Local decomposition of gray-scale morphological templates

Template decomposition techniques can be useful for improving the efficiency of imageprocessing algorithms. The improved efficiency can be realized either by reorganizing a computation to fit a specialized structure, such as an image-processing pipeline, or by reducing the number of operations used. In this paper two techniques are described for decomposing templates into sequences of 3×3 templates with respect to gray-scale morphological operations. Both techniques use linear programming and are guaranteed to find a decomposition of one exists.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46623/1/10851_2004_Article_BF00123880.pd

Combining Fingerprint and Voiceprint Biometrics for Identity Verification: an Experimental Comparison

Abstract. Combining multiple biometrics may enhance the performance of personal authentication system in accuracy and reliability. In this paper, we compare 13 combination methods in the context of combining the voiceprint and fingerprint recognition system in two different modes: verification and identification. The experimental results show that Support Vector Machine and the Dempster-Shafer method are superior to other schemes. 1

A SOM based model combination strategy

A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and aggregation weights are specific for each input data point analyzed. The possibility to detect outliers is one more characteristic feature of the strategy

Model-Based Motion Filtering for Improving Arm Gesture Recognition Performance

We describe a model-based motion filtering process that when applied to human arm motion data leads to improved arm gesture recognition. By arm gestures, we mean movements of the arm (and positional placement of the hand) that may or may not have any meaningful intent. Arm movements or gestures can be viewed as responses to muscle actuations that are guided by responses of the nervous system. Our method makes strides towards capturing this underlying knowledge of human performance by integrating a model for the arm based on dynamics and containing a control system. We hypothesize that by embedding the human performance knowledge into the processing of arm movements, it will lead to better recognition performance. We present details for the design of our filter, our analysis of the filter from both expert-user and multiple-user pilot studies. Our results show that the filter has a positive impact on the recognition performance for arm gestures