73,622 research outputs found
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
In statistical model based texture feature extraction, features based on spatially varying parameters achievehigher discriminative performances compared to spatially constant parameters. In this paper we formulate anovel Bayesian framework which achieves texture characterization by spatially varying parameters based onGaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm.The distributions of estimated spatially varying parameters are then used as successful discriminant texturefeatures in classification and segmentation. Results show that novel features outperform traditional GaussianMarkov random field texture features which use spatially constant parameters. These features capture bothpixel spatial dependencies and structural properties of a texture giving improved texture features for effectivetexture classification and segmentation
Texture Segmentation by Evidence Gathering
A new approach to texture segmentation is presented which uses Local Binary Pattern data to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class and an accumulator is used to store votes for each texture class. Tests on the Brodatz database and Berkeley Segmentation Dataset have shown that our algorithm provides excellent results; an average of 86.9% was achieved over 50 tests on 27 Brodatz textures compared with 80.3% achieved by segmentation by histogram comparison centred on each pixel. In addition, our results provide noticeably smoother texture boundaries and reduced noise within texture regions. The concept is also a "higher order" texture descriptor, whereby the arrangement of texture elements is used for classification as well as the frequency of occurrence that is featured in standard texture operators. This results in a unique descriptor for each texture class based on the structure of texture elements within the image, which leads to a homogeneous segmentation, in boundary and area, of texture by this new technique
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images
Texture descriptors applied to digital mammography
Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version
Active Contour Models for Manifold Valued Image Segmentation
Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize an objective function designed such that the desired
object boundary is the optimal solution. Recently, imaging modalities that
produce Manifold valued images have come up, for example, DT-MRI images, vector
fields. The traditional active contour model does not work on such images. In
this paper, we generalize the active contour model to work on Manifold valued
images. As expected, our algorithm detects regions with similar Manifold values
in the image. Our algorithm also produces expected results on usual gray-scale
images, since these are nothing but trivial examples of Manifold valued images.
As another application of our general active contour model, we perform texture
segmentation on gray-scale images by first creating an appropriate Manifold
valued image. We demonstrate segmentation results for manifold valued images
and texture images
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to
many applications. The present contribution focuses on the particular subset of
scale-free textures and its originality resides in the combination of three key
ingredients: First, texture characterization relies on the concept of local
regularity ; Second, estimation of local regularity is based on new multiscale
quantities referred to as wavelet leaders ; Third, segmentation from local
regularity faces a fundamental bias variance trade-off: In nature, local
regularity estimation shows high variability that impairs the detection of
changes, while a posteriori smoothing of regularity estimates precludes from
locating correctly changes. Instead, the present contribution proposes several
variational problem formulations based on total variation and proximal
resolutions that effectively circumvent this trade-off. Estimation and
segmentation performance for the proposed procedures are quantified and
compared on synthetic as well as on real-world textures
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