A performance review of recent corner detectors
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: International Conference on Digital Image Computing: Techniques and Applications, 26 November 2013 to 28 November 2013 p. 157-164
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- Description: Contour-based corner detectors directly or indirectly estimate a significance measure (eg, curvature) on the points of a planar curve and select the curvature extrema points as corners. A number of promising contour-based corner detectors have recently been proposed. They mainly differ in how the curvature is estimated on each point of the given curve. As the curvature on a digital curve can only be approximated, it is important to estimate a curvature that remains stable against significant noises, for example, geometric transformations and compression, on the curve. Moreover, in many applications, for instance, in content-based image retrieval, a fast corner detector is a prerequisite. So, it is also a primary characteristic that how much time a corner detector takes for corner detection in a given image. In addition, different authors evaluated their detectors on different platforms using different evaluation systems. Evaluation systems that depend on human judgements and visual identification of corners are manual and too subjective. Application of a manual system on a large test database will be expensive. Therefore, it is important to evaluate the detectors on a common platform using an automatic evaluation system. This paper first reviews six most recent and highly performed corner detectors and analyse their theoretical running time. Then it uses an automatic evaluation system to analyse their performance. Both the robustness to noise and efficiency are estimated to rank the detectors.
Building roof plane extraction from LIDAR data
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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- Description: This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.
A comparative study on contour-based corner detectors
- Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive
- Date: 2010
- Type: Text , Conference paper
- Relation: Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference
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- Description: Contour-based corner detectors directly or indirectly estimate a significance measure (e.g. curvature) on the points of a planar curve and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed promising detectors. This paper is an attempt to fill this gap. We present the general frame-work of the contour-based corner detection technique and discuss two major issues - curve smoothing and curvature estimation, which have major impacts on the corner detection performance. A number of promising detectors are compared using an automatic evaluation system on a common large dataset. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.
A fast corner detector based on the chord-to-point distance accumulation technique
- Authors: Awrangjeb, Mohammad , Lu, Guojun , Fraser, Clive , Ravanbakhsh, Mehdi
- Date: 2009
- Type: Text , Conference paper
- Relation: Digital Image Computing: Techniques and Applications, 2009. DICTA '09.
- Full Text: false
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- Description: Abstract—The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.