@article {271, title = {Fusing AMSR-E and QuikSCAT imagery for improved sea ice recognition}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {47}, year = {2010}, pages = {1980 - 1989}, abstract = {

The benefits of augmenting Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) image data with Quick Scatterometer (QuikSCAT) image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E-only data set against using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal component analysis (PCA) can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreases it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with PCA does not offer any benefit for these data, and training data from a specific date remains reliable within 30 days.

}, keywords = {Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E-QuikSCAT imagery fusion, data dimensionality reduction, geophysics computing, image classification, image fusion, maximum likelihood classifier, oceanographic regions, PCA, principal component analysis, Quick Scatterometer, Remote Sensing, sea ice, sea ice mapping, sea ice recognition, supervised sea ice classification, training data reliability, Western Arctic region}, doi = {http://dx.doi.org/10.1109/TGRS.2009.2013632}, author = {P Yu and D A. Clausi and S Howell} } @article {356, title = {Extended-Hungarian-JPDA: Exact single-frame stem cell tracking}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {54}, year = {2007}, pages = {2011 - 2019}, abstract = {

The fields of bioinformatics and biotechnology rely on the collection, processing and analysis of huge numbers of biocellular images, including cell features such as cell size, shape, and motility. Thus, cell tracking is of crucial importance in the study of cell behaviour and in drug and disease research. Such a multitarget tracking is essentially an assignment problem, NP-hard, with the solution normally found in practice in a reduced hypothesis space. In this paper we introduce a novel approach to find the exact association solution over time for single-frame scan-back stem cell tracking. Our proposed method employs a class of linear programming optimization methods known as the Hungarian method to find the optimal joint probabilistic data association for nonlinear dynamics and non-Gaussian measurements. The proposed method, an optimal joint probabilistic data association approach, has been successfully applied to track hematopoietic stem cells.

}, keywords = {Algorithms, Animals, biocellular images, bioinformatics, biological techniques, biotechnology, cell motility, cell shape, cell size, Cells, cellular biophysics, Computer-Assisted, Data Interpretation, extended-Hungarian-joint probabilistic data association, hematopoietic stem cells, Humans, image enhancement, image interpretation, linear programming optimization methods, Microscopy, multitarget tracking, nonGaussian measurements, nonlinear dynamics, optimal joint probabilistic data association, principal component analysis, Reproducibility of Results, Sensitivity and Specificity, single-frame scan-back stem cell tracking, Statistical, Video}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2007.895747}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {750, title = {A hybrid algorithm using discrete cosine transform and Gabor filter bank}, booktitle = {17th Canadian Conference on Electrical and Computer Engineering}, year = {2006}, abstract = {

Gabor filters have been widely used for texture segmentation and feature extraction, however there are important considerations regarding filter parameters, filter bank coverage in the frequency domain and feature dimensional reduction. In this paper, a texture segmentation algorithm based on a hybrid filter bank is presented. The proposed method uses a Gabor filter bank and discrete cosine transform (GDCT) to extract the optimal features for texture segmentation. To reduce the feature vector dimension a competitive network is trained to estimate the principal components of the extracted features. The feature vectors composing both Gabor and DCT features are quantized by estimated eigenvectors. The proposed method enables the use of multiple filter banks or larger filter banks consisting of a higher number of channels.

}, keywords = {channel bank filters, competitive network training, discrete cosine transform, discrete cosine transforms, eigenvalues and eigenfunctions, eigenvector quantization, feature dimensional reduction, Feature Extraction, filter bank coverage, filter parameters, Gabor filter bank, GDCT, hybrid algorithm, hybrid filter bank, image segmentation, image texture, multiple filter banks, neural nets, neural networks, optimal features, principal component analysis, principal components estimation, texture segmentation, unsupervised learning}, doi = {http://dx.doi.org/10.1109/CCECE.2004.1349767}, author = {N Nezamoddin Kachouie and J Alirezaie and P Fieguth} } @article {415, title = {Design-based texture feature fusion using Gabor filters and co-occurrence probabilities}, volume = {14}, year = {2005}, pages = {925 - 936}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter\&$\#$39;s capability of accurately capturing lower and mid-frequency texture information and the GLCP\&$\#$39;s capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, design-based texture feature fusion, feature contrast, feature reduction, Gabor filter, grey level cooccurrence probability, image classification, image enhancement, image interpretation, image segmentation, image segmentation classification, image texture, Information Storage and Retrieval, Models, Pattern Recognition, principal component analysis, probability, Statistical, Subtraction Technique, texture recognition}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2005.849319}, author = {D A. Clausi and H Deng} }