Straight line extraction via multi-scale Hough transform based on pre-storage weight matrix

Considering the low efficiency and accuracy of existing straight line extraction methods from large-scale imagery, a multi-scale Hough transform (HT) method based on the pre-storage weight matrix is proposed. Improvements of the proposed HT include: using the pre-storage weight matrix to save storage space and improve the efficiency of the HT, applying a multi-scale method to enable the detection of smaller features from large-size images, using overlapping adjacent tiles to avoid the fragmented lines, optimizing the dimensions of the discrete transform domain, normalizing the accumulator to extract off-centred lines and using a dual threshold for distributed line detection. The experimental results show that the proposed algorithm is not only more efficient and robust but also can obtain richer and more accurate information about features, especially for large-size images.

[1]  Timothy Poston,et al.  Fuzzy Hough transform , 1994, Pattern Recognit. Lett..

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Allen R. Hanson,et al.  Extracting Straight Lines , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Opas Chutatape,et al.  Influence of discretization in image space on Hough transform , 1999, Pattern Recognit..

[5]  Michael R. Lyu,et al.  A Hough transform based line recognition method utilizing both parameter space and image space , 2005, Pattern Recognit..

[6]  Edward M. Riseman,et al.  Token-based extraction of straight lines , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Xiaoming Huo,et al.  JBEAM: multiscale curve coding via beamlets , 2005, IEEE Transactions on Image Processing.

[8]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[9]  Hungwen Li Fast Hough transform for multidimensional signal processing , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Francisco Sandoval Hernández,et al.  Mean shift based clustering of Hough domain for fast line segment detection , 2006, Pattern Recognit. Lett..

[11]  Vincent Leemans,et al.  Line cluster detection using a variant of the Hough transform for culture row localisation , 2006, Image Vis. Comput..

[12]  Kuo-Liang Chung,et al.  New memory- and computation-efficient hough transform for detecting lines , 2004, Pattern Recognit..

[13]  Josef Kittler,et al.  The Adaptive Hough Transform , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Mohammed Atiquzzaman,et al.  Multiresolution Hough Transform-An Efficient Method of Detecting Patterns in Images , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Simon Cox,et al.  Optimising the application of the hough transform for automatic feature extraction from geoscientific images , 1998 .

[16]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[17]  M. Mirmehdi,et al.  Label inspection using the Hough transform on transputer networks , 1991 .

[18]  A.L. Warrick,et al.  Detection of linear features using a localized Radon transform with a wavelet filter , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Shuhai Liu,et al.  Orthogonalized Fisher discriminant , 2005, Pattern Recognit..

[20]  Vladimir Shapiro Accuracy of the straight line Hough Transform: The non-voting approach , 2006, Comput. Vis. Image Underst..

[21]  Heikki Kälviäinen,et al.  Combination of Local and Global Line Extraction , 2000, Real Time Imaging.