Orientation-based discrete Hough transform for line detection with low computational complexity

2014 ◽  
Vol 237 ◽  
pp. 430-437 ◽  
Author(s):  
Kuo-Liang Chung ◽  
Yong-Huai Huang ◽  
Shiang-Ren Tsai
Keyword(s):  
Computational Complexity ◽  
Hough Transform ◽  
Line Detection ◽  
Low Computational Complexity

A NEWLY MODIFIED ALGORITHM OF HOUGH TRANSFORM FOR LINE DETECTION

2005 ◽  
Vol 05 (04) ◽  
pp. 715-727
Author(s):  
QIANG WANG ◽  
HONGBO CHEN ◽  
XIAORONG XU ◽  
HAIYAN LIU
Keyword(s):  
Computational Complexity ◽  
Digital Image ◽  
Hough Transform ◽  
Experimental Results ◽  
Line Detection ◽  
Storage Requirement ◽  
Heavy Burden ◽  
Modified Algorithm ◽  
Better Than

The heavy burden of computational complexity and massive storage requirement is the drawback of the standard Hough transform (SHT). To overcome the weakness of SHT, many modified approaches, for example, the probabilistic Hough transform (PHT), have been presented. However, a very important fact, which is that a line has its own width in a real digital image and the width of the line is uniform, was ignored by all of these modified algorithms of Hough transform. This phenomenon influenced the result of line detection. In this paper a new modified algorithm of Hough transform for line detection is proposed. In our algorithm, the fact mentioned above is fully considered and a strip-shaped area corresponding to the accumulate cells of HT is proposed. Experimental results have shown that our approach is efficient and promising, and the effect of detection is far better than the popular modified approaches.

The lane line detection based on the improved Hough transform

2014 ◽  
Author(s):  
Bo Yin ◽  
Haitao Wang ◽  
Yanping Cong
Keyword(s):  
Hough Transform ◽  
Line Detection ◽  
Lane Line

An Endurance-aware RAID-6 Code with Low Computational Complexity and Write Overhead

2021 ◽  
Author(s):  
Ningjing Liang ◽  
Xingjun Zhang ◽  
Xurui Wu ◽  
Heng Chen ◽  
Changjiang Zhang
Keyword(s):  
Computational Complexity ◽  
Low Computational Complexity

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

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