415 research outputs found
Using projective invariants for constant time library indexing in model based vision
Projectively invariant shape descriptors allow fast indexing into model libraries, because recognition proceeds without reference to object pose. This paper describes progress in building a large model based vision system which uses many projectively invariant descriptors. We give a brief account of these descriptors and then describe the recognition system, giving examples of the invariant techniques working on real images. We demonstrate the ease of model acquisition in our system, where models are generated directly from images. We demonstrate fast recognition without determining object pose or camera parameters
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
Lunar Crater Identification in Digital Images
It is often necessary to identify a pattern of observed craters in a single
image of the lunar surface and without any prior knowledge of the camera's
location. This so-called "lost-in-space" crater identification problem is
common in both crater-based terrain relative navigation (TRN) and in automatic
registration of scientific imagery. Past work on crater identification has
largely been based on heuristic schemes, with poor performance outside of a
narrowly defined operating regime (e.g., nadir pointing images, small search
areas). This work provides the first mathematically rigorous treatment of the
general crater identification problem. It is shown when it is (and when it is
not) possible to recognize a pattern of elliptical crater rims in an image
formed by perspective projection. For the cases when it is possible to
recognize a pattern, descriptors are developed using invariant theory that
provably capture all of the viewpoint invariant information. These descriptors
may be pre-computed for known crater patterns and placed in a searchable index
for fast recognition. New techniques are also developed for computing pose from
crater rim observations and for evaluating crater rim correspondences. These
techniques are demonstrated on both synthetic and real images
Recognizing 3-D Objects Using 2-D Images
We discuss a strategy for visual recognition by forming groups of salient image features, and then using these groups to index into a data base to find all of the matching groups of model features. We discuss the most space efficient possible method of representing 3-D models for indexing from 2-D data, and show how to account for sensing error when indexing. We also present a convex grouping method that is robust and efficient, both theoretically and in practice. Finally, we combine these modules into a complete recognition system, and test its performance on many real images
Geometric and photometric affine invariant image registration
This thesis aims to present a solution to the correspondence problem for the registration
of wide-baseline images taken from uncalibrated cameras. We propose an affine
invariant descriptor that combines the geometry and photometry of the scene to find
correspondences between both views. The geometric affine invariant component of the
descriptor is based on the affine arc-length metric, whereas the photometry is analysed
by invariant colour moments. A graph structure represents the spatial distribution of the
primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs
represent connectivities by extracted contours. After matching, we refine the search for
correspondences by using a maximum likelihood robust algorithm. We have evaluated
the system over synthetic and real data. The method is endemic to propagation of errors
introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System
Object recognition using color and geometry indexing
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 76-79).by Lily Lee.M.S
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