An optimal linear operator for step edge detection
CVGIP: Graphical Models and Image Processing
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Kernels on bags for multi-object database retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction and integration of window in a 3D building model from ground view images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In the past few years, street-level geoviewers has become a very popular web-application. In this paper, we focus on a first urban concept which has been identified as useful for indexing then retrieving a building or a location in a city: the windows. The work can be divided into three successive processes: first, object detection, then object characterization, finally similarity function design (kernel design). Contours seem intuitively relevant to hold architecture information from building facades. We first provide a robust window detector for our unconstrained data, present some results and compare our method with the reference one. Then, we represent objects by fragments of contours and a relational graph on these contour fragments. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside the structure set as well as spatial proximity. One difficulty to evaluate the relevance of our approach is that there is no reference database available. We made, thus, our own dataset. The results are quite encouraging regarding what was expected and what provide methods the literature.