A Fast Flexible Docking Method using an Incremental Construction Algorithm

The Hausdorff distance is a measure defined between two point sets, here representing a model and an image. The Hausdorff distance is reliable even when the image contains multiple objects, noise, spurious features, and occlusions. In the past, it has been used to search images for instances of a model that has been translated, or translated and scaled, by finding transformations that bring a large number of model features close to image features, and vice versa. In this paper, we apply it to the task of locating an affine transformation of a model in an image; this corresponds to determining the pose of a planar object that has undergone weak-perspective projection. We develop a rasterised approach to the search and a number of techniques that allow us to locate quickly all transformations of the model that satisfy two quality criteria; we can also efficiently locate only the best transformation. We discuss an implementation of this approach, and present some examples of its use.

Efficiently Locating Objects Using the Hausdorff Distance

http://www.cs.columbia.edu/~allen/PAPERS/cviu.ioannis.pdf

Geometry and texture recovery of scenes of large scale

The Hausdorff distance is a measure defined between two point sets representing a model and an image. In the past, it has been used to search images for instances of a model that has been translated or translated and scaled by finding transformations that bring a large number of model features close to image features, and vice versa. The Hausdorff distance is reliable even when the image contains multiple objects, noise, spurious features, and occlusions. We apply it to the task of locating an affine transformation of a model in an image; this corresponds to determining the pose of a planar object that has undergone weak perspective projection. We develop a rasterised approach to the search and a number of techniques that allow us to quickly locate all transformations of the model that satisfy two quality criteria; we can also quickly locate only the best transformation. We discuss an implementation of this approach, and present some examples of its use. >

Locating objects using the Hausdorff distance

Pose clustering is a method to perform object recognition by determining hypothetical object poses and finding clusters of the poses in the space of legal object positions. An object that appears in an image will yield a large cluster of such poses close to the correct position of the object. If there are m model features and n image features, then there are O(m^3n^3) hypothetical poses that can be determined from minimal information for the case of recognition of three-dimensional objects from feature points in two-dimensional images. Rather than clustering all of these poses, we show that pose clustering can have equivalent performance for this case when examining only O(mn) poses, due to correlation between the poses, if we are given two correct matches between model features and image features. Since we do not usually know two correct matches in advance, this property is used with randomization to decompose the pose clustering problem into O(n^2) problems, each of which clusters O(mn) poses, for a total complexity of O(mn^3) . Further speedup can be achieved through the use of grouping techniques. This method also requires little memory and makes the use of accurate clustering algorithms less costly. We use recursive histograming techniques to perform clustering in time and space that is guaranteed to be linear in the number of poses. Finally, we present results demonstrating the recognition of objects in the presence of noise, clutter, and occlusion.

https://faculty.washington.edu/cfolson/papers/pdf/ijcv97.pdf

Efficient Pose Clustering Using a Randomized Algorithm

This paper shows that the pose clustering method of object recognition can be decomposed into small sub-problems without loss of accuracy. Randomization can then be used to limit the number of sub-problems that need to be examined to achieve accurate recognition. These techniques are used to decrease the computational complexity of pose clustering. The clustering step is formulated as an efficient tree search of the pose space. This method requires little memory since not many poses are clustered at a time. Analysis shows that pose clustering is not inherently more sensitive to noise than other methods of generating hypotheses. Finally, experiments on real and synthetic data are presented. >

Time and space efficient pose clustering

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Time and space efficient pose clustering