A robust dynamic programming algorithm to extract skyline in images for navigation

Lie, Wen-Nung; Lin, Ting-Chih; Hung, Keng-Shen

doi:10.1016/j.patrec.2004.08.021

Cited by 61 publications

(103 citation statements)

References 9 publications

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“…and in the background (e.g., clouds, snow patches, etc.). Thus, a skyline detection algorithm is employed [14], and all the edge pixels above the skyline are removed, being considered obstacles or clouds. Then, a simple weighting mechanism is applied, which assigns decreasing weights to the edge pixels as the distance from the skyline increases (Figure 3c -top).…”

Section: Offline Peak Detection For the Webmentioning

confidence: 99%

A Framework for Outdoor Mobile Augmented Reality and Its Application to Mountain Peak Detection

Fedorov

Frajberg

Fraternali

2016

Lecture Notes in Computer Science

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Abstract. Outdoor augmented reality applications project information of interest onto views of the world in real-time. Their core challenge is recognizing the meaningful objects present in the current view and retrieving and overlaying pertinent information onto such objects. In this paper we report on the development of a framework for mobile outdoor augmented reality application, applied to the overlay of peak information onto views of mountain landscapes. The resulting app operates by estimating the virtual panorama visible from the viewpoint of the user, using an online Digital Terrain Model (DEM), and by matching such panorama to the actual image framed by the camera. When a good match is found, meta-data from the DEM (e.g, peak name, altitude, distance) are projected in real time onto the view. The application, besides providing a nice experience to the user, can be employed to crowdsource the collection of annotated mountain images for environmental applications.

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Section: Offline Peak Detection For the Webmentioning

confidence: 99%

A Framework for Outdoor Mobile Augmented Reality and Its Application to Mountain Peak Detection

Fedorov

Frajberg

Fraternali

2016

Lecture Notes in Computer Science

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“…There are several applications of detected horizon including smooth navigation of unmanned aerial vehicles (UAVs) [17], [14], [23] and micro air vehicles (MAVs) [12], [13], [15], augmented reality [20], visual geo-localization and annotation of mountain/desert imagery [22], port security [16], and outdoor vehicle localization. Previous horizon line detection methods can be grouped into two major categories; (i) methods modeling sky and nonsky regions using machine learning [17], [12], [13], [16], [14], [15], [21] and (ii) methods relying on edge detection [19], [5]. Some attempts [1], [2], [20] have been made to combine these two approaches by eliminating non-horizon edges using classification; however, these attempts also fall under the second category as horizon detection is effectively based on edges.…”

Section: Introductionmentioning

confidence: 99%

“…In the classical edge based approach for horizon line detection Lie et al [5], an edge map is used to form a multistage graph and Dynamic Programming (DP) is applied to extract the horizon line by finding the shortest path. This approach is based on the assumption that there exists a consistent edge boundary segmenting the sky and non-sky regions.…”

Section: Introductionmentioning

confidence: 99%

“…This assumption, however, is not always true due to various environmental factors (e.g., clouds) and edge gaps. To deal with this issue, a gap-filling strategy has been proposed in [5]; however, it relies heavily on several parameters such as tolerance-of-gap (tog) and δ. These parameters define a search region when an edge node cannot be connected to a node in the next stage of the graph.…”

Section: Introductionmentioning

confidence: 99%

“…Edge gaps greater than the tolerance provided by the chosen parameter values, however, cannot not be filled. Moreover, Lie et al [5] assume that consistent edges appearing in the upper half of the image belong to the horizon line. To enforce this assumption, they induce biases towards shortest paths in this region.…”

Section: Introductionmentioning

confidence: 99%

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

Ahmad

Bebis

Nicolescu

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Abstract-Horizon line detection requires finding a boundary which segments an image into sky and non-sky regions. It has many applications including visual geo-localization and geotagging, robot navigation/localization, and ship detection and port security. Recently, two machine learning based approaches have been proposed for horizon line detection: one relying on edge classification and the other relying on pixel classification. In the edge-based approach, a classifier is used to refine the edge map by removing non-horizon edges. The refined edge map is then used to form a multi-stage graph where dynamic programming is applied to extract the horizon line. In the edge-less approach, classification is used to obtain a confidence of horizon-ness at each pixel location. The horizon line is then extracted by applying dynamic programming on the resultant dense classification map rather than on the edge map. Both approaches have shown to outperform the classical approach where dynamic programming is applied on the non-refined edge map. In this paper, we provide a comparison between the edge-less and edge-based approaches using two challenging data sets. Moreover, we propose fusing the information about the horizon-ness and edge-ness of each pixel. Our experimental results illustrate that the proposed fusion approach outperforms both the edge-based and edge-less approaches.

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