Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

Efficient volumetric reconstruction from multiple calibrated cameras

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2005.Includes bibliographical references (p. 137-142).The automatic reconstruction of large scale 3-D models from real images is of significant value to the field of computer vision in the understanding of images. As a consequence, many techniques have emerged to perform scene reconstruction from calibrated images where the position and orientation of the camera are known. Feature based methods using points and lines have enjoyed much success and have been shown to be robust against noise and changing illumination conditions. The models produced by these techniques however, can often appear crude when untextured due to the sparse set of points from which they are created. Other reconstruction methods, such as volumetric techniques, use image pixel intensities rather than features, reconstructing the scene as small volumetric units called voxels. The direct use of pixel values in the images has restricted current methods to operating on scenes with static illumination conditions. Creating a volumetric representation of the scene may also require millions of interdependent voxels which must be efficiently processed. This has limited most techniques to constrained camera locations and small indoor scenes. The primary goal of this thesis is to perform efficient voxel-based reconstruction of urban environments using a large set of pose-instrumented images. In addition to the 3- D scene reconstruction, the algorithm will also generate estimates of surface reflectance and illumination. Designing an algorithm that operates in a discretized 3-D scene space allows for the recovery of intrinsic scene color and for the integration of visibility constraints, while avoiding the pitfalls of image based feature correspondence.(cont.) The algorithm demonstrates how in principle it is possible to reduce computational effort over more naive methods. The algorithm is intended to perform the reconstruction of large scale 3-D models from controlled imagery without human intervention.by Manish Jethwa.Ph.D

Affine and projective structure from motion

We demonstrate the recovery of 3D structure from multiple images, without attempting to determine the motion between views. The structure is recovered up to a transformation by a 3D linear group- the affine and projective group. The recovery does not require knowledge of camera intrinsic parameters or camera motion. Three methods for recovering such structure based on point correspondences are described and evaluated. The accuracy of recovered structure is assessed by measuring its invariants to the linear transformation, and by predicting image projections.