Procedural elements for computer graphics
Procedural elements for computer graphics
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pulse and staircase edge models
Computer Vision, Graphics, and Image Processing
Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line detection using an optimal IIR filter
Pattern Recognition
Scale and the differential structure of images
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A framework for low level feature extraction
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orientation Space Filtering for Multiple Orientation Line Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grammatical inference of dashed lines
Computer Vision and Image Understanding
Robust and efficient map-to-image registration with line segments
Machine Vision and Applications
Detection, Localization, and Estimation of Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Association of Sound to Motion in Video using Perceptual Organization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Line Detection and Texture Characterization of Network Patterns
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fast acquisition of dense depth data by a new structured light scheme
Computer Vision and Image Understanding
Definition of a model-based detector of curvilinear regions
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Multi-scale midline extraction using creaseness
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Detecting Wide Lines Using Isotropic Nonlinear Filtering
IEEE Transactions on Image Processing
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This paper presents an approach to extract curvilinear structures (lines) and their widths from two-dimensional images with high accuracy. Models for asymmetric parabolic and Gaussian line profiles are proposed. These types of lines occur frequently in applications. Scale-space descriptions of parabolic and Gaussian lines are derived in closed form. A detailed analysis of these scale-space descriptions shows that parabolic and Gaussian lines are biased more significantly than the well-known asymmetric bar-shaped lines by the partial derivatives of the Gaussian filters that are used to extract the lines. A bias function is constructed that relates the parameters of the lines to biased measurements that can be extracted from the image. It is shown that this bias function can be inverted. This is used to derive an algorithm to remove the bias from the line positions and widths. Examples on synthetic and real images show the high subpixel accuracy that can be achieved with the proposed algorithm. In particular, the line extractor is tested on a publicly available data set that includes manually labeled ground truth. The results on this data set show that very accurate results can be achieved on real data if the appropriate line model is used.