Showing papers by "David G. Lowe published in 1989"

Fitting Parameterized 3-D Models to Images

Abstract: Model-based recognition and tracking from 2-D images depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. A standardized modeling language has been developed that can be used to define models and their internal parameters for efficient application to model-based vision. These new techniques allow model- based vision to be used for a much wider class of problems than was possible with earlier methods.

Segmentation of textured images

Abstract: The authors present a method for texture segmentation that does not assume any prior knowledge about either the type of textures or the number of textured regions present in the image. Local orientation and spatial frequencies are used as the key parameters for classifying texture. The information is obtained by creating a local multifrequency multiorientation channel decomposition of the image, with the width of each frequency band constant on a logarithmic scale. This decomposition is implemented by applying a set of Gabor-like functions that were modified to have a decreased frequency selectivity when the filter's center frequency increases. The set of filter outputs is then used to create robust texture descriptors. The segmentation algorithm uses the similarity of the descriptors to determine the existence of texture regions and to outline their border rather than concentrating on segregating the textures. The method has been applied to image containing natural textures, resulting in a good segmentation of the texture regions. >

Segmentation of nonrandom textures using zero-crossings

Abstract: A method for unsupervised segmentation of textured regions is presented. Rather than identifying boundaries between texture patches, this method detects regions of uniform texture in real images. No a priori knowledge regarding the image, the texture types, or their scales is assumed. The images may contain an unknown number of texture regions including regions with no texture at all. The method is most useful for identifying textures in which sharp intensity changes constitute the most distinctly perceived characteristic. Texture features are computed over image subregions from the distributions of local orientations and the separations of zero-crossing points. The segmentation algorithm establishes the existence of texture regions by finding neighboring subregions that share one or more nonaccidental properties of the computed features, e.g., a distribution of local orientations with a significant peak. Regions' accurate boundaries are identified by extending the seed regions using a region-growing technique that is applied to the computed texture features. The growth is directed by a region-specific self-adaptive thresholding scheme. No assumption is made regarding the texture scale, and the feature analysis is performed across multiple neighborhood (window) sizes. As a result different textures in the image may be segmented using different window sizes. >

Hessian Methods For Verification Of 3D Model Parameters From 2D Image Data

Abstract: A unified approach to instantiating model and camera parameters in the verification process is presented. Recognition implies the generation of a hypothesis, a map between projected model data and image data. An important issue remaining is the instantiation of model and camera parameters to verify the hypothesis. This "camera pose determination" is formulated as a nonlinear least squares problem, with functions minimizing distance between the projected model and image data. This approach treats camera and model parameters the same, simplifying the camera calibration problem. An original data structure Coordinate Trees with Null Com-ponents models the objects in the image. With this calculation of analytical first and second partial derivatives (with respect to parameters of model and camera) are now made possible. The application of various numeric techniques are compared, with tables displaying convergence results for various models and parameters. Minimal information is required, including the absence of depth data. This makes the algorithms robust in noisy images as well. Extensions to vision applications with general models is outlined.