https://hal.archives-ouvertes.fr/hal-00417283/document

Independent component analysis, a new concept?

http://www4.comp.polyu.edu.hk/~cslzhang/paper/PR_10_Mar_LBPV.pdf

Rotation invariant texture classification using LBP variance (LBPV) with global matching

European archives of psychiatry and clinical neuroscience

This paper introduces a new velocity estimator, referred to as the 2D autocorrelator, which differs from conventional Doppler techniques in two respects: the derivation of axial velocity values by evaluating the Doppler equation using explicit estimates of both the mean Doppler and the mean RF frequency at each range gate location; and, the 2D nature (depth samples versus pulse transmissions) of processing within the range gate. The estimator's output can be calculated by evaluating the 2D autocorrelation function of the demodulated (baseband) backscattered echoes at two lags. A full derivation and mathematical description of the estimator is presented, based on the framework of the 2D Fourier transform. The same framework is adopted to analyze two other established velocity estimators (the conventional 1D autocorrelator and the crosscorrelator) in a unifying manner, and theoretical arguments as well as experimental results are used to highlight the common aspects of all three estimators. In addition, a thorough performance evaluation is carried out by means of extensive simulations, which document the effect of a number of factors (velocity spread, range gate length, ensemble length, noise level, transmitted bandwidth) and provide an insight into the optimum parameters and trade-offs associated with individual algorithms. Overall, the 2D autocorrelator is shown to offer the best performance in the context of the specific simulation conditions considered here. Its superiority over the crosscorrelator is restricted to cases of low signal-to-noise ratios. However, the 2D autocorrelator always outperforms the conventional 1D autocorrelator by a significant margin. These comparisons, when linked to the computational requirements of the proposed estimator, suggest that it combines the generally higher performance of 2D broadband time-domain techniques with the relatively modest complexity of 1D narrowband phase-domain velocity estimators. >

/pdf/an-axial-velocity-estimator-for-ultrasound-blood-flow-11yyhdaxbj.pdf

An axial velocity estimator for ultrasound blood flow imaging, based on a full evaluation of the Doppler equation by means of a two-dimensional autocorrelation approach

We propose a general approach for the design of 2D feature detectors from a class of steerable functions based on the optimization of a Canny-like criterion. In contrast with previous computational designs, our approach is truly 2D and provides filters that have closed-form expressions. It also yields operators that have a better orientation selectivity than the classical gradient or Hessian-based detectors. We illustrate the method with the design of operators for edge and ridge detection. We present some experimental results that demonstrate the performance improvement of these new feature detectors. We propose computationally efficient local optimization algorithms for the estimation of feature orientation. We also introduce the notion of shape-adaptable feature detection and use it for the detection of image corners.

/pdf/design-of-steerable-filters-for-feature-detection-using-3znuqfngm1.pdf

Design of steerable filters for feature detection using canny-like criteria

Basic aspects of time delay estimation (TDE) based on sampled signals are investigated. The direct cross-correlation method is analyzed and compared to the average square difference function (ASDF) and the (addition only based) average magnitude difference function (AMDF) estimators, Their relative accuracy is theoretically evaluated, and previous empirical results are explained. It is shown that both the ASDF- and the AMDF-based estimators outperform the direct cross-correlation based estimator for medium-high signal-to-noise ratios. Moreover, the AMDF-based estimator, which avoids any multiplications, significantly reduces the computational complexity of the estimation procedure while offering only a moderate performance loss with respect to the ASDF based estimator. >

Discrete time techniques for time delay estimation

This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.

Multichannel blind image deconvolution using the Bussgang algorithm: spatial and multiresolution approaches

In this paper EEG signals are employed for the purpose of automatic user recognition. Specifically the resting state with closed eyes acquisition protocol has been here used and deeply investigated by varying the employed electrodes configuration both in number and location for optimizing the recognition performance still guaranteeing sufficient user convenience. A database of 45 healthy subjects has been employed in the analysis. Autoregressive stochastic modeling and polynomial regression based classification has been applied to extracted brain rhythms in order to identify the most distinctive contributions of the different subbands in the recognition process. Our analysis has shown that significantly high recognition rates, up to 98.73%, can be achievedwhen using proper triplets of electrodes,which cannot be achieved by employing couple of electrodes,whereas sets of five electrodes in the central posterior region of the scalp can guarantee very high recognition performance while limiting user convenience.

EEG biometrics for individual recognition in resting state with closed eyes

In this paper the use of brain waves as a biometric identifier is investigated. Among the very different protocols that can be used to acquire the electroencephalogram signal (EEG) of an individual we rely on a very simple one: closed eyes in resting conditions. A database of 48 healthy subjects, collected by the authors at the neurophysiology laboratory of the IRCCS Fondazione Santa Lucia, Roma, Italy, has been used for the experiments. Signals acquired from triplets of electrodes have been employed in the experimentations. In more detail, ten different triplets have been used separately in the experiments in order to speculate about the most suitable triplet to capture the occurring phenomena. Feature vectors constituted by the reflection coefficients of a six order AR model have been extracted for each used channel thus giving rise to a feature vector of length eighteen. A polynomial regression based classification is then employed. This analysis has been performed for three different frequency bands for each of the ten different triplet under analysis. The obtained genuine acceptance rate is of 96.08%.

Brain waves based user recognition using the “eyes closed resting conditions” protocol

In this paper, an unsupervised model-based texture reproduction technique is described. In accordance with the Julesz's (1962) conjecture, the statistical properties of the prototype up to the second order are copied in order to generate a synthetic texture perceptually indistinguishable from the given sample. However, this task is accomplished using a hybrid approach which operates partially in the spatial domain and partially in a multiresolution domain. The latter employed is the circular harmonic function (CHF) domain since it has been proven to be well suited for mimicking the behavior of the human visual system (HVS). This approach allows, for a wide range of textures typologies, obtaining synthetic textures that better match the prototype with respect to the ones obtained using techniques based on the Julesz's conjecture operating only in the spatial domain, and to dramatically reduce the computational complexity of similar methods operating only in the multiresolution domain.

G. Scarano

Papers

Discrete time techniques for time delay estimation

Multichannel blind image deconvolution using the Bussgang algorithm: spatial and multiresolution approaches

EEG biometrics for individual recognition in resting state with closed eyes

Brain waves based user recognition using the “eyes closed resting conditions” protocol

A multiresolution approach for texture synthesis using the circular harmonic functions