Performance evaluation of corner detectors using consistency and accuracy measures

Mokhtarian, Farzin; Mohanna, Farahnaz

doi:10.1016/j.cviu.2005.11.001

Cited by 109 publications

(96 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Also, the points of high curvature do not necessarily correspond to visually significant ones [4,7,19,20]. These reasons motivate the use of quantitative measures that can relate detected points to ground-truth information to assess the performance of the corner detectors.…”

Section: Performance Evaluation Methodologymentioning

confidence: 99%

“…One of the most popular multiscale curvature representation of 2D curves is the curvature scalespace [17], which has been improved and applied in different works [1,3,11,18,19,20,25,33,35,36].…”

Section: The Curvature Space-scale Techniquementioning

confidence: 99%

“…According to Mokhtarian and Mohanna (2006), corners are important features in images and more relevant than other points in a shape. Also known as high curvature points (HCP), they are useful for shape description and furthermore represent an object in a compact manner, invariant to rotation and translation [24,29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

View full text Add to dashboard Cite

This paper presents a multiscale corner detection method in planar shapes, which applies an undecimated Mexican hat wavelet decomposition of the angulation signal to identify significant points on a shape contour. The advantage of using this wavelet is that it is well suited for detecting singularities as corners and contours due to its excellent selectivity in position. Thus, this wavelet plays an important role in our approach because it identifies changes in non-stationary angulation signals, and it can be extended to multidimensional approaches in an efficient way when approximating this wavelet by difference of Gaussian. The proposed algorithm detects peaks on a correlation signal which is generated from different wavelet scales and retains relevant points on the decomposed angulation signal while discards poor information. Our approach assumes that only peaks which persist through several scales correspond to corners. Furthermore, we introduce a novel procedure to tune parameters for the corner detection algorithms that corresponds to the best relation between Precision and Recall measures. This technique guides the parameter adjustment of the algorithms according to the image database and it improves their performance with regard to true corner detection. Concerning the performance assessment of the algorithms, we compare the proposed one to other corner detectors by using Precision and Recall measures which are based on ground-truth information. Tests were carried out using more than a hundred images from a non-homogenous database that contains noisy and non-noisy binary shapes 1

show abstract

Section: Performance Evaluation Methodologymentioning

confidence: 99%

Section: The Curvature Space-scale Techniquementioning

confidence: 99%

See 1 more Smart Citation

Multiscale Corner Detection in Planar Shapes

Paula

Medeiros

Bezerra³

et al. 2012

J Math Imaging Vis

View full text Add to dashboard Cite

show abstract

“…(2) where N o is the number of corners in the original image and N t ) is the number of corners in the transformed image (N t ). CCN should be close to 100% for accurate corner detectors [8].…”

Section: Experimental Comparison With Existing Methodsmentioning

confidence: 99%

“…The consistency of corner numbers (CCN) and accuracy (ACU) [8] were used for measuring the performance of corner detectors for the rotated images. (2) where N o is the number of corners in the original image and N t ) is the number of corners in the transformed image (N t ).…”

Section: Experimental Comparison With Existing Methodsmentioning

confidence: 99%

Automated generation of rotation-robust corner detectors

Seo

Kim

2010

IEICE Electron. Express

View full text Add to dashboard Cite

This paper introduces evolutionary generation method of robust corner detectors for rotated images. Previous Harris, SUSAN and FAST corner detectors are highly efficient for well-defined corners, but frequently mis-detect as corners the corner-like edges which are often generated in rotated images. In this paper, we have focused on this challenging problem and proposed using Genetic Programming to do automated generation of corner detectors that work robustly on rotated images. Especially, a well-devised terminal set is proposed based on intensity-related information, several mask sizes, and amount of contiguity of neighboring pixels of similar intensity. This method is then compared to three existing corner detectors on test images and shows superior results.

show abstract