Robust coding in a global subspace model and its collaboration with a local model for visual tracking

Multimedia Tools and Applications - The sparse representation-based trackers has attracted much attention in the research community due to its superior performance in spite of its computational...     

A method for preserving the classifiability of digital images after performing a wavelet-based compression

A measure is introduced that predicts the number of coefficients needed to be retained in the discrete wavelet transform of images in order to maintain their classifiability. The introduction of the criterion is based on the energy content of the wavelet coefficients and the order in which they are scanned. The coefficients are weighted based on their location acquired by Morton scanning of the two-dimensional transform plane. The proposed criterion has been tested on MIT-CBCL and AT&T-Olivetti face databases, Columbia Object Image Library (COIL-20) object database, the MNIST handwritten character recognition database and on Caltech-101 object image database. To demonstrate the efficiency of the proposed method, several classification experiments are conducted on each database. Simulation results show that the proposed method can maintain the same classifiability as that of uncompressed data with only a small fraction of the wavelet coefficients.     

On-chip transduction of nucleic acid hybridization using spatial profiles of immobilized quantum dots and fluorescence resonance energy transfer

The glass surface of a glass-polydimethylsiloxane (PDMS) microfluidic channel was modified to develop a solid-phase assay for quantitative determination of nucleic acids. Electroosmotic flow (EOF) within channels was used to deliver and immobilize semiconductor quantum dots (QDs), and electrophoresis was used to decorate the QDs with oligonucleotide probe sequences. These processes took only minutes to complete. The QDs served as energy donors in fluorescence resonance energy transfer (FRET) for transduction of nucleic acid hybridization. Electrokinetic injection of fluorescent dye (Cy3) labeled oligonucleotide target into a microfluidic channel and subsequent hybridization (within minutes) provided the proximity for FRET, with emission from Cy3 being the analytical signal. The quantification of target concentration was achieved by measurement of the spatial length of coverage by target along a channel. Detection of femtomole quantities of target was possible with a dynamic range spanning an order of magnitude. The assay provided excellent resistance to nonspecific interactions of DNA. Further selectivity of the assay was achieved using 20% formamide, which allowed discrimination between a fully complementary target and a 3 base pair mismatch target at a contrast ratio of 4:1.     

Visual tracking using structural local DCT sparse appearance model with occlusion detection

Multimedia Tools and Applications - In this paper, a structural local DCT sparse appearance model with occlusion detection is proposed for visual tracking in a particle filter framework. The energy...     

A Discriminant Model for the Pattern Recognition of Linearly Independent Samples

Fisher’s linear discriminant analysis (FLDA) has been used in many pattern recognition applications. However, this method cannot be applied for solving the pattern recognition problems when the within-class scatter matrix is singular, i.e., when the so-called small sample size problem occurs. Many FLDA variants that have been proposed in the past to circumvent this problem either suffer from excessive computational load when patterns have a large dimension or lose some useful discriminant information. In this paper, a new systematic framework for the pattern recognition of datasets with linearly independent samples is developed. Within this framework, a discriminant model, in which the samples of the individual classes of a dataset lie on parallel hyperplanes and project to single distinct points of a discriminant subspace of the underlying input space, is shown to exist. Based on this model, three algorithms that do not encounter the adverse effects of the small sample size (SSS) problem are developed to obtain such a discriminant subspace for a given dataset with linearly independent samples. A kernelized algorithm is also developed for the discriminant analysis of datasets for which the samples are not linearly independent. Simulation results are provided to examine the validity of the proposed discriminant model and to demonstrate the effectiveness, both in terms of complexity and classification accuracy, of the linear and nonlinear algorithms designed based on the proposed model.     

An edge-adapting Laplacian kernel for nonlinear diffusion filters

In this paper, first, a new Laplacian kernel is developed to integrate into it the anisotropic behavior to control the process of forward diffusion in horizontal and vertical directions. It is shown that, although the new kernel reduces the process of edge distortion, it nonetheless produces artifacts in the processed image. After examining the source of this problem, an analytical scheme is devised to obtain a spatially varying kernel that adapts itself to the diffusivity function. The proposed spatially varying Laplacian kernel is then used in various nonlinear diffusion filters starting from the classical Perona-Malik filter to the more recent ones. The effectiveness of the new kernel in terms of quantitative and qualitative measures is demonstrated by applying it to noisy images.     

A functional perspective on map generalisation

In the context of map generalisation, the ambition is to store once and then maintain a very detailed geographic database. Using a mix of modelling and cartographic generalisation techniques, the intention is to derive map products at varying levels of detail – from the fine scale to the highly synoptic. We argue that in modelling this process, it is highly advantageous to take a ‘functional perspective’ on map generalisation – rather than a geometric one. In other words to model the function as it manifests itself in the shapes and patterns of distribution of the phenomena being mapped – whether it be hospitals, airports, or cities. By modelling the functional composition of such features we can create relationships (partonomic, taxonomic and topological) that lend themselves directly to modelling, to analysis and most importantly to the process of generalisation. Borrowing from ideas in robotic vision this paper presents an approach for the automatic identification of functional sites (a collection of topographic features that perform a collective function) and demonstrates their utility in multi-scale representation and generalisation.     

Simulation of second order singularly-perturbed boundary-value problems using improved meshless method

Abstract

In this study, numerical integration based on Block-Pulse functions and Chebyshev wavelets are employed for Element Free Galerkin approximation. Moving Least Squares (MLS) approach is used to construct shape functions with optimized weight functions and basis. The proposed techniques are implemented on singularly-perturbed boundary-value problems with two-point boundary conditions. Numerical results for two examples are presented in this article to show the pertinent features for the proposed technique. Comparison with existing techniques shows that our proposed method based on integration technique provides better approximation at reduced computational cost. Moreover the effect of perturbation parameter on solution of test problems has been studied.     

All digital skew tolerant synchronous interfacing methods for high-performance point-to-point communications in deep sub-micron SoCs

High-performance clocking of intellectual property (IP) modules, within a skew budget, is becoming difficult in deep sub-micron technologies. In this work, we propose a novel and all-digital synchronous design method for point-to-point communications, using two stages of interfacing registers and locally delayed clock with phase adjustments. This design is free from synchronizers and clock-data mismatch problems. Moreover, communicating modules run at frequencies which are virtually independent of the clock skew. We also provide a comprehensive case-wise mathematical analysis to facilitate design automation for synthesizing such designs as standard cells. An overall improvement in skew tolerance of up to n times (where n is the number of registers used), when compared to conventional designs, is achieved when the skew orientation is known and n/2 times if the skew orientation is unknown. Improvement in skew tolerance is validated using gate level simulations with the 0.18 μm TSMC CMOS technology. A prototype implementation of the proposed design using a Virtex-II Pro FPGA from Xilinx validates the claim that such designs allow a fast module to communicate with a slow module without constraining their frequencies.     

Salient region detection using efficient wavelet-based textural feature maps

A salient region is part of an image that captures the highest level of attention by the human visual system. In this paper, a new salient region detection method is proposed by linearly combining the feature maps for the L, a and b color channels. Since, the wavelet transform is capable of providing a multi-scale spatial-frequency decomposition of the image, the color feature maps are obtained using this transform. A scheme is proposed whereby the channel feature maps are linearly combined. The weights for the linear combination are determined by making use of the entropy of the channel feature maps and a Gaussian kernel, utilizing the fact that the salient objects are generally clustered and scene-centric. The salient region is further refined by making use of the proximity of the pixels to the centers of gravity in the image feature map. Extensive simulations are conducted in order to evaluate the performance of the proposed saliency detection scheme by applying it to the natural images from several datasets. Experimental results show that the proposed method provides values of precision, recall and F-measure larger than and that of the mean absolute error smaller than those provided by other existing methods. The performance of the proposed salient region detection method is also evaluated on noisy images and it is shown to be robust against noise.