The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >

A method for registration of 3-D shapes

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Multiple View Geometry in Computer Vision.

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

/pdf/a-taxonomy-and-evaluation-of-dense-two-frame-stereo-4c20etkubp.pdf

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing

/pdf/random-sample-consensus-a-paradigm-for-model-fitting-with-qa2gm9f83r.pdf

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Parametric correspondence is a technique for matching images to a three dimensional symbolic reference map. An analytic camera model is used to predict the location and appearance of landmarks in the image, generating a projection for an assumed viewpoint. Correspondence is achieved by adjusting the parameters of the camera model until the appearances of the landmarks optimally match a symbolic description extracted from the image.

The matching of image and map features is performed rapidly by a new technique, called "chamfer matching", that compares the shapes of two collections of shape fragments, at a cost proportional to linear dimension, rather than area. These two techniques permit the matching of spatially extensive features on the basis of shape, which reduces the risk of ambiguous matches and the dependence on viewing conditions inherent in conventional image based correlation matching.

/pdf/parametric-correspondence-and-chamfer-matching-two-new-1y9p61iemw.pdf

Parametric correspondence and chamfer matching: two new techniques for image matching

We present a technique for building a three-dimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image. The technique utilizes knowledge of the camera motion to form and analyze slices of this solid. These slices directly encode not only the three-dimensional positions of objects, but also such spatiotemporal events as the occlusion of one object by another. For straight-line camera motions, these slices have a simple linear structure that makes them easier to analyze. The analysis computes the three-dimensional positions of object features, marks occlusion boundaries on the objects, and builds a three-dimensional map of “free space.” In our article, we first describe the application of this technique to a simple camera motion, and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects.

/pdf/epipolar-plane-image-analysis-an-approach-to-determining-55gt8zm0ip.pdf

Epipolar-plane image analysis: An approach to determining structure from motion

A system that recognizes objects in a jumble, verifies them, and then determines some essential configurational information, such as which ones are on top, is presented. The approach is to use three-dimensional models of the objects to find them in range data. The matching strategy starts with a distinctive edge feature, such as the edge at the end of a cylindrical part, and then “grows” a match by adding compatible features, one at a time. (The order of features to be considered is predetermined by an interactive, off-line feature-selection process.) Once a sufficient number of compatible features has been detected to allow a hypothesis to be formed, the verification procedure evaluates it by comparing the measured range data with data predicted according to the hypothesis. When all the objects in the scene have been hypothesized and verified in this manner, a configuration-understanding procedure determines which objects are on top of others by analyzing the patterns of range data predicted from all the hypotheses. Experimental results of the system’s performance in recognizing and locating castings in a bin are presented.

/pdf/3dpo-a-three-dimensional-part-orientation-system-411cmm0ds7.pdf

3DPO: a three-dimensional part orientation system

A new method of locating partially visible two-dimensional objects is presented. The method is applicable to complex industrial parts that may contain several occurrences of local features, such as holes and corners. The matching process utilises clusters of mutually consistent features to hypothesise objects, also uses templates of the objects to verify these hypotheses. The technique is fast because it concentrates on key features that are automatically selected on the basis of a detailed analysis of CAD-type models of the objects. The automatic analysis applies general-purpose routines for building and analysing representations of clusters of local features that could be used in procedures to select features for other locational strategies. These routines include algorithms to compute the rotational and mirror symmetries of objects in terms of their local features.

Robert C. Bolles

Papers

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Parametric correspondence and chamfer matching: two new techniques for image matching

Epipolar-plane image analysis: An approach to determining structure from motion

3DPO: a three-dimensional part orientation system

Recognising and Locating Partially Visible Objects: The Local-Feature-Focus Method