Multiple-order derivatives for detecting local image characteristics
Computer Vision, Graphics, and Image Processing
Visual reconstruction
The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A regularized solution to edge detection
Journal of Complexity
Geometric invariants and object recognition
International Journal of Computer Vision
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
ACM Transactions on Mathematical Software (TOMS)
Noise-Resistant Invariants of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Invariants For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing 3D Objects Using Tactile Sensing and Curve Invariants
Journal of Mathematical Imaging and Vision
Image and Vision Computing
Computer Vision and Image Understanding
Detection of noncircularity and eccentricity of a rolling winder by artificial vision
EURASIP Journal on Applied Signal Processing
Smoothing a Network of Planar Polygonal Lines Obtained with Vectorization
Graphics Recognition. Recent Advances and New Opportunities
Image Representation in Differential Space
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Hi-index | 0.14 |
Reliable derivatives of digital images have always been hard to obtain, especially (but not only) at high orders. We analyze the sources of errors in traditional filters, such as derivatives of the Gaussian, that are used for differentiation. We then study a class of filters which is much more suitable for our purpose, namely filters that preserve polynomials up to a given order. We show that the errors in differentiation can be corrected using these filters. We derive a condition for the validity domain of these filters, involving some characteristics of the filter and of the shape. Our experiments show a very good performance for smooth functions.