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Global motion estimation (GME) algorithms are typically employed on aerial videos captured by on-board UAV cameras to compensate for the artificial motion induced in these video frames due to camera motion. However, existing methods for GME have high computational complexity and are therefore not suitable for on-board processing in UAVs with limited computing capabilities. In this paper, we propose...
Low-complexity corner detection is essential for many real-time computer vision applications that need to be executed on low-cost/low-power embedded platforms such as robots. The widely used Shi–Tomasi and Harris corner detectors become prohibitive in such platforms due to their high computational complexity, which is attributed to the need to apply a complex corner measure on the entire image. In...
Adaptive and low-complexity corner detection is essential for the realization of modern real-time video processing applications on low-resource embedded platforms. In our earlier work, we have proposed an efficient iterative pruning strategy for widely used Shi-Tomasi and Harris corner detectors. In this paper we propose a mask-based strategy for efficient nonmaximal suppression. In comparison to...
Widely-used corner detectors such as Shi-Tomasi and Harris necessitate the selection of a threshold parameter manually in order to identify good quality corners. The recent attempts based on trial-and-error methods for threshold setting are time-consuming, making them unsuitable for low-cost and embedded video processing applications. In this paper we propose a novel automated thresholding technique...
In this paper, we present a novel and computationally efficient pruning technique to speed up the Shi-Tomasi and Harris corner detectors. The proposed technique quickly prunes non-corners and selects a small corner candidate set by approximating the complex corner measure of Shi-Tomasi and Harris. The actual corner measure is then applied only to the reduced candidate set. Experimental results on...
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