In this paper, we propose a re-weighted elastic
net (REN) model for biometric recognition. The new model
is applied to data separated into geometric and color spatial
components. The geometric information is extracted using a
fast cartoon - texture decomposition model based on a dual
formulation of the total variation norm allowing us to carry
information about the overall geometry of images. Color
components are defined using linear and nonlinear color
spaces, namely the red-green-blue (RGB), chromaticitybrightness
(CB) and hue-saturation-value (HSV). Next,
according to a Bayesian fusion-scheme, sparse representations
for classification purposes are obtained. The scheme
is numerically solved using a gradient projection (GP) algorithm.
In the empirical validation of the proposed model,
we have chosen the periocular region, which is an emerging
trait known for its robustness against low quality data.
Our results were obtained in the publicly available FRGC
and UBIRIS.v2 data sets and show consistent improvements in recognition effectiveness when compared to related state-of-
the-art techniques.
@article{Surya:Perio-Sparse-JSPS-2015,
author = "J. C. Moreno and V. B. S. Prasath and G. Santos and H. Proenca",
title = "Robust periocular recognition by fusing sparse representations of color and geometry information",
year = 2015,
journal = "Journal of Signal Processing Systems",
month = "Jul",
keywords = "periocular, biometrics, decomposition",
doi = "10.1007/s11265-015-1023-3",
url = "http://link.springer.com/article/10.1007%2Fs11265-015-1023-3"
}
J. C. Moreno, V. B. S. Prasath, G. Santos, and H. Proenca. Robust periocular recognition by fusing sparse representations of color and geometry information. Journal of Signal Processing Systems, July 2015.
We study a regularization algorithm for color/vectorial images based on the vectorial total variation approach along with channel coupling for color image processing which facilitates the modeling of inter channel relations in multidimensional image data. We focus on penalizing channel gradient magnitude similarities by using L2 differences, which allow us to couple all the channels along with a vectorial total variation regularization for edge preserving smoothing of multi-channel images. A detailed mathematical analysis of the coupled vectorial total variation is provided. We are interested of applying our model to color image processing and in particular to denoising and decomposition. A fast global minimization based on the dual formulation of the total variation is used in our implementations which provides good decomposition and denoising results. Comparison with previous color image decomposition and denoising models are provided to demonstrate the advantages of our scheme.
@article{PrasathIPI15,
author = "J. C. Moreno and V. B. S. Prasath and J. C. Neves",
title = "Color image processing by vectorial total variation with gradient channels coupling",
year = 2015,
journal = "Inverse Problems and Imaging",
keywords = "restoration, total variation, color image processing"
}
J. C. Moreno, V. B. S. Prasath, and J. C. Neves. Color image processing by vectorial total variation with gradient channels coupling. Inverse Problems and Imaging, 2015.
Multiphase active contour based models are useful in identifying multiple regions with spatial consistency but varying characteristics such as the mean intensities of regions. Segmenting brain magnetic resonance images (MRIs) using a multiphase approach is useful to differentiate white and gray matter tissue for anatomical, functional and disease studies. Multiphase active contour methods are superior to other approaches due to their topological flexibility, accurate boundaries, robustness to image variations and adaptive energy functionals. Globally convex methods are furthermore initialization independent. We extend the relaxed globally convex Chan and Vese two-phase piecewise constant energy minimization formulation of Chan et al. (2006) [1] to the multiphase domain and prove the existence of a global minimizer in a specific space which is one of the novel contributions of the paper. An efficient dual minimization implementation of our binary partitioning function model accurately describes disjoint regions using stable segmentations by avoiding local minima solutions. Experimental results indicate that the proposed approach provides consistently better accuracy than other related multiphase active contour algorithms using four different error metrics (Dice, Rand Index, Global Consistency Error and Variation of Information) even under severe noise, intensity inhomogeneities, and partial volume effects in MRI imagery.
@article{Juan:BrainMRI-Segmentation-CVIU-2014,
author = "J. C. Moreno and V. B. S. Prasath and H. Proenca and K. Palaniappan",
title = "Fast and globally convex multiphase active contours for brain MRI segmentation",
year = 2014,
journal = "Computer Vision and Image Understanding",
volume = 125,
pages = "237-250",
month = "Aug",
keywords = "segmentation, active contours, globally convex, biomedical",
doi = "10.1016/j.cviu.2014.04.010",
url = "http://cell.missouri.edu/pages/BrainMRISegmentation/"
}
J. C. Moreno, V. B. S. Prasath, H. Proenca, and K. Palaniappan. Fast and globally convex multiphase active contours for brain MRI segmentation. Computer Vision and Image Understanding, volume 125, pages 237-250, August 2014.
Anisotropic diffusion based schemes are widely used in image smoothing and noise removal. Typically, the partial differential equation (PDE) used is based on computing image gradients or isotropically smoothed version of the gradient image. To improve the denoising capability of such nonlinear anisotropic diffusion schemes, we introduce a multi-direction based discretization along with a selection strategy for choosing the best direction of possible edge pixels. This strategy avoids the directionality based bias which can over-smooth features that are not aligned with the coordinate axis. The proposed hybrid discretization scheme helps in preserving multi-scale features present in the images via selective smoothing of the PDE. Experimental results indicate such an adaptive modification provides improved restoration results on noisy images.
@inproceedings{Surya:feature-restoration-NCVPRIPG-2013,
author = "V. B. S. Prasath and J. C. Moreno",
title = "Feature preserving anisotropic diffusion for image restoration",
year = 2013,
journal = "Proc. IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)",
pages = "1-4",
month = "Dec",
keywords = "restoration, anisotropic diffusion",
doi = "10.1109/NCVPRIPG.2013.6776250"
}
V. B. S. Prasath and J. C. Moreno. Feature preserving anisotropic diffusion for image restoration. Proc. IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pages 1-4, December 2013.
Sparse representations have been advocated as a relevant advance in biometrics research. In this paper a new algorithm for fusion at the data level of sparse representations is proposed, each one obtained from image patches. The main novelties are two-fold: 1) a dictionary fusion scheme is formalised, using the L1 minimization with the gradient projection method; 2) the proposed representation and classification method does not require the non-overlapping condition of image patches from where individual dictionaries are obtained. In the experiments, we focused in the recognition of periocular images and obtained independent dictionaries for the eye, eyebrow and skin regions, that were subsequently fused. Results obtained in the publicly available UBIRIS.v2 data set show consistent improvements in the recognition effectiveness when compared to state-of-the-art related representation and classification techniques.
@inproceedings{Surya:Sparse-Periocular-SIN-2013,
author = "J. C. Moreno and V. B. S. Prasath and H. Proenca",
title = "Robust periocular recognition by fusing local to holistic sparse representations",
year = 2013,
booktitle = "6th International Conference on Security of Information and Networks (SIN)",
pages = "160-164",
month = "Dec",
keywords = "periocular, recognition, sparse",
doi = "10.1145/2523514.2523540"
}
J. C. Moreno, V. B. S. Prasath, and H. Proenca. Robust periocular recognition by fusing local to holistic sparse representations. 6th International Conference on Security of Information and Networks (SIN), pages 160-164, December 2013.
In this paper a variational segmentation model is proposed. It is a generalization of the Chan and Vese model, for the scalar and vector-valued cases. It incorporates extra terms, depending on the image gradient, and aims at approximating the smoothed image gradient norm, inside and outside the segmentation curve, by mean constant values. As a result, a flexible model is obtained. It segments, more accurately, any object displaying many oscillations in its interior. In effect, an external contour of the object, as a whole, is achieved, together with internal contours, inside the object. For determining the approximate solution a Levenberg-Marquardt Newton-type optimization method is applied to the finite element discretization of the model. Experiments on in vivo medical endoscopic images (displaying aberrant colonic crypt foci) illustrate the efficacy of this model.
@inproceedings{Surya:CryptFoci_Segmentation_ICIAR_2012,
author = "I. N. Figueiredo and J. C. Moreno and V. B. S. Prasath and P. N. Figueiredo",
title = "A segmentation model and application to endoscopic images",
year = 2012,
booktitle = "Proc. International Conference on Image Analysis and Recognition (ICIAR)",
publisher = "Springer LNCS 7325",
pages = "164-171",
month = "Jun",
keywords = "segmentation, active contours, biomedical, endoscopy",
doi = "10.1007/978-3-642-31298-4_20"
}
I. N. Figueiredo, J. C. Moreno, V. B. S. Prasath, and P. N. Figueiredo. A segmentation model and application to endoscopic images. Proc. International Conference on Image Analysis and Recognition (ICIAR), Springer LNCS 7325, pages 164-171, June 2012.
We study a region based segmentation scheme for images with textures based on the gradient information weighted by local image intensity histograms. It relies on the Chan and Vese model without any edge-detectors, and incorporates a new input term, defined by the product of the smoothed gradient and a local histogram of pixel intensity measure of the input image. Segmentation of images with texture objects is performed by effectively differentiating regions displaying different textural information via the local histogram features. A fast numerical scheme based on the dual formulation of the energy minimization is considered. The performance of the proposed scheme is tested on different natural images which contain texture objects.
@inproceedings{Surya:Texture_Segmentation_COMPIMAGE_2012,
author = "I. N. Figueiredo and J. C. Moreno and V. B. S. Prasath",
title = "Texture image segmentation with smooth gradients and local information",
year = 2012,
booktitle = "Proc. Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications (CompIMAGE)",
pages = "115-119",
keywords = "segmentation, active contours, texture, globally convex",
doi = "10.1201/b12753-21"
}
I. N. Figueiredo, J. C. Moreno, and V. B. S. Prasath. Texture image segmentation with smooth gradients and local information. Proc. Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications (CompIMAGE), pages 115-119, 2012.
A variational segmentation model for images with texture is proposed. It relies on the Chan and Vese model, and incorporates an extended structure tensor of the image, defined by a coupling of the image with its first-order spatial derivatives. The average components of the extended structure tensor is also utilized in the model, by means of its gradient. Moreover, a pre-smoothing of this gradient and the structure tensor is performed, which makes the computations robust to noise. As a result, an effective segmentation model is obtained, which distinguishes and segments objects with texture information. For the numerical approximation of this model, a finite element discretization is used, and a Levenberg-Marquard-Newton type optimization method is applied. An application of the proposed scheme is to segment colonic polyps from human intestinal system images, captured by wireless capsule endoscopy. We present experiments on some synthetic and wireless capsule endoscopic textured images, for evaluation and validation of the proposed model.
@inproceedings{PrasathTextureCMNE11,
author = "J. C. Moreno and I. N. Figueiredo and V. B. S. Prasath",
title = "Texture image segmentation using higher order derivatives",
year = 2011,
booktitle = "Congress on Numerical Methods in Engineering (CMNE)",
pages = "10pp",
month = "Jun",
keywords = "texture, segmentation"
}
J. C. Moreno, I. N. Figueiredo, and V. B. S. Prasath. Texture image segmentation using higher order derivatives. Congress on Numerical Methods in Engineering (CMNE), pages 10pp, June 2011.