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ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:6364 2018-11-17T17:47:15Z 1

Pyramidal segmentation using higher-order local auto-correlations and its applications to Landsat forestry data 2010 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/6364 M. Stojmenović T. Kobayashi N. Otsu The goal of image segmentation is to partition an image into regions that are internally homogeneous and heterogeneous with respect to neighbouring regions. Recently, a link shifting based pyramidal segmentation method was proposed to resolve existing problems with elongated regions. In this paper, we propose further improvements by replacing pixel intensities at the base level with pixel level higher order local auto-correlation (HLAC) feature vectors over greyscale, RGB, and CIV channels. Thereby, rich texture-like information is incorporated into segmentation. We propose a normalized distance formula between HLAC vectors, where each component contributes with physically same unit. The new algorithms were tested on a set of Landsat images over forested areas, and compared with a non-HLAC variant and several other existing segmentation algorithms. A significant improvement in segmentation conferenceObject 3033 3036 10.1109/ICIP.2010.5654101 17th IEEE International Conference on Image Processing (ICIP) 2010