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Image segmentation using texture information and graph cut on images modelled by means of hierachical structures

Grant number: 13/00575-3
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): April 01, 2013
Effective date (End): March 31, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal researcher:Marco Antonio Garcia de Carvalho
Grantee:Kauê Tartarotti Nepomuceno Duarte
Home Institution: Faculdade de Tecnologia (FT). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil


Image segmentation is an important task in many areas of image processing, computer vision, and pattern recognition. Subdivide an image into its constituent parts may, oftentimes, be dependent on the context where the segmentation is applied. There are many techniques of image segmentation, being the use of graphs as structures to represent images quite common. Thus, the study of graph cut, or graph partitioning, corresponds to the process of image segmentation. A recent method used to partition graphs comes from Spectral Graph Theory, in which an analysis is made of the eigenvectors from an array representing a graph. The goal of this project is to perform image segmentation using the graph normalized cut technique and which uses concepts of Spectral Graph Theory. It is intended that the modeling graph takes into account aspects related to pixel information and primitive regions, being used the feature texture in particular. The images are represented by different types of graphs and these will have their effectiveness and applicability compared with respect to the segmentation task. This project provides the following contributions: investigate and explore the digital image segmentation task through graph cut and Spectral Graph Theory using texture features in image representations and consolidate measures of performance evaluation and comparison of the results of segmentation obtained by different strategies of graph cut. This project will be developed in the Computing Visual Laboratory (IMAGELab) of the School of Technology at UNICAMP. (AU)

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