Using support vector machines to enhance the performance of elasticgraph matching for frontal face authentication

A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique     

Milk lipoprotein lipases: a review.

Lipoprotein lipase activity has been found in the milks from severals species where it is assumed to result from leakage from the mammary gland into milk. The function of the enzyme in the gland is apparently to assist in the transfer of blood lipoprotein triacylglycerol fatty acids into milk triacylglycerols. Bovine skim milk is one of the richest sources of lipoprotein lipase and this enzyme has been purified extensively (7000 fold) by affinity chromatography. The lipase has a molecular weight of about 62000, is inhibited by protamine sulfate, 1.0 M sodium chloride, apolipoprotein C-I (apolipoprotein-serine), and apolipoprotein C-III (apolipoprotein-alanine). The enzyme is activated by apolipoprotein C-II (apolipoprotein-glutamic acid), serum, and by heparin to which it also binds. The lipase is highly specific for the primary esters of acylglycerols and exhibits a slight stereospecificity for the sn-1 ester in preference to the sn-3-ester. Bovine milk also has separate activity toward 1-monoacylglycerols. Human milk contains a serum stimulated lipoprotein lipase with many of the characteristics of the enzyme in bovine milk, as well as an enzyme stimulated by bile salts which resembles the sterol ester hydrolase of rat pancreatic juice. The assay, function, purification, characteristics, and substrate specificities of these enzyme are discussed.     

Multichannel distance filter

A nonlinear multichannel digital filter is presented in this correspondence. The output is a weighted sum of all samples in the filter window, with a single parameter controlling the filter nonlinearity. Although input data ordering is not required, performance can surpass the performance of other ordering-based multichannel filters     

Image authentication techniques for surveillance applications

In automatic video surveillance (VS) systems, the issue of authenticating the video content is of primary importance. Given the ease with which digital images and videos can be manipulated, practically they do not have any value as legal proof, if the possibility of authenticating their content is not provided. In this paper, the problem of authenticating video surveillance image sequences is considered. After an introduction motivating the need for a watermarking-based authentication of VS sequences, a brief survey of the main watermarking-based authentication techniques is presented and the requirements that an authentication algorithm should satisfy for VS applications, are discussed. A novel algorithm which is suitable for VS visual data authentication is also presented and the results obtained by applying it to test data are discussed     

Region-based image watermarking

We introduce a novel method for embedding and detecting a chaotic watermark in the digital spatial image domain, based on segmenting the image and locating regions that are robust to several image manipulations. The robustness of the method is confirmed by experimental results that display the immunity of the embedded watermark to several kinds of attacks, such as compression, filtering, scaling, cropping, and rotation.     

Digital image processing techniques for the detection and removal of cracks in digitized paintings.

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.     

A fast implementation of 3-D binary morphological transformations

This paper proposes a fast algorithm for implementing the basic operation of Minkowski addition for the special case of binary three-dimensional (3-D) images, using 3-D structuring elements of arbitrary size and shape. The application of the proposed algorithm for all the other morphological transformations is straightforward, as they can all be expressed in terms of Minkowski addition. The efficiency of the algorithm is analyzed and some experimental results of its application are presented. As shown, the efficiency of the algorithm increases with the size of the structuring element.     

Robust watermarking of facial images based on salient geometricpattern matching

We introduce a novel method for embedding and detecting a chaotic watermark in the digital spatial domain of color facial images, based on localizing salient facial features. These features define a certain area on which the watermark is embedded and detected. An assessment of the watermarking robustness is done experimentally, by testing resistance to several attacks, such as compression, filtering, noise addition, scaling, cropping and rotation     

Nonlinear ultrasonic image processing based on signal-adaptivefilters and self-organizing neural networks

Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L(2) mean which has been proven earlier to be the ML estimator of the original signal in US B-mode data. Thus, the design of signal-adaptive L(2) mean filters is treated for US B-mode data and displayed US image data as well. Secondly, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of the learning vector quantizer (L(2 ) LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L(2) mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed L(2) LVQ NN are studied. L(2) LVQ is combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions.