Fusion of edge-less and edge-based approaches for horizon line detection

Ahmad, Touqeer; Bebis, George; Nicolescu, Mircea; Nefian, Ara; Fong, Terry

doi:10.1109/iisa.2015.7387988

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…It contains modern photographs of mountainous terrain including challenging situations like cloud occlusions, low contrast horizons, and images taken from within cable cars (Figure 5). The dataset was used by a wide range of authors (e.g., [16,31]) for evaluation. While it consists of color images, for the developed method, the grayscale images are derived by averaging the color channels.…”

Section: Ch1mentioning

confidence: 99%

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

Mikolka-Flöry

Pfeifer

2021

Remote Sensing

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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.

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Section: Ch1mentioning

confidence: 99%

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

Mikolka-Flöry

Pfeifer

2021

Remote Sensing

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“…is mostly not answered. This is true with the exception of few [2], [27], [28], [1], [29] who have reported such measures. For example; Saurer et al [15] reported full automatic detection for 60% of their images (total 948), however how good the detections were for these images was never mentioned.…”

Section: B Paper Contributionmentioning

confidence: 99%

“…The body of work on truly automatic non-linear horizon/sky line detection is rather limited and work comparing such methods is even rarer with the exception of Ahmad et al who compare different formulation [26], [27], [28] of their approach against original approach of [4]. And quite recently the work by Porzi et al [29]; who proposed a small scale deeplearning architecture inspired from VGG [31] for horizon line detection.…”

Section: B Paper Contributionmentioning

confidence: 99%

Comparison of semantic segmentation approaches for horizon/sky line detection

Ahmad

Campr

Čadík

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on user-in-the-loop skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection [2], second focused on visual geolocalization but relying on accurate detection of skyline [15] and other two proposed for general semantic segmentation -Fully Convolutional Networks (FCN) [21] and SegNet [22]. Each of the first two methods is trained on a common training set [11] comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.

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“…Due to the unavailability of catadioptric mirrors, the authors manually synthesize the catadioptric images. Ahmad et al 7 present a detailed comparison between two machine learning-based approaches for horizon line detection (edge and edgeless-based). Additionally, they propose a fusion strategy, combining both approaches for better results, but they fail to provide attitude estimations using the detected horizon.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Attitude estimation using horizon detection in thermal images

Carrio

Bavle

Campoy

2018

International Journal of Micro Air Vehicles

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The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots' training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7 and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities.

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Fusion of edge-less and edge-based approaches for horizon line detection

Cited by 12 publications

References 25 publications

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

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

Comparison of semantic segmentation approaches for horizon/sky line detection

Attitude estimation using horizon detection in thermal images

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