IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2006 Papers
SVD-matching using SIFT features
Graphical Models - Special issue on the vision, video and graphics conference 2005
Fast multiscale clustering and manifold identification
Pattern Recognition
Robust Image Segmentation Using Resampling and Shape Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Prior knowledge driven multiscale segmentation of brain MRI
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
Learning what and how of contextual models for scene labeling
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Segmentation subject to stitching constraints: finding many small structures in a large image
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
A mobile-oriented hand segmentation algorithm based on fuzzy multiscale aggregation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
Interactive video layer decomposition and matting
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Graph-Based fast image segmentation
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Statistical hypothesis testing and wavelet features for region segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Atlas guided identification of brain structures by combining 3d segmentation and SVM classification
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Scale consistent image completion
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Combined geometric-texture image classification
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Patch-Based texture edges and segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Recognition of occluded objects by reducing feature interactions
Image and Vision Computing
PatchNet: a patch-based image representation for interactive library-driven image editing
ACM Transactions on Graphics (TOG)
Break and conquer: efficient correlation clustering for image segmentation
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Efficient segmentation of leaves in semi-controlled conditions
Machine Vision and Applications
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Texture segmentation is a difficult problem, as is apparentfrom camouflage pictures. A Textured region can containtexture elements of various sizes, each of which can itselfbe textured. We approach this problem using a bottom-upaggregation framework that combines structural characteristicsof texture elements with filter responses. Our processadaptively identifies the shape of texture elements and characterizethem by their size, aspect ratio, orientation, brightness,etc., and then uses various statistics of these propertiesto distinguish between different textures. At the sametime our process uses the statistics of filter responses tocharacterize textures. In our process the shape measuresand the filter responses crosstalk extensively. In addition,a top-down cleaning process is applied to avoid mixing thestatistics of neighboring segments. We tested our algorithmon real images and demonstrate that it can accurately segmentregions that contain challenging textures.