Horizon Line Detection in Historical Terrestrial Images in Mountainous Terrain Based on the Region Covariance

Horizon line detection is an important prerequisite for numerous tasks including the automatic estimation of the unknown camera parameters for images taken in mountainous terrain. In contrast to modern images, historical photographs contain no color information and have reduced image quality. In particular, missing color information in combination with high alpine terrain, partly covered with snow or glaciers, poses a challenge for automatic horizon detection. Therefore, a robust and accurate approach for horizon line detection in historical monochrome images in mountainous terrain was developed. For the detection of potential horizon pixels, an edge detector is learned based on the region covariance as texture descriptor. In combination with shortest path search the horizon in monochrome images is accurately detected. We evaluated our approach on 250 selected historical monochrome images in average dating back to 1950. In 85% of the images the horizon was detected with an error less than 10 pixels. In order to further evaluate the performance, an additional dataset consisting of modern color images was used. Our method, using only grayscale information, achieves comparable results with methods based on color information. In comparison with other methods using only grayscale information, accuracy of the detected horizons is significantly improved. Furthermore, the influence of color, choice of neighborhood for the shortest path calculation, and patch size for the calculation of the region covariance were investigated. The results show that both the availability of color information and increasing the patch size for the calculation of the region covariance improve the accuracy of the detected horizons.

Efficient Horizon Line Detection using Clustering and Fast Marching Method

Video analysis of maritime scenarios typically includes detection of horizon line for reference. The horizon line is the imaginary line, which separates water and sky as well as water and land. The horizon line plays a major role in terms of demarcating the water region in the video frame for further analysis. Considerable research has been aimed at horizon line detection. Various approaches have been reported including (i) Canny based edge detection followed by Hough transform, (ii) machine learning combined with statistical methods. However, the Hough transform has several limitations, in terms excessive analysis time, deviation of estimated line from the actual horizon line, sensitivity to presence of floating objects on the horizon, error due to presence of large number of edges. Present paper describes an efficient method for detecting the horizon line for analysis videos obtained by cameras mounted on floating vessels such as unmanned surface vehicle in maritime and inland scenarios. The proposed method is based on K-means clustering followed by seed based region growing using Fast Marching Method. For detecting the horizon line, two clusters are used in water-sky region like in marine environment images whereas three or more clusters are used in water-land-sky region like in in-land rivers/lakes images. In most cases, the upper part of the frame belongs to sky region whereas lower part belongs to water region. After K means clustering, based on the selection of seed point in lower part of the frame, the water region is segmented using fast marching method from non water regions and hence the horizon line is detected. This proposed method performance is compared with edge detection followed by Hough transform for different datasets. Experimental results show that the proposed method detects efficient line without compromising the processing time

Coarse-Fine-Stitched: A Robust Maritime Horizon Line Detection Method for Unmanned Surface Vehicle Applications

The horizon line has numerous applications for an unmanned surface vehicles (USV), such as autonomous navigation, attitude estimation, obstacle detection and target tracking. However, maritime horizon line detection is quite a challenging problem. The pixel points of the horizon line features are far fewer than the pixel points of the entire image, on the one hand. Conversely, the detection results might be impacted negatively by the complex maritime environment, waves, light changing, and partial occlusions due to maritime vessels or islands, for example. To solve these problems, a robust horizon line detection method named coarse-fine-stitched (CFS) is proposed in this paper. First, in the coarse step of CFS, a line segment detection approach using gradient features is applied to build a line candidate pool, which probably contains many false detection results. Then, hybrid feature filtering is designed to pick the horizon line segments from the pool in the fine step. Finally, the fine line segments are stitched to obtain the whole horizon line based on random sample consensus (RANSAC). Using real data in the maritime environment, the experimental results demonstrate the effectiveness of CFS, compared to the existing methods in terms of accuracy and robustness.