An algebraic model for fast corner detection

Willis, Andrew; Sui, Yunfeng

doi:10.1109/iccv.2009.5459443

Cited by 30 publications

(16 citation statements)

References 20 publications

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“…Certain characteristics can be assigned to these points such as intensity gradient and/or the intensity spread of adjacent pixels. Several corner point detecting algorithms can be used for that purpose including the most frequently used Harrisalgorithm [10,11]. If these characteristics are invariant to any magnification or rotation, there is a possibility for finding more corner points with similar characteristics in the overlapping point pair.…”

Section: Making 3d Models On Photogrammetric Basismentioning

confidence: 99%

3D Reconstruction of Monuments from Drone Photographs Based on The Spatial Reconstruction of The Photogrammetric Method

Molnár¹

2018

Adv. sci. technol. eng. syst. j.

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Due to their efficient flight control systems and their camera of high quality modern drones can fly precisely and take aerial photos of high resolution. Although these multi-rotor devices are not able to fly long distances yet, they are very efficient instruments for taking aerial photographs of their proximate environment. As they are able to float and rotate along their vertical axis to a discretionary direction, they can be used for monitoring and taking pictures of a building from several directions. If pictures are taken of a building from all directions with significant overlap (of at least 50%) the 3D reconstruction of the building in a photogrammetric way becomes possible. The reconstructed models do not only contain the spatial forms but also the visualinformation based on the snapshots. As a result of the entire reconstruction a virtual object is gained in the virtual space that can freely be accessed and visible from all directions by enlarging or reducing the size. A virtual collection can be established for monuments, historical buildings or other spectacular objects worth recording. The objects of the collection (buildings) are lifelike and can be perceived and studied by anyone. The 3D models, of course, cannot substitute the photographs of high resolution but they can complement them in the collection.

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Section: Making 3d Models On Photogrammetric Basismentioning

confidence: 99%

3D Reconstruction of Monuments from Drone Photographs Based on The Spatial Reconstruction of The Photogrammetric Method

Molnár¹

2018

Adv. sci. technol. eng. syst. j.

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“…[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Most of them are single-scale detectors. They can work well if the image has similar-size features, but are ineffective otherwise.…”

Section: Introductionmentioning

confidence: 99%

Adaptive algorithm for corner detecting based on the degree of sharpness of the contour

et al. 2011

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We propose an adaptive corner detector based on the degree of sharpness of the contour, which can detect the corners efficiently and accurately. First, we extract edges from an image using an edge detector, such as Canny, Sobel, etc. Second, we define a degree-of-sharpness variable and calculate its value for each point on the edge. Then we use an adaptive threshold of the degree of sharpness to find the initial corner candidates for each edge. Third, we propose a method of making a projection for an edge segment toward the edge's fitting line to remove salient points. Finally, if some neighboring corner candidates are detected in a support area, we offer an effective method to merge them into one point, and the final corners are reserved. The proposed detector is compared to a classical corner detector and two state-of-the-art methods on planar curves and gray-level images, respectively. The experimental results show that the proposed detector has high correctness in finding true corners. In addition, the algorithm is robust to the additive noise and can detect the circular contour well. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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“…While the synthetic shape instances of the same object completely overlap one another, avoiding bias to the repeatability score (Willis and Sui 2009), the real stereo data contains occlusions (the underside of each object, which varied across instances, was not captured), as well as uneven sampling density and generally more sampling noise. The applicability to real applications of our performance evaluation using synthetic data will therefore be tested by comparing the results on the Mesh dataset with those on the Stereo dataset.…”

Section: Test Datamentioning

confidence: 99%

“…The Heaviside step function H (·) returns 1 when the input is positive, 0 otherwise. This repeatability measure favours dense interest points over accurate but sparse interest points (Willis and Sui 2009). However, the fairness of our evaluation is not affected because fully-overlapped object pairs are used in the experiments.…”

Section: Repeatabilitymentioning

confidence: 99%

A Performance Evaluation of Volumetric 3D Interest Point Detectors

Woodford

Cipolla

2012

Int J Comput Vis

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This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application.

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An algebraic model for fast corner detection

Cited by 30 publications

References 20 publications

3D Reconstruction of Monuments from Drone Photographs Based on The Spatial Reconstruction of The Photogrammetric Method

3D Reconstruction of Monuments from Drone Photographs Based on The Spatial Reconstruction of The Photogrammetric Method

Adaptive algorithm for corner detecting based on the degree of sharpness of the contour

A Performance Evaluation of Volumetric 3D Interest Point Detectors

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