Syntactic descriptions of pictures and Gestalt phenomena of visual perception.

Bibliography: leaf 25.

Synthesis of texture,

"Contract US AEC AT(11-1)2118."

The recursive nature of descriptions : a fixed point /

"UIUC-ENG-R-75-2539."

Texture analysis and cartographic feature extraction /

"January 1985."

Proceedings on "Emerging Patterns in AIPR". Abstracts

On cover, 1978 : NBS-EIA

The Role of IL-25 in Colitis-Associated Colon Cancer and Pulmonary Inflammation

Thesis (Ph.D.)--University of Washington, 2016-06

Mannose-binding lectin gene polymorphism predicts hospital admissions for COPD infections

Infection frequently causes exacerbations of chronic obstructive pulmonary disease (COPD). Mannose-binding lectin (MBL) is a pattern-recognition receptor that assists in clearing microorganisms. Polymorphisms in the MBL2 gene reduce serum MBL levels and are associated with risk of infection. We studied whether the MBL2 codon 54 B allele affected serum MBL levels, admissions for infective exacerbation in COPD and disease susceptibility. Polymorphism frequency was determined by PCR-RFLP in 200 COPD patients and 104 smokers with normal lung function. Serum MBL was measured as mannan-binding activity in a subgroup of 82 stable COPD patients. Frequency of COPD admissions for infective exacerbation was ascertained for a 2-year period. The MBL2 codon 54 B allele reduced serum MBL in COPD patients. In keeping, patients carrying the low MBL-producing B allele had increased risk of admission for infective exacerbation (OR 4.9, P-corrected = 0.011). No association of MBL2 genotype with susceptibility to COPD was detected. In COPD, serum MBL is regulated by polymorphism at codon 54 in its encoding gene. Low MBL-producing genotypes were associated with more frequent admissions to hospital with respiratory infection, suggesting that the MBL2 gene is disease-modifying in COPD. MBL2 genotype should be explored prospectively as a prognostic marker for infection risk in COPD.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Outcome measurement in Australian rehabilitation environments

Objective: To determine the frequency and pattern of methods of outcome assessment used in Australian physical rehabilitation environments. Design: Postal survey. Methods: A questionnaire on service type, staffing, numbers of adults treated and outcome measures used for 7 conditions related to injury and road trauma as well as stroke and neuromuscular disorders was sent to 973 services providing adult physical rehabilitation treatment. Results: Questionnaires were completed by 440 service providers for a response rate of 45%, similar to that reported in a recent European survey reported in this journal. A small number of measures were reported as in use by most respondents, while a large number of measures were used by a few respondents. Measures of physical changes were used more frequently than those of generic well-being or quality of life. Ease of use and reporting to other professionals were cited as the most important reasons in selection of outcome measures. Conclusion: This Australian-wide survey detected considerable heterogeneity in outcome measurement procedures used in rehabilitation environments. While the goal of measurement may vary between providers and differ between conditions, the results highlight opportunities for harmonization, benchmarking and measurement of health-related quality of life.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Low mannose-binding lectin function is associated with sepsis in adult patients

Mannose-binding lectin (MBL) is an innate immune system pattern recognition molecule that kills a wide range of pathogens via the lectin complement pathway. MBL deficiency is associated with severe infection but the best measure of this deficiency is undecided. We investigated the influence of MBL functional deficiency on the development of sepsis in 195 adult patients, 166 of whom had bloodstream infection and 35 had pneumonia. Results were compared with 236 blood donor controls. MBL function (C4b deposition) and levels were measured by enzyme-linked immunosorbent assay. Using receiver-operator characteristics of MBL function in healthy controls, we identified a level of < 0.2 U mu L-1 as a highly discriminative marker of low MBL2 genotypes. Median MBL function was lower in sepsis patients (0.18 U mu L-1) than in controls (0.48 U mu L-1, P < 0.001). MBL functional deficiency was more common in sepsis patients than controls (P < 0.001). MBL functional deficient patients had significantly higher sequential organ failure assessment (SOFA) scores and higher MBL function and levels were found in patients with SOFA scores predictive of good outcome. Deficiency of MBL function appears to be associated with bloodstream infection and the development of septic shock. High MBL levels may be protective against severe sepsis. © 2006 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.