A Fast shape detection approach by directional integrations

Okman, Osman Erman
Detection and identification of objects from aerial images are important problems for various types of application areas. For many of the man-made structures shape is a fundamental feature by which these objects are separated from the background and other structures. In this thesis, a novel geometric shape detection algorithm based on the spatial properties of structures is proposed. Since the objects are transformed into 1-D vectors by evaluating directional integrals and detections occur by the analysis of the features obtained in those 1-D spaces, the proposed approach requires less memory and computation than most of the approaches reported in the literature. Detailed derivation of the method is given and the experimental results are presented in order to show the detection performance of the method under different amount of noise and geometric deformations. Experimental results on real images also show that the proposed approach can significantly speed up the computation without degrading the performance. Moreover, a Petroleum Oil Lubricants (POL) depots identification procedure in high-resolution satellite images is developed where detection of the circular structures is one of the crucial steps, which is achieved by the proposed shape detection approach. Performed experiments over a large data set imply promising identification performance and the usability of the shape detection approach in real world applications.


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The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...
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A great number of methods based on local features or global appearances have been proposed in the literature for geospatial object detection and recognition from satellite images. However, since these approaches do not have enough discriminative capabilities between object and non-object classes, they produce results with innumerable false positives during their detection process. Moreover, due to the sliding window mechanisms, these algorithms cannot yield exact location information for the detected object...
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Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of im...
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Citation Formats
O. E. Okman, “A Fast shape detection approach by directional integrations,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.