@inproceedings {592, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {449, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} } @inproceedings {531, title = {Image segmentation using MRI vertebral cross-sections}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Computer assisted spinal surgery requires the development of a 3-D image of a patient\&$\#$39;s spine. A method is being developed to construct such a 3-D spinal image from axial MRI cross-sections, using a deformable template. This paper outlines techniques used to register the model with the patient data. First an algorithm based on symmetry of the anatomy is developed to determine the position and orientation of the vertebrae in each cross-section. Second, an active contour is used for segmentation of the spinal cord. Last, a novel edge detection scheme is developed to identify the low contrast edges of the vertebral body in each cross-section

}, keywords = {3D image, 3D spinal image, algorithm, anatomy symmetry, axial MRI cross-sections, biomedical MRI, computer assisted spinal surgery, deformable template, edge detection, image segmentation, low contrast edges identification, medical image processing, MRI vertebral cross-sections, orthopaedics, orthopedic surgery, patient data, patient spine, scale estimation, spinal cord segmentation, surgery, vertebrae orientation, vertebrae position, vertebral body}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933633}, author = {S Booth and D A. Clausi} } @inproceedings {461, title = {Multiresolution stochastic processing of Topex/Poseidon oceanographic altimetry}, booktitle = {IEEE OCEANS{\textquoteright}94}, year = {1994}, abstract = {

Remarkable advances in remote sensing are producing measurements on increasingly finer grids, offering the possibility of a far better understanding and characterization of the ocean surface. The sheer quantity of measurements has rendered traditional data smoothing/assimilation algorithms obsolete however, even on high performance workstations; the basis of this paper is the application of an extremely fast multiresolution smoothing framework to ocean altimetry data. Using the previously developed multiresolution framework, a simple multiscale model is developed which captures the characteristic power-law spectrum of the ocean surface. Smoothing altimetric data and computing error variances on a grid of 512 by 512 points is accomplished in about 10 seconds on a SUN Sparc-10. Finally, further applications of this framework within the oceanographic context are discussed, including model identification and anomaly detection

}, keywords = {algorithm, data assimilation, data smoothing, geophysical signal processing, geophysics computing, measurement technique, multiresolution stochastic processing, multiscale model, ocean dynamics, ocean surface, oceanographic techniques, Poseidon, radar altimetry, radar applications, radar imaging, radar signal processing, Remote Sensing, remote sensing by radar, satellite remote sensing, sea surface, signal processsing, Topex}, doi = {http://dx.doi.org/10.1109/OCEANS.1994.363913}, author = {P Fieguth and W C. Karl and A S. Willsky} }