IPPro: FPGA based Image Processing Processor

The paper presents IPPro which is a high performance, scalable soft-core processor targeted for image processing applications. It has been based on the Xilinx DSP48E1 architecture using the ZYNQ Field Programmable Gate Array and is a scalar 16-bit RISC processor that operates at 526MHz, giving 526MIPS of performance. Each IPPro core uses 1 DSP48, 1 Block RAM and 330 Kintex-7 slice-registers, thus making the processor as compact as possible whilst maintaining flexibility and programmability. A key aspect of the approach is in reducing the application design time and implementation effort by using multiple IPPro processors in a SIMD mode. For different applications, this allows us to exploit different levels of parallelism and mapping for the specified processing architecture with the supported instruction set. In this context, a Traffic Sign Recognition (TSR) algorithm has been prototyped on a Zedboard with the colour and morphology operations accelerated using multiple IPPros. Simulation and experimental results demonstrate that the processing platform is able to achieve a speedup of 15 to 33 times for colour filtering and morphology operations respectively, with a reduced design effort and time.

Implementation of Content Centric Networks Through Domain Name System

Content Centric Network (CCN) is a proposed future internet architecture that is based on the concept of contents name instead of the hosts name followed in the traditional internet architecture. CCN architecture might do changes in the existing internet architecture or might replace it completely. In this paper, we present modifications to the existing Domain Name System (DNS) based on the CCN architecture requirements without changing the existing routing architecture. Hence the proposed solution achieves the benefits of both CCN and existing network infrastructure (i.e. content based routing, independent of host location, caching and content delivery protocols).

Characterisation and evaluation of novel surfactant bacterial anti-adherent coatings for endotracheal tubes designed for the prevention of ventilator-associated pneumonia.

It is accepted that ventilator-associated pneumonia is a frequent cause of morbidity and mortality in intensive care patients. This study describes the physicochemical properties of novel surfactant coatings of the endotracheal tube and the resistance to microbial adherence of surfactant coated endotracheal tube polyvinylchloride (PVC). Organic solutions of surfactants containing a range of ratios of cholesterol and lecithin (0:100, 25:75, 50:50, 75:25, dissolved in dichloromethane) were prepared and coated onto endotracheal tube PVC using a multiple dip-coating process. Using modulated temperature differential scanning calorimetry it was confirmed that the binary surfactant systems existed as physical mixtures. The surface properties of both surfactant-coated and uncoated PVC, following treatment with either pooled human saliva or phosphate-buffered saline (PBS), were characterised using dynamic contact angle analysis. Following treatment with saliva, the contact angles of PVC decreased; however, those of the coated biomaterials were unaffected, indicating different rates and extents of macromolecular adsorption from saliva onto the coated and uncoated PVC. The advancing and receding contact angles of the surfactant-coated PVC were unaffected by sonication, thereby providing evidence of the durability of the coatings. The cell surface hydrophobicity and zeta potentials of isolates of Staphylococcus aureus and Pseudomonas aeruginosa, following treatment with either saliva or PBS, and their adherence to uncoated and surfactant-coated PVC (that had been pre-treated with saliva) were examined. Adherence of S. aureus and Ps. aeruginosa to surfactant-coated PVC at each successive time period (0.5, 1, 2, 4, 8 h) was significantly lower than to uncoated PVC, the extent of the reduction frequently exceeding 90%. Interestingly, the microbial anti-adherent properties of the coatings were dependent on the lecithin content. Based on the impressive microbial anti-adherence properties and durability of the surfactant coating on PVC following dip coatings, it is proposed that these systems may usefully reduce the incidence of ventilator-associated pneumonia when employed as luminal coatings of the endotracheal tube.

Approach to automatic video motion segmentation

A novel, fast automatic motion segmentation approach is presented. It differs from conventional pixel or edge based motion segmentation approaches in that the proposed method uses labelled regions (facets) to segment various video objects from the background. Facets are clustered into objects based on their motion and proximity details using Bayesian logic. Because the number of facets is usually much lower than the number of edges and points, using facets can greatly reduce the computational complexity of motion segmentation. The proposed method can tackle efficiently the complexity of video object motion tracking, and offers potential for real-time content-based video annotation.

Electrical estimulation of retinal pigment epithelial cells

We investigated and characterized the effect of externally applied electric fields (EF) on retinal pigment epithelial (RPE) cells by exposing primary cultures of human RPE cells (hRPE) and those from the ARPE19 immortalized cell line to various strengths of EF (EF-treated cells) or to no EF (control cells) under different conditions including presence or absence of serum and gelatin and following wounding. We evaluated changes in RPE cell behavior in response to EF by using a computer based image capture and analysis system (Metamorph). We found that RPE cells responded to externally applied EFs by preferential orientation perpendicular to the EF vector, directed migration towards the anode, and faster translocation rate than control, untreated cells. These responses were voltage-dependent. Responses were observed even at low voltages, of 50-300 mV. Furthermore, the migration of hRPE cell sheets generated by wounding of confluent monolayers of cells at early and late confluence could be manipulated by the application of EF, with directed migration towards the anode observed at both sides of the wounded hRPE. In conclusion, RPE cell behaviour can be controlled by an externally applied EF. The potential for externally applied EF to be used as a therapeutic strategy in the management of selected retinal diseases warrants further investigation. © 2010 Elsevier Ltd.

FPGA-based minutia matching for biometric fingerprint image database retrieval

In this paper, a parallel-matching processor architecture with early jump-out (EJO) control is proposed to carry out high-speed biometric fingerprint database retrieval. The processor performs the fingerprint retrieval by using minutia point matching. An EJO method is applied to the proposed architecture to speed up the large database retrieval. The processor is implemented on a Xilinx Virtex-E, and occupies 6,825 slices and runs at up to 65 MHz. The software/hardware co-simulation benchmark with a database of 10,000 fingerprints verifies that the matching speed can achieve the rate of up to 1.22 million fingerprints per second. EJO results in about a 22% gain in computing efficiency.

SVD-Based Universal Spatial Domain Image Steganalysis

This paper is concerned with the universal (blind) image steganalysis problem and introduces a novel method to detect especially spatial domain steganographic methods. The proposed steganalyzer models linear dependencies of image rows/columns in local neighborhoods using singular value decomposition transform and employs content independency provided by a Wiener filtering process. Experimental results show that the novel method has superior performance when compared with its counterparts in terms of spatial domain steganography. Experiments also demonstrate the reasonable ability of the method to detect discrete cosine transform-based steganography as well as the perturbation quantization method.

Thresholding of noisy shoeprint images based on pixel context

In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.

Far-Field Optical Microscopy with a Nanometer-Scale Resolution Based on the In-Plane Image Magnification by Surface Plasmon Polaritons

A new far-field optical microscopy capable of reaching nanometer-scale resolution is developed using the in-plane image magnification by surface plasmon polaritons. This approach is based on the optical properties of a metal-dielectric interface that may provide extremely large values of the effective refractive index neff up to 103 as seen by surface polaritons, and thus the diffraction limited resolution can reach nanometer-scale values of lambda/2neff. The experimental realization of the microscope has demonstrated the optical resolution better than 60 nm at 515 nm illumination wavelength.

Custom coprocessor based matrix algorithms for image and signal processing

Matrix algorithms are important in many types of applications including image and signal processing. A close examination of the algorithms used in these, and related, applications reveals that many of the fundamental actions involve matrix algorithms such as matrix multiplication. This paper presents an investigation into the design and implementation of different matrix algorithms such as matrix operations, matrix transforms and matrix decompositions using a novel custom coprocessor system for MATrix algorithms based on Reconfigurable Computing (RCMAT). The proposed RCMAT architectures are scalable, modular and require less area and time complexity with reduced latency when compared with existing structures.

Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.