@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 {368, title = {Robust invariant descriptor for symbol-based image recognition and retrieval}, booktitle = {1st IEEE International Conference on Semantic Computing}, year = {2007}, abstract = {

This paper presents a robust invariant descriptor for symbol-based image recognition and retrieval. A modified Hough-based Transform is used to extract parameter space information (i.e., position data and angular data) from a symbol image to derive an invariant descriptor. The proposed descriptor provides a compact representation of a symbol image that can be evaluated efficiently. The extracted descriptor is highly robust against geometric transformations such as translation, rotation, reflection, and scaling, and image degradation. A series of experiments were conducted using a set of architectural and engineering symbols subjected to geometric transformations and image degradation. The experimental results clearly show that the proposed descriptor can be used effectively for symbol recognition and retrieval.

}, keywords = {content-based retrieval, Feature Extraction, geometric transformation, Hough transforms, Hough-based transform, image degradation, image recognition, image representation, image retrieval, robust invariant descriptor, symbol-based image recognition}, doi = {http://dx.doi.org/10.1109/ICSC.2007.24}, author = {A Wong and W Bishop} } @inproceedings {421, title = {A Gabor based technique for image denoising}, booktitle = {18th Canadian Conference on Electrical and Computer Engineering}, year = {2005}, address = {Saskatoon}, abstract = {

As an alternative to the wavelet, Gabor function has been used as an efficient representation of two dimensional signals. We are interested in BayesShrink techniques for image denoising, and have shown in our previous work that BayesShrink Ridgelet performs better than VisuShrink ridgelet and VisuShrink wavelet. In this paper, a dyadic Gabor filter bank is combined with BayesShrink method for image denoising. In the proposed method, the noisy image is decomposed to different channels in several levels by a dyadic Gabor filter bank. To recover the image, the corrupting noise is removed by applying the proposed BayesShrink method on the noisy Gabor coefficients. The noise variance is estimated in Gabor domain and the estimated noise is then used to dynamically calculate an individual threshold for each spatio-frequency channel. Finally denoised coefficients are transformed back to reconstruct the image

}, keywords = {Bayes methods, BayesShrink ridgelet, channel bank filters, dyadic Gabor filter banks, Gabor based technique, Gabor filters, Image Denoising, image reconstruction, image representation, noise variance, spatiofrequency channels, two-dimensional signal representation, VisuShrink ridgelet, wavelet transforms}, doi = {http://dx.doi.org/10.1109/CCECE.2005.1557140}, author = {N Nexamoddin Kachouie and 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 {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} } @inproceedings {453, title = {Multiresolution model development for overlapping trees via canonical correlation analysis}, booktitle = {International Conference on Image Processing}, year = {1995}, abstract = {

A class of multiscale stochastic models has been introduced in which Gaussian random processes are described by scale-recursive dynamics that are indexed by the nodes of a tree. One of the primary reasons the framework is useful is that it leads to an extremely fast, statistically optimal algorithm for least-squares estimation in the context of 2-D images. We refine this approach to estimation by eliminating the visually distracting blockiness that has been observed in the previous work. We eliminate the blockiness by discarding the standard assumption that distinct nodes at a given level of our tree correspond to disjoint portions of the image domain; as a consequence of this simple idea, a given image pixel may now correspond to several tree nodes. We develop tools for systematically building overlapping-tree multiscale representations of prespecified statistics, and we develop a corresponding estimation algorithm for this processes. In this way, we achieve nearly optimal estimation results, we generate corresponding error covariance information, and we eliminate blockiness without sacrificing the resolution of fine-scale detail

}, keywords = {canonical correlation analysis, correlation methods, covariance analysis, error analysis, error covariance information, estimation algorithm, estimation theory, fast statistically optimal algorithm, Gaussian processes, Gaussian random processes, image domain, image pixel, image representation, image resolution, multiresolution model, multiscale representations, multiscale stochastic models, optimal estimation results, overlapping trees, random processes, scale recursive dynamics, statistics, tree nodes, trees (mathematics)}, doi = {http://dx.doi.org/10.1109/ICIP.1995.529035}, author = {P Fieguth and W W. Irving and A S. Willsky} }