Michael J. Brooks

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We develop a systematic approach to the discovery of parallel iterative schemes for solving the shape-from-shading problem on a grid. A standard procedure for finding such schemes is outlined, and subsequently used to derive several new ones. The shape-from-shading problem is known to be mathematically equivalent to a nonlinear first-order partial(More)
We consider the problem of estimating parameters of a model described by an equation of special form. Specific models arise in the analysis of a wide class of computer vision problems, including conic fitting and estimation of the fundamental matrix. We assume that noisy data are accompanied by (known) covariance matrices characterising the uncertainty of(More)
The main result of this paper is a procedure for self-calibration of a moving camera from instantaneous optical flow. Under certain assumptions, this procedure allows the ego-motion and some intrinsic parameters of the camera to be determined solely from the instantaneous positions and velocities of a set of image features. The proposed method relies upon(More)
The renormalisation technique of Kanatani is intended to iteratively minimise a cost function of a certain form while avoiding systematic bias inherent in the common method of minimisation due to Sampson. Within the computer vision community, the technique has generally proven difficult to absorb. This work presents an alternative derivation of the(More)
The usefulness of networks of surveillance cameras is primarily limited by the demand placed on human supervisors to monitor many real time video feeds simultaneously. The goal of automated visual surveillance is to reduce the burden on operators by including software in a surveillance system that can analyse video content automatically. This paper reviews(More)
Many parameter estimation methods used in computer vision are able to utilise covariance information describing the uncertainty of data measurements. This paper considers the value of this information to the estimation process when applied to measured image point locations. Covariance matrices are first described and a procedure is then outlined whereby(More)
Tracking objects in video using the mean shift (MS) technique has been the subject of considerable attention. In this work, we aim to remedy one of its shortcomings. MS, like other gradient ascent optimization methods, is designed to find local modes. In many situations, however, we seek the global mode of a density function. The standard MS tracker assumes(More)
Problems requiring accurate determination of parameters from image-based quantities arise often in computer vision. Two recent, independently developed frameworks for estimating such parameters are the FNS and HEIV schemes. Here, it is shown that FNS and a core version of HEIV are essentially equivalent, solving a common underlying equation via different(More)
We consider the problem of metrically reconstructing a scene viewed by a moving stereo head. The head comprises two cameras with coplanar optical axes arranged on a lateral rig, each camera being free to vary its angle of vergence. Under various constraints, we derive novel explicit forms for the epipolar equation, and show that a static stereo head(More)