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Shape from focus (SFF), represents a passive technique in recovering object shapes from scenes. In SFF systems, a single imaging device is used to acquire images followed by the application of Focus Measure (FM) at every point to obtain focus quality, to obtain depth by maximizing the focus curves. Although numerous focus measures have been recently proposed, less attention has been paid to the factors...
RRT* is a recent and improved variant of the RRT path finding algorithm. While RRT concentrates on simply finding an initial obstacle-free path, RRT* guarantees eventual convergence to an optimum, collision-free path for any given geometrical environment. On the other hand, the main limitations of RRT* include its slow processing rate and high memory utilization due to the large number of iterations...
The Rapidly Exploring Random Tree Star (RRT∗) is an extension of the Rapidly Exploring Random Tree path finding algorithm. RRT∗ guarantees an optimal, collision free path solution but is limited by slow convergence rates and inefficient memory utilization. This paper presents APGD-RRT∗, a variant of RRT∗ which utilizes Artificial Potential Fields to improve RRT∗ performance, providing relatively better...
Detection and Tracking of human being is a very important problem in Computer Vision. Human robot interaction is a very essential need for service robots where robots are required to detect and track human beings in order to provide the required service. In this paper we present an improved novel approach for tracking a target person in crowded environment. We used multi-sensor data fusion approach...
Although numerous methods have been recently proposed in recovering shape from image focus, all follow similar precedent of focus measure application after image acquisition and depth is recovered by maximizing the focus curves obtained via this application. This is followed by utilizing approximation techniques to refine the initial depth map. In this paper we have followed an unorthodox approach...
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