Emma E. Regentova

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Planetary rover localization is a challenging problem since no conventional methods such as GPS, structural landmarks etc. are available. Horizon line is a promising visual cue which can be exploited for estimating the rover's position and orientation during planetary missions. By matching the horizon line detected in 2D images captured by the rover with(More)
Sky segmentation is an important task for many applications related to obstacle detection and path planning for autonomous air and ground vehicles. In this paper, we present a method for the automated sky segmentation by fusing K-means clustering and Neural Network (NN) classifications. The performance of the method has been tested on images taken by two(More)
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(More)
In this paper, we consider the problem of segmenting an image into sky and non-sky regions, typically referred to as horizon line detection or skyline extraction. Specifically, we present a new approach to horizon line detection by coupling machine learning with dynamic programming. Given an image, the Canny edge detector is applied first and keeping only(More)
In current Personal Communication Service (PCS) Networks, such as Global Systems for Mobile Communications (GSM), the always-update (AU) strategy is used to keep track of mobile terminals (MTs) within the network. However, future PCS networks, like Universal Mobile Telecommunication System (UMTS) envision larger number of MTs and smaller cells. This will(More)