AbstractÐTwo of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial

The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex

properties of MRF image priors

• Was stimulated by the need to understand the effectiveness of “off the shelf ” self protection (ECM) systems- in relation to specific threat weapons and engagement geometries • Was stimulated by the high cost of field trials and the availability of airliners • Was stimulated by the need for a systematic & repeatable analytical approach resulting in a documented self protection effectiveness audit trail

Intelligent tutoring systems which could be explained by the fact that the xTEx-Sys’s domain knowledge presentation is rather novel titution etwee rning 01), e-l

scale with problem size and the number of processors. The other, related goal is methodological: to assist people who will use the programs in architectural evaluations to prune the space of application and machine parameters in an informed and meaningful way. For example, by characterizing the working

algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multi-view image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our

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greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed

Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine