Realistic limits for subpixel movement detection

Object tracking with subpixel accuracy is of fundamental importance in many fields since it provides optimal performance at relatively low cost. Although there are many theoretical proposals that lead to resolution increments of several orders of magnitude, in practice this resolution is limited by the imaging systems. In this paper we propose and demonstrate through simple numerical models a realistic limit for subpixel accuracy. The final result is that maximum achievable resolution enhancement is connected with the dynamic range of the image, i.e., the detection limit is 1/2∧(nr.bits). The results here presented may aid in proper design of superresolution experiments in microscopy, surveillance, defense, and other fields.

LINDA (Line Drawing Analyzer) /

Originally presented as the author's thesis, University of Illinois at Urbana-Champaign.

Picture analysis by graph transformation,

"Supported in part by the Department of Computer Science and the Atomic Energy Commission under contract US AEC AT(11-1)2118."

SYM-1, a program that detects symmetry of variable-valued logic functions /

Mode of access: Internet.

Eidolyzer: a hardware realization of context-guided picture interpretation.

Cover title.

Methodological aspects of scene segmentation.

"COO-2118-0028."

Experiments with an image processing computer : part 3: proposed technical program, October 1, 1970 to September 30, 1971 /

"C00-1018-1213"--Cover.

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.