Abstract-Horizon line is a promising visual cue which can be exploited for robot localization or visual geo-localization. Prominent approaches to horizon line detection rely on edge detection as a pre-processing step which is inherently a non-stable approach due to parameter choices and underlying assumptions. We present a novel horizon line detection approach which uses machine learning and Dynamic Programming (DP) to extract the horizon line from a classification map instead of an edge map. The key idea is assigning a classification score to each pixel, which can be interpreted as the likelihood of the pixel belonging to the horizon line, and representing the classification map as a multi-stage graph. Using DP, the horizon line can be extracted by finding the path that maximizes the sum of classification scores. In contrast to edge maps which are typically binary (edge vs no-edge) and contain gaps, classification maps are continuous and contain no gaps, yielding significantly better solutions. Using classification maps instead of edge maps allows for removing certain assumptions such as the horizon is close to the top of the image or that the horizon forms a straight line. The purpose of these assumptions is to bias the DP solution but they fail to produce good results when they are not valid. We demonstrate our approach on three different data sets and provide comparisons with a traditional approach based on edge maps. Although our training set is comprised of a very small number of images from the same location, our results illustrate that our method generalizes well to images acquired under different conditions and geographical locations.
Abstract.Horizon line detection is a segmentation problem where a boundary between a sky and non-sky region is searched. Conventionally edge detection is performed as the first step followed by dynamic programming to find the shortest path which conforms to the detected horizon line. Recent work has proposed the use of machine learning to reduce the number of non-horizon edges to accurately detect the horizon line. In this paper, we investigate the suitablity of various local texture features and their combinations to reduce the number of false classifications for a recently proposed horizon detection approach. Specifically, we explore SIFT, LBP, HOG and their combinations SIFT-LBP, SIFT-HOG, LBP-HOG and SIFT-LBP-HOG as features to train the SVM classifier. We further show that using only edge information as the nodal costs is not enough and propose various nodal costs which can result in enhanced accuracy of the detected horizon line as evidenced by the conducted experiments and results. We compare our proposed formulations with an earlier approach relying only on edges and suffers due to faulty assumptions. We report our comparative results for an image set comprising of mountainous images captured during an outdoor robot exploration of Basalt Hills.
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