Algorithms for Straight Line Detection in Images: A Comprehensive Review

In this article, we present DSeg, a novel line segmentation algorithm that can handle noisy images with high accuracy and speed. Unlike traditional methods that rely on thresholding or edge detection, DSeg uses a probabilistic approach to identify lines in an image. It first detects seed points using the Hough transform and then grows segments based on their similarity. The algorithm is robust to various types of noise and can extract meaningful lines even in low-contrast environments.
The key innovation of DSeg lies in its ability to handle non-stationary noise, which is a common problem in image processing. Traditional methods struggle to accurately segment lines when the noise level varies across the image. DSeg addresses this issue by using a Kalman filter to refine the detection process and ensure that segments are accurately aligned with the true edges of the image.
We evaluate the performance of DSeg through experiments on various noisy images, comparing it to other state-of-the-art methods. Our results show that DSeg outperforms its competitors in terms of both accuracy and speed. Additionally, we demonstrate the robustness of DSeg by analyzing its performance under different noise conditions.
In summary, DSeg is a powerful line segmentation algorithm that can handle noisy images with ease. Its probabilistic approach ensures high accuracy even when the image contrast is low, while its ability to handle non-stationary noise makes it ideal for real-world applications. With its fast execution time and robust performance, DSeg is an excellent choice for any image processing task that requires accurate line segmentation.