Authors:
Thales Sehn Korting
;
Luciano Vieira Dutra
and
Leila Maria Garcia Fonseca
Affiliation:
National Institute for Space Research (INPE), Brazil
Keyword(s):
Re-Segmentation, Graph-Based Segmentation, Remote Sensing, Urban Imagery.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
Image segmentation is a broad area, which covers strategies for splitting one input image into its components. This paper aims to present a re-segmentation approach applied to urban imagery, where the interest elements (houses roofs) are considered to have a rectangular shape. Our technique finds and generates rectangular objects, leaving the remaining objects as background. With an over-segmented image we connect adjacent objects in a graph structure, known as Region Adjacency Graph - RAG. We then go into the graph, searching for best cuts that may result in segments more rectangular, in a relaxation-like approach. Graph search considers information about object class, through a pre-classification stage using Self-Organizing Maps algorithm. Results show that the method was able to find rectangular elements, according user-defined parameters, such as maximum levels of graph searching and minimum degree of rectangularity for interest objects.