A generative probabilistic oriented wavelet model for texture segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

A reevaluation of achromatic spatio-temporal vision: nonoriented filters are monocular, they adapt, and can be used for decision making at high flicker speeds

Masking, adaptation, and summation paradigms have been used to investigate the characteristics of early spatio-temporal vision. Each has been taken to provide evidence for (i) oriented and (ii) nonoriented spatial-filtering mechanisms. However, subsequent findings suggest that the evidence for nonoriented mechanisms has been misinterpreted: those experiments might have revealed the characteristics of suppression (eg, gain control), not excitation, or merely the isotropic subunits of the oriented detecting mechanisms. To shed light on this, we used all three paradigms to focus on the ‘high-speed’ corner of spatio-temporal vision (low spatial frequency, high temporal frequency), where cross-oriented achromatic effects are greatest. We used flickering Gabor patches as targets and a 2IFC procedure for monocular, binocular, and dichoptic stimulus presentations. To account for our results, we devised a simple model involving an isotropic monocular filter-stage feeding orientation-tuned binocular filters. Both filter stages are adaptable, and their outputs are available to the decision stage following nonlinear contrast transduction. However, the monocular isotropic filters (i) adapt only to high-speed stimuli—consistent with a magnocellular subcortical substrate—and (ii) benefit decision making only for high-speed stimuli (ie, isotropic monocular outputs are available only for high-speed stimuli). According to this model, the visual processes revealed by masking, adaptation, and summation are related but not identical.

Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Kinetic and hydraulic studies in high-rate biological filters

Most published work on either low- or high-rate biological filters covers one of three topics: kinetics, microbiology/ecology or hydraulics. These areas have been re-examined together for high-rate filters in order to further integrate them and enable appropriate utilization of low-rate filter experience.

Effect of light filters on reading speed in normal and low vision due to age-related macular degeneration

Purpose: To investigate the effects of light filters on reading speed in normal and low vision due to age-related macular degeneration (AMD). Methods: Reading speed was determined for 12 subjects with normal vision and 12 subjects with non-exudative AMD using stationary lowercase nonsensical print in Times Roman font and four light filters; a yellow Corning Photochromic Filter (CPF) 450, a grey neural density (ND) filter, an individual filter obtained using the Intuitive Colorimeter® and a clear filter. Results: There was no statistically significant light filter effect on reading speed for the normal subjects. The AMD group demonstrated a statistically significant 5% average improvement in reading speed with the CPF450 compared with the other filters although some AMD subjects had improvements of 10-15%. Conclusions: Light filters obtained using the Intuitive Colorimeter® performed poorly when compared with the CPF450, ND and clear filters for both the study groups. For the AMD group, average reading speed was statistically greater with the CPF450 than the other filters, however it is questionable whether the improvement (5%) would be clinically significant. As some of the subjects with AMD had greater improvements with the CPF450 we advocate clinical assessment of light filters using existing protocols on an individual basis. © 2004 The College of Optometrists.

Do tinted lenses or filters improve visual performance in low vision? A review of the literature

This is a review of studies that have investigated the proposed rehabilitative benefit of tinted lenses and filters for people with low vision. Currently, eye care practitioners have to rely on marketing literature and anecdotal reports from users when making recommendations for tinted lens or filter use in low vision. Our main aim was to locate a prescribing protocol that was scientifically based and could assist low vision specialists with tinted lens prescribing decisions. We also wanted to determine if previous work had found any tinted lens/task or tinted lens/ocular condition relationships, i.e. were certain tints or filters of use for specific tasks or for specific eye conditions. Another aim was to provide a review of previous research in order to stimulate new work using modern experimental designs. Past studies of tinted lenses and low vision have assessed effects on visual acuity (VA), grating acuity, contrast sensitivity (CS), visual field, adaptation time, glare, photophobia and TV viewing. Objective and subjective outcome measures have been used. However, very little objective evidence has been provided to support anecdotal reports of improvements in visual performance. Many studies are flawed in that they lack controls for investigator bias, and placebo, learning and fatigue effects. Therefore, the use of tinted lenses in low vision remains controversial and eye care practitioners will have to continue to rely on anecdotal evidence to assist them in their prescribing decisions. Suggestions for future research, avoiding some of these experimental shortcomings, are made. © 2002 The College of Optometrists.

From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.

Third-derivative filters predict edge locations in spatial vision

Edge detection is crucial in visual processing. Previous computational and psychophysical models have often used peaks in the gradient or zero-crossings in the 2nd derivative to signal edges. We tested these approaches using a stimulus that has no such features. Its luminance profile was a triangle wave, blurred by a rectangular function. Subjects marked the position and polarity of perceived edges. For all blur widths tested, observers marked edges at or near 3rd derivative maxima, even though these were not 1st derivative maxima or 2nd derivative zero-crossings, at any scale. These results are predicted by a new nonlinear model based on 3rd derivative filtering. As a critical test, we added a ramp of variable slope to the blurred triangle-wave luminance profile. The ramp has no effect on the (linear) 2nd or higher derivatives, but the nonlinear model predicts a shift from seeing two edges to seeing one edge as the ramp gradient increases. Results of two experiments confirmed such a shift, thus supporting the new model. [Supported by the Engineering and Physical Sciences Research Council].

Mach edges: a key role for 3rd derivative filters in spatial vision

Edges are key points of information in visual scenes. One important class of models supposes that edges correspond to the steepest parts of the luminance profile, implying that they can be found as peaks and troughs in the response of a gradient (first-derivative) filter, or as zero-crossings (ZCs) in the second-derivative. A variety of multi-scale models are based on this idea. We tested this approach by devising a stimulus that has no local peaks of gradient and no ZCs, at any scale. Our stimulus profile is analogous to the classic Mach-band stimulus, but it is the local luminance gradient (not the absolute luminance) that increases as a linear ramp between two plateaux. The luminance profile is a smoothed triangle wave and is obtained by integrating the gradient profile. Subjects used a cursor to mark the position and polarity of perceived edges. For all the ramp-widths tested, observers marked edges at or close to the corner points in the gradient profile, even though these were not gradient maxima. These new Mach edges correspond to peaks and troughs in the third-derivative. They are analogous to Mach bands - light and dark bars are seen where there are no luminance peaks but there are peaks in the second derivative. Here, peaks in the third derivative were seen as light-to-dark edges, troughs as dark-to-light edges. Thus Mach edges are inconsistent with many standard edge detectors, but are nicely predicted by a new model that uses a (nonlinear) third-derivative operator to find edge points.

Wideband tuneable fibre Bragg grating filters

A fibre Bragg grating filter device, tunable over 45 nm, is reported. The device has a wavelength setting time below 1.5 ms and a maximum tuning speed of 21 nm/ns.

Picosecond soliton propagation using nonlinear optical loop mirrors as intensity filters

The authors show that by inserting nonlinear optical loop mirrors into an optical fibre transmission line, 1.5 ps solitons may be transmitted over at least 750 km, with amplifiers spaced at 15 km intervals.

Picosecond soliton transmission using concatenated nonlinear optical loop mirror intensity filters

We investigate the use of nonlinear optical loop mirrors as saturable absorbers in picosecond soliton transmission systems. It is found that they allow short (1–5-ps) pulses to be propagated through chains of optical amplifiers spaced at intervals of typically 10 km. The loop mirror removes dispersive waves and stabilizes the peak amplitude of the soliton. An additional advantage is that the self-frequency shift of the soliton may be suppressed by bandwidth filtering without causing growth of dispersive waves at the center of the passband. The timing jitter and soliton interactions present in the scheme are also described.

Functional source separation applied to induced visual gamma activity

Objective of this work was to explore the performance of a recently introduced source extraction method, FSS (Functional Source Separation), in recovering induced oscillatory change responses from extra-cephalic magnetoencephalographic (MEG) signals. Unlike algorithms used to solve the inverse problem, FSS does not make any assumption about the underlying biophysical source model; instead, it makes use of task-related features (functional constraints) to estimate source/s of interest. FSS was compared with blind source separation (BSS) approaches such as Principal and Independent Component Analysis, PCA and ICA, which are not subject to any explicit forward solution or functional constraint, but require source uncorrelatedness (PCA), or independence (ICA). A visual MEG experiment with signals recorded from six subjects viewing a set of static horizontal black/white square-wave grating patterns at different spatial frequencies was analyzed. The beamforming technique Synthetic Aperture Magnetometry (SAM) was applied to localize task-related sources; obtained spatial filters were used to automatically select BSS and FSS components in the spatial area of interest. Source spectral properties were investigated by using Morlet-wavelet time-frequency representations and significant task-induced changes were evaluated by means of a resampling technique; the resulting spectral behaviours in the gamma frequency band of interest (20-70 Hz), as well as the spatial frequency-dependent gamma reactivity, were quantified and compared among methods. Among the tested approaches, only FSS was able to estimate the expected sustained gamma activity enhancement in primary visual cortex, throughout the whole duration of the stimulus presentation for all subjects, and to obtain sources comparable to invasively recorded data.