces a novel morphological skeletonization algorithm developed for electrode pattern inspection of PDPs. This algorithm has been successfully integrated within a commercial machine vision system

}, keywords = {automated applications, commercial machine vision system, Computer Vision, continuity checking, defects detection, electrode pattern inspection, Feature Extraction, feature point matching, image matching, image processing, image representation, image thinning, Inspection, Mathematical morphology, morphological skeleton algorithm, PDP inspection, PDP production line inspection, plasma display panel, plasma displays, production, redundant image data reduction, semiconductor device testing, semiconductor inspection, shape analysis, shape representation, single pixel lines, thick-lined images}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933598}, author = {R Ge and D A. Clausi} } @inproceedings {501, title = {Fast retrieval methods for images with significant variations}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

Fast image retrieval is the key to success for operations on large image databases, and a great many techniques have been developed for efficient retrieval. However, most of these methods are tailored to visual scenes or to images having limited variations. We investigate the searching of enormous databases (of up to 10 7 images) for the matching and identification of precious stones (principally diamonds). Because of the size of the database, we propose a hierarchy of classifiers, which successively prune candidate images such that the more complex classifiers are required to test only tiny portions of the data. The new classifier developed here applies a wavelet transform to image histograms and is capable of rejecting 99.9\% of bad matches.

}, keywords = {classifiers hierarchy, database searching, diamond, diamonds, fast image retrieval methods, image classification, image histograms, image matching, image retrieval, large image databases, precious stones identification, precious stones matching, visual databases, visual scenes, wavelet transform, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899470}, author = {P Fieguth and R Wan} } @inproceedings {502, title = {Incremental shape reconstruction using stereo image sequences}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

The limitations of estimating structure from either stereo or motion alone can be addressed by the use of stereo image sequences; however, many existing techniques for processing stereo sequences rely on having accurate feature correspondences initially. We present an iterative feature matching and shape reconstruction algorithm. The proposed method simultaneously resolves matching ambiguities by multiple hypothesis testing and develops an incrementally accurate and dense representation of the reconstructed object. This approach uses minimal domain specific constraints and can be easily generalised. The method\&$\#$39;s potential is demonstrated in a space vision application.

}, keywords = {artificial satellites, Computer Vision, dense representation, dynamic estimation, feature correspondences, Feature Extraction, image matching, image reconstruction, image representation, image sequences, incremental shape reconstruction, iterative feature matching algorithm, minimal domain specific constraints, multiple hypothesis testing, parameter estimation, reconstructed object, satellite docking, satellite rendezvous, space vision application, stereo image processing, stereo image sequences, stereo sequences processing, structure estimation}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899818}, author = {T J. Moyung and P Fieguth} }