Efficient, recursively implemented differential operator, with application to edge detection
Pattern Recognition Letters
A new edge detector based on Fresnel diffraction
Pattern Recognition Letters
A novel approach for edge detection based on the theory of universal gravity
Pattern Recognition
Expert Systems with Applications: An International Journal
Feature controlled adaptive difference operators
Discrete Applied Mathematics
Geometric active contours without re-initialization for image segmentation
Pattern Recognition
A novel iris segmentation using radial-suppression edge detection
Signal Processing
Computer Vision and Image Understanding
Multiresolution local polynomial regression: A new approach to pointwise spatial adaptation
Digital Signal Processing
Smoothed differentiation filters for images
Journal of Visual Communication and Image Representation
Multi-edge detection by isotropical 2-D ISEF cascade
Pattern Recognition
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
A new algorithm to extract the lines and edges through orthogonal projections
Digital Signal Processing
Tree species recognition with fuzzy texture parameters
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Improving difference operators by local feature detection
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
Automatic closed edge detection using level lines selection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Quantitative error measures for edge detection
Pattern Recognition
On the impact of anisotropic diffusion on edge detection
Pattern Recognition
Hi-index | 0.15 |
We use the facet model to accomplish step edge detection. The essence of the facet model is that any analysis made on the basis of the pixel values in some neighborhood has its final authoritative interpretation relative to the underlying gray tone intensity surface of which the neighborhood pixel values are observed noisy samples. With regard to edge detection, we define an edge to occur in a pixel if and only if there is some point in the pixel's area having a negatively sloped zero crossing of the second directional derivative taken in the direction of a nonzero gradient at the pixel's center. Thus, to determine whether or not a pixel should be marked as a step edge pixel, its underlying gray tone intensity surface must be estimated on the basis of the pixels in its neighborhood. For this, we use a functional form consisting of a linear combination of the tensor products of discrete orthogonal polynomials of up to degree three. The appropriate directional derivatives are easily computed from this kind of a function. Upon comparing the performance of this zero crossing of second directional derivative operator with the Prewitt gradient operator and the Marr-Hildreth zero crossing of the Laplacian operator, we find that it is the best performer; next is the Prewitt gradient operator. The Marr-Hildreth zero crossing of the Laplacian operator performs the worst.