@article {221, title = {Generalized probabilistic scale space for image restoration}, journal = {IEEE Transactions on Image Processing}, volume = {19}, year = {2010}, pages = {2774 - 2780}, abstract = {

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.

}, keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2010.2048973}, author = {A Wong and A Mishra} } @inproceedings {338, title = {Robust edge detection based on non-local contribution of local frequency characteristics}, booktitle = {IEEE International Symposium on Multimedia}, year = {2008}, abstract = {

This paper introduces a robust approach to the problem of edge detection in situations characterized by high levels of image degradation and illumination non-uniformity. Popular edge detection methods are dependent solely on local image characteristics to localize edge information. In the proposed algorithm, we demonstrate that superior edge detection performance can be achieved by utilizing nonlocal image characteristics. This is accomplished by estimating the edge strength of a given pixel based on the local frequency characteristics of every pixel within an image. The proposed method is highly robust to scenarios characterized by illumination non-uniformity and low signal-to-noise ratios. Experimental results show that using the proposed method, edge detection can be qualitatively improved over existing methods on a small set of test images.

}, keywords = {edge detection, edge strength estimation, estimation theory, illumination nonuniformity, image degradation, local frequency characteristics, robust edge detection, signal-to-noise ratio}, doi = {http://dx.doi.org/10.1109/ISM.2008.79}, author = {A Wong and W Bishop} } @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} }