A Fast Corner Detector Based on the Chord-to-Point Distance Accumulation Technique

Awrangjeb, Mohammad; Lu, Guojun; Fraser, Clive S.; Ravanbakhsh, Mehdi

doi:10.1109/dicta.2009.91

Cited by 50 publications

(58 citation statements)

References 18 publications

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“…These boundary points are first smoothed using the Gaussian function and then corner detection is carried out using the technique in [39]. We determine the curvature peaks (red circles in Figure 11e) that specify the locations where a considerable change in curve direction occurs.…”

Section: Edge Line Estimationmentioning

confidence: 99%

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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Abstract:The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m 2 ), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over-and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.

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Section: Edge Line Estimationmentioning

confidence: 99%

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Gilani

Awrangjeb

2016

Remote Sensing

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“…Our definition is based on the image edges and thus requires edge extraction as a first step. But contrary to many existing methods based on edges, such as [5], we do not want to use the edge connectivity property. Indeed, edge connectivity is often compromised by various perturbations such as noise, occlusion, outliers and smoothing.…”

Section: Definition Of Interest Points Locationmentioning

confidence: 99%

“…A recent example of pixelbased algorithm is [4], and an edge-based algorithm is de- scribed in [5]. The here proposed algorithm is based on accumulation and thus belongs to the second class.…”

Section: Introductionmentioning

confidence: 99%

Robust 2D location of interest points by accumulation

Tarel

Belaroussi

2012

2012 19th IEEE International Conference on Image Processing

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Various interest point and corner definitions were proposed in the past with associated detection algorithms. We propose an intuitive and novel detection algorithm for finding the location of such features in an image. The detection is based on the edges in the original image. Interest points are detected as accumulation points where several edge tangent lines in a neighborhood are crossing. Edge connectivity is not used and thus detected interest points are robust to partial edges, outliers and edge extraction failures at junctions. One advantage of the approach is that detected interest points are not shifted in location when the original image is smoothed compared with other approaches. Experiments performed on Oxford and Cambridge reference databases allow us to show that the proposed detection algorithm performs better than 9 existing interest point detectors in terms of repeatability from multiple camera views.

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“…All chosen parameter values are decided based on existing work and empirical study [1,5]. Firstly, there are edge extraction and selection processes, that is to say, the Canny edge detector is used to extract planar curves from the grey image.…”

Section: Precise Detector By Use Of Chord-to-point Techniquementioning

confidence: 99%