14,158 research outputs found
Improved Stroke Detection at Early Stages Using Haar Wavelets and Laplacian Pyramid
Stroke merupakan pembunuh nomor tiga di dunia, namun hanya sedikit metode tentang deteksi dini. Oleh karena itu dibutuhkan metode untuk mendeteksi hal tersebut. Penelitian ini mengusulkan sebuah metode gabungan untuk mendeteksi dua jenis stroke secara simultan. Haar wavelets untuk mendeteksi stroke hemoragik dan Laplacian pyramid untuk mendeteksi stroke iskemik. Tahapan dalam penelitian ini terdiri dari pra proses tahap 1 dan 2, Haar wavelets, Laplacian pyramid, dan perbaikan kualitas citra. Pra proses adalah menghilangkan bagian tulang tengkorak, reduksi derau, perbaikan kontras, dan menghilangkan bagian selain citra otak. Kemudian dilakukan perbaikan citra. Selanjutnya Haar wavelet digunakan untuk ekstraksi daerah hemoragik sedangkan Laplacian pyramid untuk ekstraksi daerah iskemik. Tahapan terakhir adalah menghitung fitur Grey Level Cooccurrence Matrix (GLCM) sebagai fitur untuk proses klasifikasi. Hasil visualisasi diproses lanjut untuk ekstrasi fitur menggunakan GLCM dengan 12 fitur dan kemudian GLCM dengan 4 fitur. Untuk proses klasifikasi digunakan SVM dan KNN, sedangkan pengukuran performa menggunakan akurasi. Jumlah data hemoragik dan iskemik adalah 45 citra yang dibagi menjadi 2 bagian, 28 citra untuk pengujian dan 17 citra untuk pelatihan. Hasil akhir menunjukkan akurasi tertinggi yang dicapai menggunakan SVM adalah 82% dan KNN adalah 88%
Facial Component Detection in Thermal Imagery
This paper studies the problem of detecting facial components in thermal imagery (specifically eyes, nostrils and mouth). One of the immediate goals is to enable the automatic registration of facial thermal images. The detection of eyes and nostrils is performed using Haar features and the GentleBoost algorithm, which are shown to provide superior detection rates. The detection of the mouth is based on the detections of the eyes and the nostrils and is performed using measures of entropy and self similarity. The results show that reliable facial component detection is feasible using this methodology, getting a correct detection rate for both eyes and nostrils of 0.8. A correct eyes and nostrils detection enables a correct detection of the mouth in 65% of closed-mouth test images and in 73% of open-mouth test images
Separating Gravitational Wave Signals from Instrument Artifacts
Central to the gravitational wave detection problem is the challenge of
separating features in the data produced by astrophysical sources from features
produced by the detector. Matched filtering provides an optimal solution for
Gaussian noise, but in practice, transient noise excursions or ``glitches''
complicate the analysis. Detector diagnostics and coincidence tests can be used
to veto many glitches which may otherwise be misinterpreted as gravitational
wave signals. The glitches that remain can lead to long tails in the matched
filter search statistics and drive up the detection threshold. Here we describe
a Bayesian approach that incorporates a more realistic model for the instrument
noise allowing for fluctuating noise levels that vary independently across
frequency bands, and deterministic ``glitch fitting'' using wavelets as
``glitch templates'', the number of which is determined by a trans-dimensional
Markov chain Monte Carlo algorithm. We demonstrate the method's effectiveness
on simulated data containing low amplitude gravitational wave signals from
inspiraling binary black hole systems, and simulated non-stationary and
non-Gaussian noise comprised of a Gaussian component with the standard
LIGO/Virgo spectrum, and injected glitches of various amplitude, prevalence,
and variety. Glitch fitting allows us to detect significantly weaker signals
than standard techniques.Comment: 21 pages, 18 figure
TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys
There have been many efforts to correct systematic effects in astronomical
light curves to improve the detection and characterization of planetary
transits and astrophysical variability. Algorithms like the Trend Filtering
Algorithm (TFA) use simultaneously-observed stars to remove systematic effects,
and binning is used to reduce high-frequency random noise. We present TFAW, a
wavelet-based modified version of TFA. TFAW aims to increase the periodic
signal detection and to return a detrended and denoised signal without
modifying its intrinsic characteristics. We modify TFA's frequency analysis
step adding a Stationary Wavelet Transform filter to perform an initial noise
and outlier removal and increase the detection of variable signals. A wavelet
filter is added to TFA's signal reconstruction to perform an adaptive
characterization of the noise- and trend-free signal and the noise contribution
at each iteration while preserving astrophysical signals. We carried out tests
over simulated sinusoidal and transit-like signals to assess the effectiveness
of the method and applied TFAW to real light curves from TFRM. We also studied
TFAW's application to simulated multiperiodic signals, improving their
characterization. TFAW improves the signal detection rate by increasing the
signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light
curves. For simulated transits, the transit detection rate improves by a factor
~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs
up to a factor ~2x better than bin averaging for planetary transits. The
standard deviations of simulated and real TFAW light curves are ~40x better
than TFA. TFAW yields better MCMC posterior distributions and returns lower
uncertainties, less biased transit parameters and narrower (~10x) credibility
intervals for simulated transits. We present a newly-discovered variable star
from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table
Continuous Wavelet Transform and Hidden Markov Model Based Target Detection
Standard tracking filters perform target detection process by comparing the sensor output signal with a predefined threshold. However, selecting the detection threshold is of great importance and a wrongly selected threshold causes two major problems. The first problem occurs when the selected threshold is too low which results in increased false alarm rate. The second problem arises when the selected threshold is too high resulting in missed detection. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. Although TBD techniques eliminate the need for detection threshold at sensor’s signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Continuous Wavelet Transform (CWT) and Hidden Markov Model (HMM) based target detection method for employing with TBD techniques which does not employ any thresholding
Relative Facial Action Unit Detection
This paper presents a subject-independent facial action unit (AU) detection
method by introducing the concept of relative AU detection, for scenarios where
the neutral face is not provided. We propose a new classification objective
function which analyzes the temporal neighborhood of the current frame to
decide if the expression recently increased, decreased or showed no change.
This approach is a significant change from the conventional absolute method
which decides about AU classification using the current frame, without an
explicit comparison with its neighboring frames. Our proposed method improves
robustness to individual differences such as face scale and shape, age-related
wrinkles, and transitions among expressions (e.g., lower intensity of
expressions). Our experiments on three publicly available datasets (Extended
Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement
of our approach over conventional absolute techniques. Keywords: facial action
coding system (FACS); relative facial action unit detection; temporal
information;Comment: Accepted at IEEE Winter Conference on Applications of Computer
Vision, Steamboat Springs Colorado, USA, 201
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