Estimation precision of degree of polarization in the presence of signal-dependent and additive Poisson noises

We address precision of estimation of the degree of polarization (DOP) from the orthogonal state contrast image (OSCI) in the presence of both signal-dependent Poisson noise due to useful signal, and additive Poisson noise due to dark current and / or background light. We determine the Cramer Rao Lower Bound and deduce from it figures of merit for DOP estimation. In particular, we show that the additive Poisson noise has larger influence on DOP estimation than on intensity estimation when light is highly polarized

Segmentation non supervisée d'images polarimétriques passives

Ce papier montre l'intérêt de la segmentation par grille active multi-composantes dans le traitement d'images obtenues par un système d'imagerie polarimétrique de Stokes. L'utilisation conjointe d'un système d'imagerie non-conventionnel performant et d'une méthode de traitement (segmentation) adaptée peut ainsi être mis à profit pour des applications de contrôle non-destructif. L'exemple présenté illustre en particulier la possibilité de segmenter l'image d'un objet suivant l'orientation géométrique de ses diverses facettes

Experimental demonstration of extended depth-of-field f/1.2 visible High Definition camera with jointly optimized phase mask and real-time digital processing

Increasing the depth of field (DOF) of compact visible high resolution cameras while maintaining high imaging performance in the DOF range is crucial for such applications as night vision goggles or industrial inspection. In this paper, we present the end-to-end design and experimental validation of an extended depth-of-field visible High Definition camera with a very small f-number, combining a six-ring pyramidal phase mask in the aperture stop of the lens with a digital deconvolution. The phase mask and the deconvolution algorithm are jointly optimized during the design step so as to maximize the quality of the deconvolved image over the DOF range. The deconvolution processing is implemented in real-time on a Field-Programmable Gate Array and we show that it requires very low power consumption. By mean of MTF measurements and imaging experiments we experimentally characterize the performance of both cameras with and without phase mask and thereby demonstrate a significant increase in depth of field of a factor 2.5, as it was expected in the design step

Polarimetric SAR Image Segmentation with B-Splines and a New Statistical Model

We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or manually, and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the parameters of the polarimetric GHP model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This is a local algorithm since it works only on the region to be segmented. Results of its performance are presented

Gene selection for classification of microarray data based on the Bayes error

<p>Abstract</p> <p>Background</p> <p>With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.</p> <p>Results</p> <p>In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.</p> <p>Conclusion</p> <p>The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.</p

On the equivalence of optimization metrics in Stokes polarimetry

Optimization of polarimeters has historically been achieved using an assortment of performance metrics. Selection of an optimization parameter is, however, frequently made on an ad hoc basis. We rigorously demonstrate that optimization strategies in Stokes polarimetry based on three common metrics, namely the Frobenius condition number of the instrument matrix, the determinant of the associated Gram matrix, or the equally weighted variance, are frequently formally equivalent. In particular, using each metric, we derive the same set of constraints on the measurement states, correcting a previously reported proof, and show that these can be satisfied using spherical 2 designs. Discussion of scenarios in which equivalence between the metrics breaks down is also given. Our conclusions are equally applicable to optimization of the illumination states in Mueller matrix polarimetry

Comparative study of the best achievable contrast in scalar, Stokes and Mueller images

We compare the relative performance of different active polarimetric imaging architectures for target detection applications. We show that if the noise that affects the measurements is additive and if the only relevant parameter is the contrast between an object of interest and a background with different Mueller matrices, the most efficient imaging architecture consists in acquiring a single intensity image while optimizing the illumination and analysis states of polarization