This paper proposes a novel method of performing inverse compositional image alignment which elegantly deals with missing data and re-weighting, and does not require the Jacobians and Hessian to be re-computed at every iteration. We show how missing data and re-weighting can be handled through preconditioning. We propose a few preconditioning techniques and analyse how each technique models the effects of missing data and re-weighting for inverse composition. We show through extensive experiments on different applications that our method improves the convergence rate of the conventional re-weighted inverse compositional method while remaining robust to outliers. We also show that the the update parameters are usually underestimated and how this can be used to further speed up convergence of image alignment methods.

We propose a method of monocular camera-inertial based navigation for computationally limited micro air vehicles (MAVs). Our approach is derived from the recent development of parallel tracking and mapping algorithms, but unlike previous results, we show how the tracking and mapping processes operate using different representations. The separation of representations allows us not only to move the computational load of full map inference to a ground station, but to further reduce the computational cost of on-board tracking for pose estimation. Our primary contribution is to show how the cost of tracking the vehicle pose on-board can be substantially reduced by estimating the camera motion directly in the image frame, rather than in the world co-ordinate frame. We demonstrate our method on an Ascending Technologies Pelican quad-rotor, and show that we can track the vehicle pose with reduced on-board computation but without compromised navigation accuracy.

Modern approaches to monocular SLAM retain all observations and repeatedly perform bundle adjustment in order to overcome the inconsistency problem that arises in approaches that marginalise out camera positions. Bundle adjustment is inherently an expensive operation and so sparse matrix techniques and double window optimisation on sparsely sampled key-frames are employed to minimize the computational cost.

Computational complexity of the Kalman filter grows at least quadratically with the number of dimensions in the filter. This is a particular problem for applications like monocular simultaneous localization and mapping (SLAM) where it is not possible to run a single filter on a large map with many thousands of landmarks. The filtering approach for SLAM, maintains only the current camera pose with all landmarks of interest as the state [2]. This paper presents a method for reducing the computational complexity of the Kalman filters by reducing the dimensionality as information is acquired. The method reduces the dimensionality of the extended Kalman filter (EKF) for SLAM by identifying dominant modes of the filter. This can be used in general to reduce the dimensionality of the EKF irrespectively of its application, without being limited to SLAM. For a filter with zero process noise, the mean of this distribution can be represented as a point in a nD space with its uncertainty as a hyper ellipse. Further information will reduce this uncertainty along some directions. After some time uncertainty along many directions becomes comparatively small, making further information redundant. So the covariance matrix can be decompose into certain and uncertain dimensions.

Many tasks in computer vision rely on accurate detection and matching of visual landmarks (e.g. image corners) between two images. In particular, for the calculation of epipolar geometry from a minimal set of five correspondences the spatial accuracy of matched landmarks is critical because the result is very sensitive to errors. The most common way of improving the accuracy is to calculate a sub-pixel location independently for each landmark in the hope that this reduces the re-projection error of the point in space to which they refer. This paper presents a method for refining the coordinates of correspondences directly. Thus given some coordinates in the first image, our goal is to maximise the accuracy of the estimate of the coordinates in second image corresponding to the same real world point without being too concerned about which real world point is being matched. We show how this can be achieved as a frequency domain optimisation between two image patches to refine the correspondence by estimating affine parameters. We select the correct frequency range for optimisation by identifying a direct relationship between the Gabor phase based approach and the frequency response of a patch. Further, we show how parametric estimation can be made accurate by operating in the frequency domain. Finally, we present experiments which demonstrate the accuracy of this approach, its robustness to changes in scale and orientation and its superior performance by comparison to other sub-pixel methods.

With the proliferation of internet technologies, publish/subscribe systems have gained wide usage as a middleware. However for this model, catering large number of publishers and subscribers while retaining acceptable performance is still a challenge. Therefore, this paper presents two parallelization strategies to improve message delivery of such systems. Furthermore, we discuss other techniques which can be adopted to increase the performance of the middleware. Finally, we conclude with an empirical study, which establishes the comparative merit of those two parallelization strategies in contrast to serial implementations.