Sea ice information obtained from synthetic aperture radar (SAR) images is crucial for ensuring safe marine navigation and supporting climate change studies in polar regions. We propose a kernel principal component analysis (KPCA) local texture feature model for efficient sea ice segmentation. The proposed KPCA texture feature model is significant for several reasons. First, it takes into accou...

In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by ...

This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavel...

This study proposes a segmentation procedure based on multivariate texture to extract spatial objects from an image scene. Object uncertainty is quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, is integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image....

This paper deals with region based watershed segmentation for developing “MemberingFilters”.Watershed transformation is a common technique for image segmentation. Region based segmentation classify a particular image into a number of regions or classes. Region-based segmentation methods attempt to partition or group regions according to Intensity values from original images, Textures or pattern...

This paper deals with region based watershed segmentation for developing “MemberingFilters”.Watershed transformation is a common technique for image segmentation. Region based segmentation classify a particular image into a number of regions or classes. Regionbased segmentation methods attempt to partition or group regions according to Intensity values from original images, Textures or patterns...

This paper deals with region based watershed segmentation for developing “MemberingFilters”.Watershed transformation is a common technique for image segmentation. Region based segmentation classify a particular image into a number of regions or classes. Region-based segmentation methods attempt to partition or group regions according to Intensity values from original images, Textures or pattern...

There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characte...

We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multi{scale Gabor lter image representation. We discuss and compare a class of clustering obje...

Texture segmentation is a long standing problem in computer vision. In this paper, we propose an interactive framework for texture segmentation. Our framework has two advantages. One is that the user can define the textures to be segmented by labelling a small part of points belonging to them. The other is that the user can further improve the segmentation quality through a few interactive mani...