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.

/pdf/gapped-blast-and-psi-blast-a-new-generation-of-protein-2ybl67x5yx.pdf

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

In this Automatic Population of GeospatialDatabases (APGD) overview paper, we describeour progress in developing and evaluating a genericcontext-based architecture (which we call the BOSfor the Battlespace Observer System) and algorith-mic techniques to radically reduce the human effortrequired to extract cartographic features, especiallyroads and buildings, from aerial and satelliteimagery. We also describe progress in supportingthe DARPA APGD community by constructingveriﬁed and well-documented datasets and evalu-ation procedures to allow interested researchers toexperimentally evaluate their extraction techniquesin a common framework. Our APGD Communityoriented activities are accessible at SRI’s APGDweb site, http://www.ai.sri.com/~apgd,and the APGD “Virtual Laboratory,”http://www.ai.sri.com/~apgd/vl.In a formal April1998presentation,we successfullydemonstrated the “end-to-end” extraction of all the This work was sponsored by the Defense Advanced Re-search Projects Agency under contract NMA100-97-C-1004monitored by the National Imagery and Mapping Agency, Re-ston, VA. The views and conclusions contained in this docu-ment are those of the author and should not be interpreted asrepresenting the ofﬁcial policies, either expressed or implied,of the DefenseAdvancedResearchProjects Agency,the UnitedStates Government, or SRI International.

An Integrated Feasibility Demonstration for Automatic Population of Geospatial Databases

Image understanding research at SRI International

The SESAME (video SEarch with Speed and Accuracy for Multimedia Events) team submitted six runs as a full participant in the Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) evaluations. The SESAME system combines low-level visual, audio, and motion features; high-level semantic concepts for visual objects, scenes, persons, sounds, and actions; automatic speech recognition (ASR); and video optical character recognition (OCR). These three types of features and five types of concepts were used in eight event classifiers. One of the event classifiers, VideoStory, is a new approach that exploits the relationship between semantic concepts and imagery in a large training corpus. The SESAME system uses a total of over 18,000 concepts. We combined the event-detection results for these classifiers using a log-likelihood ratio (LLR) late-fusion method, which uses logistic regression to learn combination weights for event-detection scores from multiple classifiers originating from different data types. The SESAME system generated event recountings based on visual and action concepts, and on concepts recognized by ASR and OCR. Training data included the MED Research dataset, ImageNet, a video dataset from YouTube, the UCF101 and HMDB51 action datasets, the NIST SIN dataset, and Wikipedia. The components that contributed most significantly to event-detection performance were the lowand high-level visual features, low-level motion features, and VideoStory. The LLR late-fusion method significantly improved performance over the best individual classifier for 100Ex and 010Ex. For the Semantic Query (SQ), equal fusion weights, instead of the LLR method, were used in fusion due to the absence of training data.

/pdf/the-2014-sesame-multimedia-event-detection-and-recounting-3donkfaium.pdf

The 2014 SESAME Multimedia Event Detection and Recounting System.

A method and apparatus are provided for person identification. In one embodiment, a method for identifying an individual includes obtaining at least one image of the individual, where the image depicts at least a portion of the individual’s hair, comparing the visual characteristics of the individual’s hair to the visual characteristics of imaged hair in one or more stored images, and identifying the individual based on the comparison.

Robert C. Bolles

Papers

An Integrated Feasibility Demonstration for Automatic Population of Geospatial Databases

Image understanding research at SRI International

The 2014 SESAME Multimedia Event Detection and Recounting System.

Method and apparatus for person identification

Implement positioning control apparatus and processes