Fast Line Segment Detector Research Articles

Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces Detection in Farmlands

Among the most evident anthropogenic modifications of the landscape, terraces related to agricultural activities are ubiquitous structures that constitute important investments worldwide, and they recently acquired a new relevance to modern concerns about land-use management and erosion control. Conservation agriculture and terraces management are an application with great potentialities for Satellite Earth observation and the derived high-resolution topography. Due to its high agility, the Pleiades satellite constellation provides new, high-resolution digital elevation models (DEMs) with a submetric resolution that could be potentially useful for this task, and their application in a farmland context is nowadays an open research line. This work provides a first analysis, performing an automatic terrace mapping from DEMs obtained from Pleiades images, as compared to LiDAR DEMs. Two existing methods are considered: 1) the fast line segment detector (LSD) algorithm and 2) a geomorphometric method based on surface curvature. Despite the lower performances of Pleiades DEMs with respect to that of the LiDAR models, the results indicate that the Pleiades models can be used to automatically detect terrace slopes greater than 2 m with a detection rate of more than 80% of the total length of the terraces. In addition, the results showed that when using noisy DEMs, the geomorphometric method is more robust, and it slightly outperforms the LSD algorithm. These results provide a first analysis on how effective Pleiades DEMs can be as an alternative to LiDAR DEMs, also highlighting the future challenges for monitoring large extents in a farmland context.

A Novel Airport Detection Method via Line Segment Classification and Texture Classification

Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. First, a fast line segment detector is applied to extract the line segments in images and compute the features of these line segments. Then, the line segments are discriminated by a trained runway line classifier, and the regions of interest (ROIs) are extracted from the line segments, which are classified as runway lines. Finally, whether the ROI is actually an airport is determined by analyzing the classification results of the image blocks. This method is unique in terms of the computing of line segment features and line segment classification. Experimental results demonstrate the effectiveness and robustness of the proposed method.

Recognition of Airport Runways in FLIR Images Based on Knowledge

Airport runway recognition technology would play an important role in developing intelligent weapon systems in the future. In this letter, a method of automatically finding runways in forward looking infrared (FLIR) images is proposed based on the knowledge of vision. First, the line segments in the images are extracted by a fast line segment detector (LSD) and an improved line segment linking method. Then, the regions of interest (ROI) of runways are detected using some constraint rules based on the direction, gradient, and width of line segment pairs. Afterward, an ROI length backtracking technique based on texture distribution is presented to retrieve the complete ROI. Finally, using runway regional self-similarity and contextual information, several decision criteria are formulated to accurately recognize the runway. Experimental results on the FLIR images with different imaging ranges show that the proposed algorithm is robust and has a good real-time performance.

An extended flow-based difference-of-Gaussians method of line drawing for polyhedral image

This paper presents a non-photorealistic rendering technique for stylizing a photograph in the line drawing style. The flow-based difference-of-Gaussians (FDoG) filter constructs lines of style and quality, which usually used to extract the line drawing for some images. In this paper, we inspired by the polyhedral image feature (the line of which are mostly straight line), proposed the method based on the standard FDoG to extract the line drawing of polyhedral image. First, we detect line by fast line segment detector with a false detection control (LSD); then construct a smooth and coherent edge tangent flow (ETF) of the polyhedral images. On the basis of the ETF, the anisotropic FDOG is used to render the lines. This scheme gives clear line features for polyhedral images without much noise, which is also simple and easy to implement, and is robust. The experimental results prove that our algorithm is more efficient than the traditional FDOG approaches in render line drawing for polyhedral image.