DSeg: Direct Line Segments Detection
This work addresses image processing challenges for computer vision applications, but it is incremental as it builds on existing model-driven techniques.
The paper tackles the problem of detecting line segments in images by introducing a model-driven approach using a linear Kalman filter, which results in a fast and robust algorithm that detects longer segments than data-driven methods without requiring parameter tuning.
This paper presents a model-driven approach to detect image line segments. The approach incrementally detects segments on the gradient image using a linear Kalman filter that estimates the supporting line parameters and their associated variances. The algorithm is fast and robust with respect to image noise and illumination variations, it allows the detection of longer line segments than data-driven approaches, and does not require any tedious parameters tuning. An extension of the algorithm that exploits a pyramidal approach to enhance the quality of results is proposed. Results with varying scene illumination and comparisons to classic existing approaches are presented.