Abstract
Line segments play an important role in the perception and representation of images by providing the geometry information about the scene. These segments can also be used as low-level features to analyze and detect more elaborated shapes. A length-based line segment detector is proposed in this paper. Based on the edge proportion statistics, an adaptive, robust and effective edge detection method is presented to extract edge segments from the image. Each of the segments is a clean, contiguous, 1-pixel wide chain of pixels. The line segment detector then approximates all the edge segments in a way that the curve satisfies a criterion of length condition. The detection result is a series of piecewise-linear segments with some dominant corners. Experimental results indicate that the proposed detector has a good detection accuracy and outperforms the state-of-the-art methods in terms of execution time.
| Original language | English |
|---|---|
| Pages (from-to) | 247-254 |
| Number of pages | 8 |
| Journal | Pattern Recognition Letters |
| Volume | 128 |
| DOIs | |
| State | Published - 1 Dec 2019 |
Keywords
- Adaptive threshold
- Edge segment
- Helmholtz principle
- Length condition
- Line segment detection
- Real-time
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