Abstract
The need for line detection in images is growing rapidly due to its importance in many image processing applications. The selection of an appropriate line detection method is essential for accurate detection of line pixels, but few studies provide an analytical basis for selecting a specific line detection method. In this study, to solve the problem, a method to analytically determine the signal-to-noise ratio (SNR) of line detection methods is proposed. Three line detection methods were selected for comparison: edge-detection (ED)-based, second derivative (SD)-based, and the sum of gradient angle differences (SGAD)-based line detection methods. Then, this study quantifies the SNR of the three line detectors through error propagation and signal noise coupling. In addition, the derived SNRs are graphically visualized to explicitly compare the performance of line detectors. Then, the quantified SNRs were validated by showing that they are highly correlated with the completeness and correctness observed in the experiment with a set of natural images. The experimental results show that the proposed SNR analysis can be used to select or design a suitable line detector.
Highlights
In the field of image processing, edges and lines are important features used to detect object shapes in a scene
Definition of sum of gradient angle differences (SGAD) According to the work in [6], the gradient angle at each pixel can be derived as follows: θi = tan−1 gri gci where gri and gci are the gradients at pixel i in the row and column directions, respectively
The performances of line detectors were evaluated by the analytical quantifications of their signal-to-noise ratio (SNR) and completeness and correctness
Summary
In the field of image processing, edges and lines are important features used to detect object shapes in a scene. Lines are the primary features observed in various types of images and are used to detect and recognize the appearance of objects in an image. By developing various applications, the demand for efficient line extraction methods is rapidly increasing [7,8,9,10,11,12,13]. Edge detection (ED) is used to indirectly extract line features [7,8,9,10,11,12], and different operators have been proposed for ED [14,15,16,17,18]. ED-based methods are often inefficient for detecting lines because they require the detection of edge pixels on both sides of the lines
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