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We propose a perceptually based system for pattern retrieval and matching. The central idea is that similarity judgment has to be modeled along perceptual dimensions. Hence, we detect basic visual categories that people use in their judgment of similarity, and design a computational model that accepts patterns as input and, depending on the query, produces(More)
In this paper we describe a Hidden Markov Model (HMM) based writer independent handwriting recognition system. A combination of signal normalization preprocessing and the use of invariant features makes the system robust with respect to variability among di!erent writers as well as di!erent writing environments and ink collection mechanisms. A combination(More)
Color descriptors are among the most important features used in image analysis and retrieval. Due to its compact representation and low complexity, direct histogram comparison is a commonly used technique for measuring the color similarity. However, it has many serious drawbacks, including a high degree of dependency on color codebook design, sensitivity to(More)
Personalized predictive models are customized for an individual patient and trained using information from similar patients. Compared to global models trained on all patients, they have the potential to produce more accurate risk scores and capture more relevant risk factors for individual patients. This paper presents an approach for building personalized(More)
Patient similarity assessment is an important task in the context of patient cohort identif cation for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. How to incorporate physician(More)
A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to(More)