TY - CHAP
T1 - Simultaneous segmentation-recognition-vectorization of meaningful geographical objects in geo-images
AU - Levachkine, Serguei
AU - Torres, Miguel
AU - Moreno, Marco
AU - Quintero, Rolando
PY - 2003
Y1 - 2003
N2 - 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 vectoriztion are shown.
AB - 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 vectoriztion are shown.
UR - http://www.scopus.com/inward/record.url?scp=35048866192&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-24586-5_78
DO - 10.1007/978-3-540-24586-5_78
M3 - Capítulo
AN - SCOPUS:35048866192
SN - 354020590X
SN - 9783540205906
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 635
EP - 642
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Sanfeliu, Alberto
A2 - Ruiz-Shulcloper, Jose
PB - Springer Verlag
ER -