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.

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available. 

The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

SEMAINE has created a large audiovisual database as a part of an iterative approach to building Sensitive Artificial Listener (SAL) agents that can engage a person in a sustained, emotionally colored conversation. Data used to build the agents came from interactions between users and an operator simulating a SAL agent, in different configurations: Solid SAL (designed so that operators displayed an appropriate nonverbal behavior) and Semi-automatic SAL (designed so that users' experience approximated interacting with a machine). We then recorded user interactions with the developed system, Automatic SAL, comparing the most communicatively competent version to versions with reduced nonverbal skills. High quality recording was provided by five high-resolution, high-framerate cameras, and four microphones, recorded synchronously. Recordings total 150 participants, for a total of 959 conversations with individual SAL characters, lasting approximately 5 minutes each. Solid SAL recordings are transcribed and extensively annotated: 6-8 raters per clip traced five affective dimensions and 27 associated categories. Other scenarios are labeled on the same pattern, but less fully. Additional information includes FACS annotation on selected extracts, identification of laughs, nods, and shakes, and measures of user engagement with the automatic system. The material is available through a web-accessible database. © 2010-2012 IEEE.

Annotated Chromosome Maps for Renal Disease

A combination of linkage analyses and association studies are currently employed to promote the identification of genetic factors contributing to inherited renal disease. We have standardized and merged complex genetic data from disparate sources, creating unique chromosomal maps to enhance genetic epidemiological investigations. This database and novel renal maps effectively summarize genomic regions of suggested linkage, association, or chromosomal abnormalities implicated in renal disease. Chromosomal regions associated with potential intermediate clinical phenotypes have been integrated, adding support for particular genomic intervals. More than 500 reports from medical databases, published scientific literature, and the World Wide Web were interrogated for relevant renal-related information. Chromosomal regions highlighted for prioritized investigation of renal complications include 3q13-26, 6q22-27, 10p11-15, 16p11-13, and 18q22. Combined genetic and physical maps are effective tools to organize genetic data for complex diseases. These renal chromosome maps provide insights into renal phenotype-genotype relationships and act as a template for future genetic investigations into complex renal diseases. New data from individual researchers and/or future publications can be readily incorporated to this resource via a user-friendly web-form accessed from the website: www.qub.ac.uk/neph-res/CORGI/index.php.

TMAX – a Database System Integrating Tissue Biomarker & Genomic Data

Background: Popular approaches in human tissue-based biomarker discovery include tissue microarrays (TMAs) and DNA Microarrays (DMAs) for protein and gene expression profiling respectively. The data generated by these analytic platforms, together with associated image, clinical and pathological data currently reside on widely different information platforms, making searching and cross-platform analysis difficult. Consequently, there is a strong need to develop a single coherent database capable of correlating all available data types.

Method: This study presents TMAX, a database system to facilitate biomarker discovery tasks. TMAX organises a variety of biomarker discovery-related data into the database. Both TMA and DMA experimental data are integrated in TMAX and connected through common DNA/protein biomarkers. Patient clinical data (including tissue pathological data), computer assisted tissue image and associated analytic data are also included in TMAX to enable the truly high throughput processing of ultra-large digital slides for both TMAs and whole slide tissue digital slides. A comprehensive web front-end was built with embedded XML parser software and predefined SQL queries to enable rapid data exchange in the form of standard XML files.

Results & Conclusion: TMAX represents one of the first attempts to integrate TMA data with public gene expression experiment data. Experiments suggest that TMAX is robust in managing large quantities of data from different sources (clinical, TMA, DMA and image analysis). Its web front-end is user friendly, easy to use, and most importantly allows the rapid and easy data exchange of biomarker discovery related data. In conclusion, TMAX is a robust biomarker discovery data repository and research tool, which opens up the opportunities for biomarker discovery and further integromics research.

The Belfast Storytelling Database: A spontaneous social interaction database with laughter focused annotation

To support the endeavor of creating intelligent interfaces between computers and humans the use of training materials based on realistic human-human interactions has been recognized as a crucial task. One of the effects of the creation of these databases is an increased realization of the importance of often overlooked social signals and behaviours in organizing and orchestrating our interactions. Laughter is one of these key social signals; its importance in maintaining the smooth flow of human interaction has only recently become apparent in the embodied conversational agent domain. In turn, these realizations require training data that focus on these key social signals. This paper presents a database that is well annotated and theoretically constructed with respect to understanding laughter as it is used within human social interaction. Its construction, motivation, annotation and availability are presented in detail in this paper.