14,855 research outputs found
Accurate and reliable segmentation of the optic disc in digital fundus images
We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)
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
Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk
Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Testâretest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent testâretest reliability (Spearmanâs rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry
Sensitivity limits of a Raman atom interferometer as a gravity gradiometer
We evaluate the sensitivity of a dual cloud atom interferometer to the
measurement of vertical gravity gradient. We study the influence of most
relevant experimental parameters on noise and long-term drifts. Results are
also applied to the case of doubly differential measurements of the
gravitational signal from local source masses. We achieve a short term
sensitivity of 3*10^(-9) g/Hz^(-1/2) to differential gravity acceleration,
limited by the quantum projection noise of the instrument. Active control of
the most critical parameters allows to reach a resolution of 5*10^(-11) g after
8000 s on the measurement of differential gravity acceleration. The long term
stability is compatible with a measurement of the gravitational constant G at
the level of 10^(-4) after an integration time of about 100 hours.Comment: 19 pages, 20 figure
A Novel Optical/digital Processing System for Pattern Recognition
This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network
Shapes and Shears, Stars and Smears: Optimal Measurements for Weak Lensing
We present the theoretical and analytical bases of optimal techniques to
measure weak gravitational shear from images of galaxies. We first characterize
the geometric space of shears and ellipticity, then use this geometric
interpretation to analyse images. The steps of this analysis include:
measurement of object shapes on images, combining measurements of a given
galaxy on different images, estimating the underlying shear from an ensemble of
galaxy shapes, and compensating for the systematic effects of image distortion,
bias from PSF asymmetries, and `"dilution" of the signal by the seeing. These
methods minimize the ellipticity measurement noise, provide calculable shear
uncertainty estimates, and allow removal of systematic contamination by PSF
effects to arbitrary precision. Galaxy images and PSFs are decomposed into a
family of orthogonal 2d Gaussian-based functions, making the PSF correction and
shape measurement relatively straightforward and computationally efficient. We
also discuss sources of noise-induced bias in weak lensing measurements and
provide a solution for these and previously identified biases.Comment: Version accepted to AJ. Minor fixes, plus a simpler method of shape
weighting. Version with full vector figures available via
http://www.astro.lsa.umich.edu/users/garyb/PUBLICATIONS
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