In this paper we present a robust method for automatically matching features in images corresponding to the same physical point on an object seen from two arbitrary viewpoints. Unlike conventional stereo matching approaches we assume no prior knowledge about the relative camera positions and orientations. In fact in our application this is the information we wish to determine from the image feature matches. Features are detected in two or more images and characterised using affine texture invariants. The problem of window effects is explicitly addressed by our method -our feature characterisation is invariant to linear transformations of the image data including rotation, stretch and skew. The feature matching process is optimised for a structure-from-motion application where we wish to ignore unreliable matches at the expense of reducing the number of feature matches.
There has been considerable research interest recently, in the areas of real time contour tracking and active shape models. This paper demonstrates how dynamic jiltering can be used in combination with a nwdal-basedJIexibIe shape model to track an articulated non-rigid body in motion. The results show the method being used to track the silhouette of a walking pedestrian in real time. The active shape model used was generated automatically from real image data and incorporates variability in shape due to orientation as well as objectjkxibility. A Kalmanfilter is used to control spatial scale forfeature search over successive frames. Iterative refinement allows accurate contour localisation where feasible. The shape model incorporates knowledge of the likely shape of the contour and speeds up tracking by reducing the number of system parameters. A further increase in speed is obtained by filtering the shape parameters independently.194 0-8186-6435-5/94 $04.00 0 1994 IEEE
This paper describes a novel system for building seamless texture maps for a surface of arbitrary topology from real images of the object taken with a standard digital camera and uncontrolled lighting. In our application we wish to take a sparse set of real images of a 3D object, and apply the images to an approximate surface model of the object to generate a high quality textured model. In practice the measured colour and intensity for a surface element observed in different photographic images will not agree. This is due to the interaction between real world lighting effects (such as highlights and specularities) and variations in the camera gain settings as well as registration and surface modelling errors. We describe a new automatic approach that extends a classical 2D image blending technique to a 3D surface, which produces high quality photo-realistic results at a low computational cost.
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