A robust and fast line detection method based on Hough transform (HT) is proposed in this paper. Edge pixels are extracted based on the summation and ratio of principal curvatures. Probabilistic sampling on the edge pixels is applied to reduce the count of voting. Then a one-to-one voting strategy is applied by taking advantages of the information of principal direction. The principal direction is also conducive for the successive accurate line segment extraction. The experiments demonstrate that the proposed method shows better locating accuracy and computation efficiency compared with several significant variations of HT.Department of Electronic and Information Engineerin
In this work we present a novel kernel-based Hough Transform method for robust line detection in poo...
This paper explains how to associate a rigorous probability value to the main straight line feature...
In this paper, we introduce a novel technique for memory compression of the Hough transform. Our app...
Hough Transform (HT) is a widely used method for finding lines, circles and other image features by ...
AbstractThis paper deals with the problem of detecting every line component, a set of edge points cl...
[[abstract]]A modified Hough transform based on a likelihood principle of connectivity and thickness...
Although global voting schemes, such as the Hough Transform (HT), have been widely used to robustly ...
Hough Transform [1] has become a common method in the usage of line detection because of its robustn...
The detection of straight lines and other types of curves is a very important operation in digital i...
An important aspect of any scientific discipline is the objective and independent comparison of algo...
The Hough transform is a well-established scheme for detecting digital line components in a binary e...
Line detection is very important task in image processing field. It is mainly used in auto focusing ...
The application of Hough Transform (HT) has been limited to small-size images for a long time. For l...
Abstract The Hough Transform (HT) is an effective and popular technique for detecting image features...
In a wide range of image processing and computer vision problems, line segment detection is one of t...
In this work we present a novel kernel-based Hough Transform method for robust line detection in poo...
This paper explains how to associate a rigorous probability value to the main straight line feature...
In this paper, we introduce a novel technique for memory compression of the Hough transform. Our app...
Hough Transform (HT) is a widely used method for finding lines, circles and other image features by ...
AbstractThis paper deals with the problem of detecting every line component, a set of edge points cl...
[[abstract]]A modified Hough transform based on a likelihood principle of connectivity and thickness...
Although global voting schemes, such as the Hough Transform (HT), have been widely used to robustly ...
Hough Transform [1] has become a common method in the usage of line detection because of its robustn...
The detection of straight lines and other types of curves is a very important operation in digital i...
An important aspect of any scientific discipline is the objective and independent comparison of algo...
The Hough transform is a well-established scheme for detecting digital line components in a binary e...
Line detection is very important task in image processing field. It is mainly used in auto focusing ...
The application of Hough Transform (HT) has been limited to small-size images for a long time. For l...
Abstract The Hough Transform (HT) is an effective and popular technique for detecting image features...
In a wide range of image processing and computer vision problems, line segment detection is one of t...
In this work we present a novel kernel-based Hough Transform method for robust line detection in poo...
This paper explains how to associate a rigorous probability value to the main straight line feature...
In this paper, we introduce a novel technique for memory compression of the Hough transform. Our app...