Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images

  • Serguei Levachkine
  • Miguel Torres
  • Marco Moreno
  • Rolando Quintero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic). This is a simultaneous segmentation-recognition system when segmented geographical objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in same, but are different for each type of objects, gray-level values. We exchange the source image by a number of simplified images. These images are called composites. Every composite image is associated with certain image feature. Some of the composite images that contain the objects of interest are used in the following object detection-recognition by means of association to the segmented objects corresponding “names” from the user-defined subject domain. The specification of features and object names associated with perspective composite representations is regarded as a type of knowledge domain, which allows automatic or interactive system’s learning. The results of gray-level and color image segmentation-recognition and vectorization are shown.


Color Image Source Image Composite Image Invariant Moment Dynamic Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Serguei Levachkine
    • 1
  • Miguel Torres
    • 1
  • Marco Moreno
    • 1
  • Rolando Quintero
    • 1
  1. 1.Geoprocessing Laboratory (GEOLAB) Centre for Computing Research (CIC)National Polytechnic Institute (IPN) 

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