A survey of the state of the art and focused research in range systems, task 2

Contract generated publications are compiled which describe the research activities for the reporting period. Study topics include: equivalent configurations of systolic arrays; least squares estimation algorithms with systolic array architectures; modeling and equilization of nonlinear bandlimited satellite channels; and least squares estimation and Kalman filtering by systolic arrays

AN EFFECTIVE APPROACH OF BILATERAL FILTER IMPLEMENTATION IN SPARTAN-3 FIELD PROGRAMMABLE GATE ARRAY

This paper presents the Field Programmable Gate Array (FPGA) implementation of Bilateral Filter, in order to achieve high performance and low power consumption. Bilateral filtering is a technique to smooth images while preserving edges by means of a nonlinear combination of nearby image values. This method is nonlinear, local, and simple. We give an idea that bilateral filtering can be accelerated by bilateral grid scheme that enables fast edge-aware image processing. Nowadays, most of the applications require real time hardware systems with large computing potentiality for which fast and dedicated Very Large Scale Integration (VLSI) architecture appears to be the best possible solution. While it ensures high resource utilization, that too in cost effective platforms like FPGA, designing such architecture does offers some flexibilities like speeding up the computation by adapting more pipelined structures and parallel processing possibilities of reduced memory consumptions. Here we have developed an effective approach of bilateral filter implementation in Spartan-3 FPGA

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

The Haar Wavelet Transform of a Dendrogram: Additional Notes

We consider the wavelet transform of a finite, rooted, node-ranked, $p$-way tree, focusing on the case of binary ($p = 2$) trees. We study a Haar wavelet transform on this tree. Wavelet transforms allow for multiresolution analysis through translation and dilation of a wavelet function. We explore how this works in our tree context.Comment: 37 pp, 1 fig. Supplementary material to "The Haar Wavelet Transform of a Dendrogram", http://arxiv.org/abs/cs.IR/060810

Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments

Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms.This paper was performed while Eric A. Lehmann was working with National ICT Australia. National ICT Australia is funded by the Australian Government’s Department of Communications, Information Technology, and the Arts, the Australian Research Council, through Backing Australia’s Ability, and the ICT Centre of Excellence programs

Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing

We describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing. The network consists of magneto-electric cells integrated onto a common ferromagnetic film - spin wave bus. The magneto-electric cell is an artificial two-phase multiferroic structure comprising piezoelectric and ferromagnetic materials. A bit of information is assigned to the cell's magnetic polarization, which can be controlled by the applied voltage. The information exchange among the cells is via the spin waves propagating in the spin wave bus. Each cell changes its state as a combined effect of two: the magneto-electric coupling and the interaction with the spin waves. The distinct feature of the network with spin wave bus is the ability to control the inter-cell communication by an external global parameter - magnetic field. The latter makes possible to realize different image processing functions on the same template without rewiring or reconfiguration. We present the results of numerical simulations illustrating image filtering, erosion, dilation, horizontal and vertical line detection, inversion and edge detection accomplished on one template by the proper choice of the strength and direction of the external magnetic field. We also present numerical assets on the major network parameters such as cell density, power dissipation and functional throughput, and compare them with the parameters projected for other nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and Quantum Dot Image Processor. Potentially, the utilization of spin waves phenomena at the nanometer scale may provide a route to low-power consuming and functional logic circuits for special task data processing