Segmentation

Image segmentation is an extremely wide topic of Computer Vision. Its definition is not univocal and is therefore commonly expressed in very generic terms, like "a process aimed to partition an image in regions which are homogeneous with respect to given visual properties". Furthermore, image segmentation is often confused with the contour detection problem. This is due to the lack of a clear cut between these two specular topics. Applicability to a large variety of domains, like remote sensing, security, automation and medical imaging, to mention a few, has contributed to enlarge the set of different solutions available in literature.

The GRIP team has expereinced the design of image segmentation algorithms for different applications since '99. In this page you can found demos, codes and references of some representative GRIP projects on image segmentations:

  • MR-EMFMulti-Resolution Edge, Mark and Fill algorithm: it is particular version of the watershed-based method EMF, specifically designed for multi-resolution remote sensing images, like Ikonos, GeoEye, WorldView.
  • TFR, Texture Fragmentation and Reconstruction algorithm.

It follows a complete list of the GRIP publications on image segmentation.

AuthorsTitleJournal/ConferenceDateFile
R. Gaetano, G. Masi, G. Poggi, L. Verdoliva and G. ScarpaMarker-controlled watershed-based segmentation of multiresolution remote-sensing imagesIEEE Transactions on Geoscience and Remote Sensing June 2015 PDF
R. Gaetano, D. Amitrano, G. Masi, G. Poggi, G. Ruello, L. Verdoliva and G. ScarpaExploration of multitemporal COSMO-SkyMed data via interactive tree-structured MRF segmentationIEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 2014 PDF
R. Gaetano, D. Amitrano, G. Masi, G. Poggi, G. Ruello, L. Verdoliva and G. ScarpaInteractive segmentation of high resolution synthetic aperture radar data by tree-structured mrfIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2014 PDF
G. Masi, G. Scarpa, R. Gaetano and G. PoggiA Watershed-Based Segmentation Technique for Multiresolution DataInternational Conference on Image Analysis and Processing (ICIAP) 2013 PDF
G. Scarpa, G. Masi, R. Gaetano, L. Verdoliva and G. PoggiDynamic Hierarchical Segmentation of Remote Sensing ImagesInternational Conference on Image Analysis and Processing (ICIAP) 2013 PDF
R. Gaetano, G. Masi, G. Scarpa and G. PoggiA marker-controlled watershed segmentation: Edge, mark and fillIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2012 PDF
S. Mikes, M. Haindl and G. ScarpaRemote sensing segmentation benchmarkIAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) 2012 PDF
G. Scarpa, G. Masi, L. Verdoliva, G. Poggi and R. GaetanoRecursive-TFR Algorithm for Segmentation of Remotely Sensed ImagesInternational Conference on Signal Image Technology and Internet Based Systems (SITIS) 2012 PDF
R. Gaetano, J. Zerubia, G. Scarpa and G. PoggiMorphological road segmentation in urban areas from high resolution satellite imagesInternational Conference on Digital Signal Processing (DSP) 2011 PDF
R. Gaetano, G. Scarpa and T. SzirányiGraph-based Analysis of Textured Images for Hierarchical SegmentationBritish Machine Vision Conference (BMVC) 2010 PDF
G. Masi, R. Gaetano, G. Scarpa and G. PoggiDynamic segmentation for image information miningIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2010 PDF
R. Gaetano, G. Scarpa and G. PoggiRecursive Texture Fragmentation and Reconstruction segmentation algorithm applied to VHR imagesIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2009 PDF
R. Gaetano, G. Scarpa and G. PoggiHierarchical texture-based segmentation of multiresolution remote-sensing imagesIEEE Transactions on Geoscience and Remote Sensing 2009 PDF
G. Scarpa, R. Gaetano, M. Haindl and J. ZerubiaHierarchical multiple Markov chain model for unsupervised texture segmentationIEEE Transactions on Image Processing 2009 PDF
R. Gaetano, G. Scarpa and G. PoggiAdvances in texture-based segmentation of high resolution remote sensing imageryIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2009 PDF
G. Moser, R. Gaetano, G. Poggi, G. Scarpa and S. B. SerpicoContextual Classification of Multisensor Optical-SAR Remote Sensing ImagesHandbook of pattern recognition and computer vision 2009 PDF
R. Gaetano, G. Scarpa and G. PoggiTexture-Based Segmentation of Very High Resolution Remote-Sensing ImagesInternational Conference on Intelligent Systems Design and Applications (ISDA) 2009 PDF
R. Gaetano, G. Moser, G. Poggi, G. Scarpa and S. B. SerpicoRegion-based classification of multisensor optical-SAR imagesIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2008 PDF
R. Gaetano, G. Scarpa, G. Poggi and J. ZerubiaUnsupervised hierarchical image segmentation based on the TS-MRF model and fast mean-shift clusteringEuropean Signal Processing Conference (EUSIPCO) 2008 PDF
G. Scarpa, R. Gaetano and G. PoggiTexture image segmentation by hierarchical modelingEuropean Signal Processing Conference (EUSIPCO) 2008 PDF
M. Haindl, S. Mikes and G. ScarpaUnsupervised Detection of Mammogram Regions of InterestKnowledge-Based Intelligent Information and Engineering Systems (KES) 2007 PDF
R. Gaetano, G. Scarpa and G. PoggiA hierarchical segmentation algorithm for multiresolution satellite imagesIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2007 PDF
G. Scarpa, M. Haindl and J. ZerubiaA hierarchical texture model for unsupervised segmentation of remotely sensed imagesScandinavian Conference on Image Analysis (SCIA) 2007 PDF
R. Gaetano, G. Poggi and G. ScarpaHierarchical MRF-based segmentation of remote-sensing imagesIEEE International Conference on Image Processing (ICIP) 2006 PDF
G. Scarpa and M. HaindlUnsupervised Texture Segmentation by Spectral-Spatial-Independent ClusteringInternational Conference on Pattern Recognition (ICPR) 2006 PDF
R. Gaetano, G. Poggi and G. ScarpaIdentification of image structure by the Mean Shift procedure for hierarchical MRF-based image segmentationEuropean Signal Processing Conference (EUSIPCO) 2006 PDF
G. Poggi, G. Scarpa and J. ZerubiaSupervised segmentation of remote sensing images based on a tree-structured MRF modelIEEE Transactions on Geoscience and Remote Sensing 2005 PDF
R. Gaetano, G. Poggi and G. ScarpaMultitemporal image classification with automatic building of tree-structured MRF modelsEuropean Signal Processing Conference (EUSIPCO) 2005 PDF
L. Cicala, G. Poggi and G. ScarpaSupervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field modelIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2004 PDF
G. Poggi, G. Scarpa and J. ZerubiaSegmentation of remote-sensing images by supervised TS-MRFIEEE International Conference on Image Processing (ICIP) 2004 PDF
C. D'Elia, C. Marrocco, M. Molinara, G. Poggi, G. Scarpa and F. TortorellaDetection of microcalcifications clusters in mammograms through TS-MRF segmentation and SVM-based classificationInternational Conference on Pattern Recognition (ICPR) 2004 PDF
C. D'Elia, G. Poggi and G. ScarpaA tree-structured Markov random field model for Bayesian image segmentationIEEE Transactions on Image Processing 2003 PDF
C. D'Elia and G. ScarpaContour-Based Shape Representation for Image Compression and AnalysisDiscrete Geometry for Computer Imagery 2003 PDF
C. D'Elia, G. Poggi and G. ScarpaImproved tree-structured segmentation of remote sensing imagesIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2003 PDF
C. D'Elia, G. Poggi and G. ScarpaSequential Bayesian segmentation of remote sensing imagesIEEE International Conference on Image Processing (ICIP) 2003 PDF
C. D'Elia, G. Poggi and G. ScarpaAn adaptive MRF model for boundary-preserving segmentation of multi-spectral imagesEuropean Signal Processing Conference (EUSIPCO) 2002 PDF
C. D'Elia, G. Poggi and G. ScarpaAdvances in the segmentation and compression of multispectral imagesIEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2001 PDF
G. Poggi and A. R. P. RagoziniImage Segmentation by Tree-Structured Markov Random FieldsIEEE Signal Processing Letters 1999 PDF