TY - GEN
T1 - Applying local cooccurring patterns for object detection from aerial images
AU - Jia, Wenjing
AU - Tien, David
AU - He, Xiangjian
AU - Hope, Brian A.
AU - Wu, Qiang
PY - 2007
Y1 - 2007
N2 - Developing a spatial searching tool to enhance the search car pabilities of large spatial repositories for Geographical Information System (GIS) update has attracted more and more attention. Typically, objects to be detected are represented by many local features or local parts. Testing images are processed by extracting local features which are then matched with the object's model image. Most existing work that uses local features assumes that each of the local features is independent to each other. However, in many cases, this is not true. In this paper, a method of applying the local cooccurring patterns to disclose the cooccurring relationships between local features for object detection is presented. Features including colour features and edge-based shape features of the interested object are collected. To reveal the cooccurring patterns among multiple local features, a colour cooccurrence histogram is constructed and used to search objects of interest from target images. The method is demonstrated in detecting swimming pools from aerial images. Our experimental results show the feasibility of using this method for effectively reducing the labour work in finding man-made objects of interest from aerial images.
AB - Developing a spatial searching tool to enhance the search car pabilities of large spatial repositories for Geographical Information System (GIS) update has attracted more and more attention. Typically, objects to be detected are represented by many local features or local parts. Testing images are processed by extracting local features which are then matched with the object's model image. Most existing work that uses local features assumes that each of the local features is independent to each other. However, in many cases, this is not true. In this paper, a method of applying the local cooccurring patterns to disclose the cooccurring relationships between local features for object detection is presented. Features including colour features and edge-based shape features of the interested object are collected. To reveal the cooccurring patterns among multiple local features, a colour cooccurrence histogram is constructed and used to search objects of interest from target images. The method is demonstrated in detecting swimming pools from aerial images. Our experimental results show the feasibility of using this method for effectively reducing the labour work in finding man-made objects of interest from aerial images.
KW - Colour cooccurrence histogram
KW - Local cooccurring patterns
KW - Swimming pool detection
UR - http://www.scopus.com/inward/record.url?scp=38349032613&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76414-4_47
DO - 10.1007/978-3-540-76414-4_47
M3 - Conference contribution
AN - SCOPUS:38349032613
SN - 9783540764137
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 478
EP - 489
BT - Advances in Visual Information Systems - 9th International Conference, VISUAL 2007, Revised Selected Papers
PB - Springer Verlag
T2 - 9th International Conference on Visual Information Systems, VISUAL 2007
Y2 - 28 June 2007 through 29 June 2007
ER -