5 research outputs found
Efficient unimodality test in clustering by signature testing
This paper provides a new unimodality test with application in hierarchical
clustering methods. The proposed method denoted by signature test (Sigtest),
transforms the data based on its statistics. The transformed data has much
smaller variation compared to the original data and can be evaluated in a
simple proposed unimodality test. Compared with the existing unimodality tests,
Sigtest is more accurate in detecting the overlapped clusters and has a much
less computational complexity. Simulation results demonstrate the efficiency of
this statistic test for both real and synthetic data sets
Salient point region covariance descriptor for target tracking
Cataloged from PDF version of article.Features extracted at salient points are used to construct a
region covariance descriptor (RCD) for target tracking. In the classical
approach, the RCD is computed by using the features at each pixel
location, which increases the computational cost in many cases. This
approach is redundant because image statistics do not change significantly
between neighboring image pixels. Furthermore, this redundancy
may decrease tracking accuracy while tracking large targets because statistics
of flat regions dominate region covariance matrix. In the proposed
approach, salient points are extracted via the Shi and Tomasi’s minimum
eigenvalue method over a Hessian matrix, and the RCD features extracted
only at these salient points are used in target tracking. Experimental
results indicate that the salient point RCD scheme provides comparable
and even better tracking results compared to a classical RCD-based
approach, scale-invariant feature transform, and speeded-up robust
features-based trackers while providing a computationally more efficient
structure. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10
.1117/1.OE.52.2.027207
Application of Noise Invalidation Denoising in MRI
Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have beenused in clinical industry for diagnostic and research purposes. These images are subjectto noises while capturing the data that can eect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution andsignal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique isone of the common tools to remove the noise in the MRI images. The noise is eliminatedfrom the detailed coecients of the signal in the wavelet domain. This can be done byapplying thresholding methods. The main task here is to nd an optimal threshold andkeep all the coecients larger than this threshold as the noiseless ones. Noise InvalidationDenoising technique is a method in which the optimal threshold is found by comparingthe noisy signal to a noise signature (function of noise statistics). The original NIDeapproach is developed for one dimensional signals with additive Gaussian noise. In thiswork, the existing NIDe approach has been generalized for applications in MRI imageswith dierent noise distribution. The developed algorithm was tested on simulated datafrom the Brainweb database and compared with the well-known Non Local Mean lteringmethod for MRI. The results indicated better detailed structural preserving forthe NIDe approach on the magnitude data while the signal to noise ratio is compatible.The algorithm shows an important advantageous which is less computational complexitythan the NLM method. On the other hand, the Unbiased NLM technique is combinedwith the proposed technique, it can yield the same structural similarity while the signalto noise ratio is improved
Application of Noise Invalidation Denoising in MRI
Magnetic Resonance Imaging (MRI) is a common medical imaging tool that have beenused in clinical industry for diagnostic and research purposes. These images are subjectto noises while capturing the data that can eect the image quality and diagnostics.Therefore, improving the quality of the generated images from both resolution andsignal to noise ratio (SNR) perspective is critical. Wavelet based denoising technique isone of the common tools to remove the noise in the MRI images. The noise is eliminatedfrom the detailed coecients of the signal in the wavelet domain. This can be done byapplying thresholding methods. The main task here is to nd an optimal threshold andkeep all the coecients larger than this threshold as the noiseless ones. Noise InvalidationDenoising technique is a method in which the optimal threshold is found by comparingthe noisy signal to a noise signature (function of noise statistics). The original NIDeapproach is developed for one dimensional signals with additive Gaussian noise. In thiswork, the existing NIDe approach has been generalized for applications in MRI imageswith dierent noise distribution. The developed algorithm was tested on simulated datafrom the Brainweb database and compared with the well-known Non Local Mean lteringmethod for MRI. The results indicated better detailed structural preserving forthe NIDe approach on the magnitude data while the signal to noise ratio is compatible.The algorithm shows an important advantageous which is less computational complexitythan the NLM method. On the other hand, the Unbiased NLM technique is combinedwith the proposed technique, it can yield the same structural similarity while the signalto noise ratio is improved