@inproceedings {34, title = {TIGER: A Texture-Illumination Guided Energy Response Model for Illumination Robust Local Saliency}, booktitle = {IEEE International Conference on Image Processing}, year = {2015}, abstract = {

Local saliency models are a cornerstone in image processing and computer vision, used in a wide variety of applications ranging from keypoint detection and feature extraction, to image matching and image representation. However, current models exhibit difficulties in achieving consistent results under varying, non-ideal illumination conditions. In this paper, a novel texture-illumination guided energy response (TIGER) model for illumination robust local saliency is proposed. In the TIGER model, local saliency is quantified by a modified Hessian energy response guided by a weighted aggregate of texture and illumination aspects from the image. A stochastic Bayesian disassociation approach via Monte Carlo sampling is employed to decompose the image into its texture and illumination aspects for the saliency computation. Experimental results demonstrate that higher correlation between local saliency maps constructed from the same scene under different illumination conditions can be achieved using the TIGER model when compared to common local saliency approach, i.e., Laplacian of Gaussian, Difference of Gaussians, and Hessian saliency models.

}, keywords = {Bayesian estimation, illumination, illumination robust, local features, local saliency model, stochastic, texture}, author = {B Chwyl and A Chung and F Li and A Wong and D A. Clausi} } @inproceedings {405, title = {Pixel-based sea ice classification using the MAGSIC system}, booktitle = {International Society for Photogrammetry and Remote Sensing}, year = {2006}, address = {Enschede, The Netherlands}, abstract = {

MAGSIC is an operation-oriented system in development dedicated to map-guided classification of sea ice for navigation route planning and meteorological modeling. It has already produced promising results in difficult situations such as the Gulf of Saint-Lawrence in late winter. The Canadian Ice Service (CIS) produces ice maps made of large regions with relatively homogeneous concentrations of different ice types. MAGSIC uses the information of these maps to produce a pixel-based (rather than region-based) ice map by labeling a Markov random field (MRF) segmentation of RADARSAT-1 data along with its derived texture features. The system uses a novel implementation of MRF segmentation in combination with a unique labeling approach based on \“cognitive reasoning\”. Although reasonably successful, the system often had difficulties identifying ice type that required cues based on the shape recognition of large ice floes or leads. This article aims at thoroughly testing the MAGSIC system using validation data acquired during the \“2003 Gulf of Saint-Lawrence SAR Validation Field Program\” performed by CIS. Some new features were also added to MAGSIC and were evaluated. Results suggest a reasonable success and that the errors can be partially attributed to the generalized nature of the analysts ́interpretation and to the difficulties of obtaining concurring ground and image data. They suggest that classification metrics that can compare sample distributions were slightly superior for labeling purposes but this could not be confirmed statistically.

}, keywords = {Classification, navigation, Radarsat, Reasoning, Segmentation, Snow Ice, texture}, author = {P Maillard and D A. Clausi} } @article {389, title = {Preserving boundaries for image texture segmentation using grey level co-occurring probabilities}, journal = {Pattern Recognition}, volume = {39}, year = {2006}, pages = {234 - 235}, abstract = {

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries.

}, keywords = {Co-occurence probabilities, Co-occurrence matrix, Computer Vision, Digital imaging, Remote Sensing, sea ice, Segmentation, synthetic aperture radar, texture}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/j.patcog.2005.07.010}, url = {http://www.sciencedirect.com/science/article/pii/S0031320305002992}, author = {R Jobanputra and D A. Clausi} } @article {419, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {528 - 538}, chapter = {2940}, abstract = {

\—Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This paper uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.

}, keywords = {Classification, coocurrence, expectation-maximization (EM), Gamma distribution, intensity, K-means clustering, Markov random field (MRF), mixture model, Pattern Recognition, probabilities, sea ice, Segmentation, synthetic aperture radar (SAR), texture, unsupervised}, author = {H Deng and D A. Clausi} } @inproceedings {445, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using MRF models}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, month = {05/2004}, address = {London, Ontario, Canada}, abstract = {

Due to both environmental and sensor reasons, it is challenging to develop computer-assisted algorithms to segment SAR (synthetic aperture radar) sea ice imagery. In this research, images containing either ice and water or multiple ice classes are segmented. This paper proposes to use the image intensity to discriminate ice from water and to use texture features to separate different ice types. In order to seamlessly combine spatial relationship information in an ice image with various image features, a novel Bayesian segmentation approach is developed Experiments demonstrate that the proposed algorithm is able to segment both types of sea ice images and achieves an improvement over the standard MRF (Markov random field) based method, the finite Gamma mixture model and the K-means clustering method.

}, keywords = {expectation-maximization (EM), Gamma distribution, image feature, image segmentation, K-means clustering, Markov random field (MRF), mixture model, sea ice, synthetic aperture radar (SAR), texture, unsupervised segmentation}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301420}, author = {H Deng and D A. Clausi} } @article {813, title = {A comparison of two 85-GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {41}, year = {2003}, pages = {2294 - 2306}, abstract = {

Sea ice concentrations obtained with two algorithms from Special Sensor Microwave/Imager (SSM/I) data are compared to spaceborne visible/infrared and active microwave imagery for the Greenland Sea in spring. Both algorithms, the ARTIST Sea Ice algorithm (ASI) and the SEA LION algorithm (SLA), utilize 85-GHz SSM/I brightness temperatures with a spatial resolution of 15 km times;13 km. Ice concentrations obtained from Advanced Very High Resolution Radiometer (AVHRR) infrared data in cloud-free areas are underestimated by SLA and ASI ice concentrations by 3.6\% and 8.3\% (correlation coefficients of 0.90 and 0.91). Ice concentrations estimated from texture classified ERS-2 synthetic aperture radar (SAR) images by assigning experience-based ice concentrations to ice-type classes are overestimated by SLA and ASI ice concentrations by 4.4\% and 1.5\% (correlation coefficients of 0.84 and 0.77). However, omitting low/high ice concentrations forming up to 80\% (AVHRR) and 60\% (SAR) of the entire dataset reveals a significantly different statistic. For instance, the correlation between AVHRR and SLA and ASI ice concentrations drops to 0.77 and 0.70, respectively. All presented techniques to obtain ice concentrations need improvement and future developments should involve larger datasets. However, with care, both algorithms can be used to obtain reasonable ice concentration maps with a 12.5 km times;12.5 km grid-cell size.

}, keywords = {85 GHz, Advanced Very High Resolution Radiometer data, ARTIST sea ice algorithm, ASI, AVHRR imagery, brightness temperatures, Greenland Sea, ice-type classes, oceanographic techniques, radar imaging, remote sensing by radar, SAR imagery, sea ice, SEA LION algorithm, SLA, spaceborne imagery, spaceborne radar, Special Sensor Microwave/Imager data, Spring, SSM/I ice concentration algorithms, synthetic aperture radar, texture}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817181}, author = {S Kern and L Kaleschke and D A. Clausi} }