TY - GEN
T1 - Knowledge-based method to recognize objects in geo-images
AU - Levachkine, Serguei
AU - Torres, Miguel
AU - Moreno, Marco
AU - Quintero, Rolando
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2004.
PY - 2004
Y1 - 2004
N2 - We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic) using knowledge-based learning and self-learning system. This approach exploits the user’s experience providing the knowledge domain in the form of the prescribed feature-attribute set. That 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 (composites). Every composite is associated with certain image feature. Some of the composites 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. Additionally, we describe the fine-to-coarse scale method of the raster-to-vector conversion in which the “knowledge” of cartographic patterns into small-scale map aids in recognizing the corresponding patterns into large-scale map of the same territory. The results of gray-level and color image segmentation-recognitionvectorization are shown.
AB - We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic) using knowledge-based learning and self-learning system. This approach exploits the user’s experience providing the knowledge domain in the form of the prescribed feature-attribute set. That 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 (composites). Every composite is associated with certain image feature. Some of the composites 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. Additionally, we describe the fine-to-coarse scale method of the raster-to-vector conversion in which the “knowledge” of cartographic patterns into small-scale map aids in recognizing the corresponding patterns into large-scale map of the same territory. The results of gray-level and color image segmentation-recognitionvectorization are shown.
UR - http://www.scopus.com/inward/record.url?scp=84975521689&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30134-9_96
DO - 10.1007/978-3-540-30134-9_96
M3 - Contribución a la conferencia
AN - SCOPUS:84975521689
SN - 9783540232056
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 718
EP - 725
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Negoita, Mircea Gh.
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
PB - Springer Verlag
T2 - 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004
Y2 - 20 September 2004 through 25 September 2004
ER -
