Pixelated Filters Research Articles

A Secondary Fingerprint Enhancement and Minutiae Extraction

In this paper, we introduce a new fingerprint enhancement algorithm which used FFT and Gaussian filter. FFT is applied on a block, of size 32x32 pixel and Gaussian filter is applied in each interconnection of each block. This make ridge smoothness and also reduce the “hairy” structure in the ridge when it is thinning. The experimental result shows that the implementation of FFT and Gaussian filter in the preprocessing stage make less effort in the postprocessing stage and hence the total error rate of the proposed algorithm is very low.

An Efficient Neighbourhood Pixel Filtering Algorithm for Wavelet-Based Image Denoising

Image denoising using the wavelet transform has been attracting much attention. Image corrupted by a noise is a classical problem in the field of signal or image processing. À Trous algorithm is introduced to overcome the problem of translation variant mechanism, existing on using discrete wavelet transform (DWT). This algorithm up samples low-pass filter by inserting zeros between the filter coefficients at each level and accordingly the low-pass and high-pass filter coefficients are modified. The efficiency of wavelet images by using this algorithm is low because the detail preservations of images at different scales are not uniform; also random noise rapidly attenuates with increasing scales. Due to this the contrast of the resulting image is weaker. In order to improve the clarity of the image an algorithm called a neighbourhood pixel filtering algorithm (NPFA) is added along with the existing à trous algorithm. In the proposed algorithm, find neighbourhood pixel difference (NPD ) by subtracting the neighbourhood pixel values from its current noisy pixel value. Also, calculate weight of each pixel which depends on this NPD. A filtered value is assigned for each current pixel in order to approximate the original pixel value of that pixel. This filtered value is generated by minimizing NPD and weighted mean square error (WMSE) using method of least square. A reduction in noise pixel is observed on replacing the optimal weight namely NPFA filter solution with the noisy value of the current pixel. Due to this NPFA filter gain the effect of both high-pass and low-pass filter. This filter behaves like a low-pass filter in smooth region by decreasing noise variance effectively and giving similar weights to all its neighbourhood pixels. This in turn cuts off only high frequency noise signal instead of all noisy signals. The resultant image thus obtained is observed to have much less blurring effect compared to the other wavelet method.

Adaptive sampling and reconstruction using greedy error minimization

We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating over two steps. First, given a current sample distribution, we optimize over a discrete set of filters at each pixel and select the filter that minimizes the pixel error. Next, given the current filter selection, we distribute additional samples to further reduce MSE. The success of our approach hinges on a robust technique to select suitable per pixel filters. We develop a novel filter selection procedure that robustly solves this problem even with noisy input data. We evaluate our approach using effects such as motion blur, depth of field, interreflections, etc. We provide a comparison to a state-of-the-art algorithm based on wavelet shrinkage and show that we achieve significant improvements in numerical error and visual image quality. Our approach is simple to implement, requires a single user parameter, and is compatible with standard Monte Carlo rendering.

Fabrication of a dual-layer aluminum nanowires polarization filter array

In this paper we present a procedure for fabricating an array of micropolarization filter array via an optimized interference lithography and microfabrication procedure. The filter array is composed of two linear polarization filters offset by 45 degrees with pixel pitch of 18 microns. The individual polarization filters are composed of aluminum nanowires with 140 nm pitch, 140 nm height and 70 nm width. The maximum extinction ratio of the pixelated filters is measured to be 95 at 700 nm wavelength.

Analysis and optimization of the asynchronous modulated light detection pixel

Detection of modulated light signals transmitted by active image markers can be used in object localization tasks and machine vision applications. The asynchronous demodulation pixel is able to detect the signal transmitted by these modulated markers. The modulated marker position in a two-dimensional pixel array can be used to identify the marker location in the field. In this paper the asynchronous demodulation pixel is analyzed and its operation is investigated in the presence of non-ideal effects such as parasitic elements, device mismatches and process variations. The effect of demodulator bias conditions and different switch pulsing techniques are examined and a modified demodulator controlling method is presented to increase the dynamic range and sensitivity of the pixel compared with the common operation schemes. The different demodulator structures are also simulated as 64 × 64 pixel array sensors using a 0.18 μm standard CMOS process and their operation is compared regarding sensitivity, filter transfer function, pixel size and power consumption.

Applications of Kalman Filtering to Single Hyperspectral Signature Analysis

Kalman filter (KF) is a widely used statistical signal processing technique for parameter estimation. Recently, a KF-based approach to linear spectral unmixing, called KF-based linear spectral unmixing (KFLU) was developed for mixed pixel classification. However, its applicability to spectral characterization for spectral estimation, identification, and quantification has not been explored. This paper presents new applications of Kalman filtering in spectral estimation, identification and abundance quantification for which three KF-based spectral characterization signal processing techniques are developed. These techniques are completely different from the KFLU in the sense that the former performs a KF across a spectral coverage wavelength by wavelength as opposed to the latter, which implements a Kalman filter pixel vector by pixel vector throughout an entire image cube. In addition, the proposed KF-based techniques do not require a linear mixture model as KFLU does. Accordingly, they are not linear spectral unmixing methods, but rather spectral signature filters operating as if they are spectral measures.

Generation of Stratified Samples for B-Spline Pixel Filtering

B-spline filter kernels have proved useful in many pixel-sampling applications. A cubic B-spline filter kernel, having a width of four pixels, is particularly effective. In distribution ray tracing, pixel filters are evaluated implicitly by having the density of sampling proportional to the filter value. In this work we present a simple method to generate random samples having an underlying B-spline density function. To reduce error it is important to stratify the samples, akin to jittering for uniform sampling. We provide an algebraic and a numerical technique for doing this for B-spline kernels of degree 1, 2, and 3.

A motion-adaptive de-interlacing method using an efficient spatial and temporal interpolation

This paper proposes a motion-adaptive de-interlacing algorithm based on an edge-based median filter (EMF) and adaptive minimum pixel difference filter (AMPDF). To compensate for missing-motion error, which is an important factor in motion-adaptive methods, we used an AMPDF, which estimates an accurate value using different thresholds after classifying areas of the input image into 3 classes. To efficiently interpolate moving diagonal edges, we used an EMF, which adopted a 5-point median filter using the edge information. Finally, to increase the performance, we adopted adaptive interpolation after subdividing the input image into moving, background, and boundary regions. Simulation results showed that the proposed method performs better than existing methods.

Optimal region of support for arbitrary filter spatial light modulators

When only certain filter pixels in a spatial light modulator (SLM) are allowed to transmit light, those pixels constitute the region of support (ROS) for the filter. At the frequencies where noise power overwhelms signal power, or where the available values of the filter are not well aligned with the ideal spectral filter, the filter can actually work against the intent of the metric, and zero transmittance is preferable to what the SLM can do. In contrast to prior theories, which applied to only unit-magnitude filters, we present algorithms for optimizing and studying ROS for an arbitrary SLM. We also present laboratory demonstrations of improvement in signal-to-noise ratio when a blocking mask is inserted into an optical correlator. A mask has detailed functional dependence on the signal, the noise power spectral density, the selected metric, and the realizable filter values. Broadly useful rules are given for designing a mask. Conjecture is offered that it could be beneficial to build a permanent ROS into SLMs.

A coronary calcification diagnosis system based on helical CT images

Describes a computer assisted diagnostic algorithm for coronary calcifications based on helical X-ray CT images which is used in mass screening process for lung cancer diagnosis. The authors' diagnostic algorithm consists of four processes: First, they choose the heart slices from the CT images which were taken at the mass screening. They classify the heart slices into three sections which have different coronary geometries, using the information of the heart shape, trachea, CT values in the heart region, and the bone. Second, the authors extract the heart region in each slice, using the information of the lung shape and the body of vertebra. Third, they detect the candidate regions of the coronary calcifications using an edge filter and thresholding pixel values. Finally, to increase the effectiveness of the diagnosis, the authors exclude the artifact regions included in the candidate regions by using the diagnostic rule based on a neural network. They applied this algorithm to helical CT images of 462 patients screened for lung cancer. The results generated by this system were compared with a physician's diagnosis. This system could detect 213 of 214 regions which were diagnosed as coronary calcifications or probably coronary calcifications by a physician. There was only one false negative case. The false positive ratio was 0.30 per patient.

Reduced-resolution synthetic-discriminant-function design by multiresolution wavelet analysis

Several approaches to the design of reduced-resolution synthetic discriminant functions (SDF's) using multiresolution wavelet analysis (MWA) techniques are investigated. In the first approach, reducedresolution approximations of a full-resolution SDF are obtained by MWA. In the second approach, reduced-resolution approximations of the training-image Fourier transforms are obtained by MWA, and a reduced-resolution SDF is obtained directly by training on these. For both approaches, reducedresolution MICE-SDF filters were designed with MWA and conventional down-sampling techniques. Simulations showed that filters designed by the second approach with MWA techniques permitted reductions in the number of filter pixels from 128 × 128 to 32 × 32, while still satisfying the design constraints. In comparison, the performances of 32 × 32 filters designed by conventional downsampling techniques were significantly degraded.

Spatial filtering for speckle reduction, contrast enhancement, and texture analysis of GLORIA images

This paper reports a comparative study of digital enhancement techniques using spatial filtering to improve the geologic interpretation of side-scan sonar GLORIA images. Seven algorithms for speckle reduction with window sizes of 3/spl times/3-7/spl times/7 pixel and various numbers of iterations were tested for cosmetic purposes, and also to improve subsequent image processing. The filtered images were evaluated using both quantitative and qualitative techniques. It was determined that a normalized inverse gradient weighted smoothing scheme, with a 3/spl times/3 pixel filter and five iterations, allows a significant speckle reduction without blurring the edges in the GLORIA image which correspond to geological structures. Three local contrast enhancement techniques were also tested and evaluated to increase the perception of these geologic structures. Subtracting the gradient magnitude twice, calculated with spatial filters of a 5/spl times/5 pixel on smoothed images, was found to enhance most GLORIA images. Texture analysis methods developed for GLORIA images of mid-oceanic ridges and based on edge detection and orientation determination by spatial filtering are also presented. It enables the GLORIA mosaic of the Rodriguez triple junction (Indian Ocean) to be partitioned into regions of preferred orientation corresponding to the different seafloor fabrics generated at each arm of the triple junction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>