30,131 research outputs found
Automatic facial analysis for objective assessment of facial paralysis
Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance
MEDEA: a real time imaging pipeline for pixel lensing
Pixel lensing is a technique used to search for baryonic components of dark
matter (MACHOs) and allows to detect microlensing events even when the target
galaxies are not resolved into individual stars. Potentially, it has the
advantage to provide higher statistics than other methods but, unfortunately,
traditional approaches to pixel lensing are very demanding in terms of
computing time. We present the new, user friendly, tool MEDEA (Microlensing
Experiment Data-Analysis Software for Events with Amplification). The package
can be used either in a fully automatic or in a semi-automatic mode and can
perform an on-line identification of events by means of a two levels trigger
and a quasi-on-line data analysis. The package will find application in the
exploration of large databases as well as in the exploitation of specifically
tailored future surveys.Comment: To appear in New Astronom
Image processing for plastic surgery planning
This thesis presents some image processing tools for plastic surgery planning. In particular,
it presents a novel method that combines local and global context in a probabilistic
relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic
surgery. It also uses a method that utilises global and local symmetry to identify abnormalities
in CT frontal images of the human body. The proposed methodologies are
evaluated with the help of several clinical data supplied by collaborating plastic surgeons
Visual analysis for drum sequence transcription
A system is presented for analysing drum performance video sequences. A novel ellipse detection algorithm is introduced that automatically locates drum tops. This algorithm fits ellipses to edge clusters, and ranks them according to various fitness criteria. A background/foreground segmentation method is then used to extract the silhouette of the drummer and drum sticks. Coupled with a motion
intensity feature, this allows for the detection of âhitsâ in each of the extracted regions. In order to obtain a transcription of the performance, each of these regions is automatically labeled with the corresponding instrument class. A partial audio transcription and color cues are used to measure the compatibility between a region and its label, the Kuhn-Munkres algorithm is then employed to find the optimal labeling. Experimental results demonstrate the ability of visual analysis to enhance the performance of an audio drum transcription system
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
An Objective and Automatic Cluster Finder: An Improvement of the Matched-Filter Method
We describe an objective and automated method for detecting clusters of
galaxies from optical imaging data. This method is a variant of the so-called
`matched-filter' technique pioneered by Postman et al. (1996). With
simultaneous use of positions and apparent magnitudes of galaxies, this method
can, not only find cluster candidates, but also estimate their redshifts and
richnesses as byproducts of detection. We examine errors in the estimation of
cluster's position, redshift, and richness with a number of Monte Carlo
simulations. No systematic discrepancies between the true and estimated values
are seen for either redshift or richness. Spurious detection rate of the method
is about less than 10% of those of conventional ones which use only surface
density of galaxies. A cluster survey in the North Galactic Pole is executed to
verify the performance characteristics of the method with real data. Two known
real clusters are successfully detected. We expect these methods based on
`matched-filter' technique to be essential tools for compiling large and
homogeneous optically-selected cluster catalogs.Comment: 13 pages, 12 PostScript figures, uses LaTeX L-AA, A&AS accepte
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