@article {174, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @article {175, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @article {173, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} }