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This paper proposes a new focus measurement method for Depth From Focus to recover depth of scenes. The method employs an all-focused image of the scene to address the focus measure ambiguity problem of the existing focus measures in the presence of occlusions. Depth discontinuities are handled effectively by using adaptively shaped and weighted support windows. The size of the support window can be increased conveniently for more robust depth estimation without introducing any window size related Depth From Focus problems. The experiments on the real and synthetically refocused images show that the introduced focus measurement method works effectively and efficiently in real world applications.

Due to copyright restrictions, the access to the full text of this article is only available via subscription.We present an HMD based AR system comprising visual and inertial sensors. The visual sensor is a camera pair and the inertial sensors consist of an accelerometer and a gyroscope. We discuss the temporal and spatial calibration issues that relate to such a system. We introduce simple yet effective methods for estimating the time lag between the camera and the inertial sensors and for estimating the relative pose between the camera and the inertial sensors. These methods do not require a complicated setup for data collection and involve simple equations to solve. Sample results are presented to demonstrate the visual performance of the system

This paper presents a novel system that uses two synchronous optimization processes to recover 3D structure from recti ed stereo image pairs. The synchronization of the processes are done by energy terms that inform the optimization processes about the recovered positions of each other. This information is used to direct the optimizations towards a better direction. The system is initialization insensitive and it is very robust against local minima.We performed experiments on real and synthetic images with ground truth that showed the effectiveness and the robustness of our system. We also compared our system to other systems for further validation. IntroductionAlthough the estimation of the 3D structure using stereo is one of the oldest techniques of Computer Vision, the problem is still being researched intensely by many groups. The simplicity and availability of the image acquisition hardware, very strong epipolar geometric constraints, naturally inspiring systems such as human vision, and the wide range of applicability of these systems are a few reasons for the popularity of stereo.Establishment of the correspondence between stereo image pairs is considered as the rst and most important problem of classical stereo analysis [8,2]. More current techniques take the path of formalizing the stereo problem as the global solution of estimating the 3D structure directly from the images, e.g., [10,9,14]. The formulations are usually written as one global energy functional that needs to be optimized to produce the desired 3D surface. The optimization of the functionals are generally NP-Hard for most of the cases in stereo [3]. As a result, some researchers simpli ed the functionals so that a globally optimal solution is possible, e.g., using dynamic programming [6][12]. However, for most cases, it is not possible to simplify the stereo analysis model. Therefore, using an approximate optimization method became more popular. These methods include [13] stereo by simulated annealing, graph cuts, gradient descent, genetic algorithms, etc. Although some of these methods produce very good results, optimality is still not satis ed and getting closer to optimal results is always desirable.This paper describes a system that uses two separate optimization processes for the recovery of 3D surfaces. The optimization processes are based on gradient descent heuris-