A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Shape Classification Using Contour Matching in Distance Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Detection of Corners of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image analysis with two-dimensional continuous wavelet transform
Signal Processing
Scale-Space Derived From B-Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Using Functions to Describe the Shape
Journal of Mathematical Imaging and Vision
Gesture Spotting in Low-Quality Video with Features Based on Curvature Scale Space
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Multiscale contour corner detection based on local natural scale and wavelet transform
Image and Vision Computing
Direct Curvature Scale Space: Theory and Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Improved Method of Angle Detection on Digital Curves
IEEE Transactions on Computers
Novel Similarity Measures for Differential Invariant Descriptors for Generic Object Retrieval
Journal of Mathematical Imaging and Vision
Robust image corner detection based on scale evolution difference of planar curves
Pattern Recognition Letters
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Scale-Space Behavior of Planar-Curve Corners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Angle Detection on Digital Curves
IEEE Transactions on Computers
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Faster and Better: A Machine Learning Approach to Corner Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner detection based on gradient correlation matrices of planar curves
Pattern Recognition
Anisotropic diffusion for effective shape corner point detection
Pattern Recognition Letters
Corner Detection within a Multiscale Framework
SIBGRAPI '11 Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images
Representing images using points on image surfaces
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multiscale corner detection by using wavelet transform
IEEE Transactions on Image Processing
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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 Gaussians. 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.