This PDF file contains the front matter associated with SPIE Proceedings Volume 8401, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Theory of wavelet frames and their applications to image restoration problems have been extensively studied for the past two decades. The success of wavelet frames in solving image restoration problems, which includes denoising, deblurring, inpainting, computed tomography, etc., is mainly due to their capability of sparsely approximating piecewise smooth functions such as images. However, in contrast to the wide applications of wavelet frame based approaches to image restoration problems, they are rarely used for some image/data analysis tasks, such as image segmentation, registration and surface reconstruction from unorganized point clouds. The main reason for this is the lack of geometric interpretations of wavelet frames and their associated transforms. Recently, geometric meanings of wavelet frames have been discovered and connections between the wavelet frame based approach and the differential operator based variational model were established.1 Such discovery enabled us to extend the wavelet frame based approach to some image/data analysis tasks that have not yet been studied before. In this paper, we will provide a unified survey of the wavelet frame based models for image segmentation and surface reconstruction from unorganized point clouds. Advantages of the wavelet frame based approach are illustrated by numerical experiments.

We introduced human sciences as components, and integrated them together as an interdisciplinary endeavor over decades. This year, we built a website to maintain systematically the educational research service. We captured the human sciences in various components in the SPIE proceedings over the last decades, which included: (i) ears & eyes like adaptive wavelets, (ii) brain-like unsupervised learning independent component analysis (ICA); (iii) compressive sampling spatiotemporal sparse information processing, (iv) nanoengineering approach to sensing components, (v) systems biology measurements, and (vi) biomedical wellness applications. In order to serve the interdisciplinary community better, our system approach is based on that the former recipients invited the next recipients to deliver their review talks and panel discussions. Since only the former recipients of each component can lead the nomination committees and make the final selections, we also create a leadership award which may be nominated by any conference attendance, to be approved by the conference organization committee.

We analyze Schroedinger Eigenmaps - a new semi-supervised manifold learning and recovery technique - for applications in hyperspectral imagery. This method is based on an implementation of graph Schroedinger operators with appropriately constructed potentials as carriers of expert/labeled information. In this paper, we analyze the features of Schroedinger Eigenmaps through analysis of the potential locations and their imapct on the classication. The imaging modalities which we shall incorporate in our analysis include multispectral and hyperspectral imagery. For the purpose of constructing ecient methods for building the potentials we refer to expert ground-truth data, as well as to using automated clustering techniques. We also investigate the role of dierent sources of the barrier potential locations, and the role they play in the separation of classes.

A wavelet-based method is proposed for multispectral face recognition in this paper. Gabor wavelet transform is a common tool for orientation analysis of a 2D image; whereas Hamming distance is an efficient distance measurement for face identification. Specifically, at each frequency band, an index number representing the strongest orientational response is selected, and then encoded in binary format to favor the Hamming distance calculation. Multiband orientation bit codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB algorithm was initially created using thermal images, while the EBGM method was originated with visible images. When two or more spectral images from the same subject are available, the identification accuracy and reliability can be enhanced using score fusion. We compare the identification performance of applying five recognition algorithms to the three-band (visible, near infrared, thermal) face images, and explore the fusion performance of combing the multiple scores from three recognition algorithms and from three-band face images, respectively. The experimental results show that the FPB is the best recognition algorithm, the HMM yields the best fusion result, and the thermal dataset results in the best fusion performance compared to other two datasets.

A video-based surveillance system for passengers includes face detection, face tracking and face recognition. In general, the final recognition result of the video-based surveillance system is usually determined by the cumulative recognition results. Under this strategy, the correctness of face tracking plays an important role for the system recognition rate. For face tracking, the challenges of face tracking on a moving platform are that the space and time information used for conventional face tracking algorithms may be lost. Consequently, conventional face tracking algorithms can barely handle the face tracking on a moving platform. In this paper, we have verified the state-of-the-art technologies for face detection, face tracking and face recognition on a moving platform. In the mean time, we also proposed a new strategy for face tracking on a moving platform or face tracking under very low frame rate. The steps of the new strategy for face detection are: (1) classification the detected faces over a certain period instead of every frame (2) Tracking of each passenger is equivalent to reconstruct the time order of certain period for each passenger. If the cumulative recognition results are the only part needed for the surveillance system, step 2 can be skipped. In addition, if the additional information from the passengers is required, such as path tracking, lip read, gesture recognition, etc, time order reconstruction in step 2 can offer the information required.

Image tracking algorithms are critical to many applications including image super-resolution and surveillance. However, there exists no method to independently verify the accuracy of the tracking algorithm without a supplied control or visual inspection. This paper proposes an image tracking framework that uses deep restricted Boltzmann machines trained without external databases to quantify the accuracy of image tracking algorithms without the use of ground truths. In this paper, the tracking algorithm is comprised of the combination of flux tensor segmentation with four image registration methods, including correlation, Horn-Schunck optical flow, Lucas-Kanade optical flow, and feature correspondence methods. The robustness of the deep restricted Boltzmann machine is assessed by comparing between results from training with trusted and not-trusted data. Evaluations show that the deep restricted Boltzmann machine is a valid mechanism to assess the accuracy of a tracking algorithm without the use of ground truths.

Various algorithms for text-independent speaker recognition have been developed through the decades, aiming to improve both accuracy and efficiency. This paper presents a novel PCA/LDA-based approach that is faster than traditional statistical model-based methods and achieves competitive results. First, the performance based on only PCA and only LDA is measured; then a mixed model, taking advantages of both methods, is introduced. A subset of the TIMIT corpus composed of 200 male speakers, is used for enrollment, validation and testing. The best results achieve 100%, 96% and 95% classification rate at population level 50, 100 and 200, using 39- dimensional MFCC features with delta and double delta. These results are based on 12-second text-independent speech for training and 4-second data for test. These are comparable to the conventional MFCC-GMM methods, but require significantly less time to train and operate.

Audio source separation aims to extract the signals of individual sound sources from a given recording. In this paper, we review three recent advances which improve the robustness of source separation in real-world challenging scenarios and enable its use for multisource content retrieval tasks, such as automatic speech recognition (ASR) or acoustic event detection (AED) in noisy environments. We present a Flexible Audio Source Separation Toolkit (FASST) and discuss its advantages compared to earlier approaches such as independent component analysis (ICA) and sparse component analysis (SCA). We explain how cues as diverse as harmonicity, spectral envelope, temporal fine structure or spatial location can be jointly exploited by this toolkit. We subsequently present the uncertainty decoding (UD) framework for the integration of audio source separation and audio content retrieval. We show how the uncertainty about the separated source signals can be accurately estimated and propagated to the features. Finally, we explain how this uncertainty can be efficiently exploited by a classifier, both at the training and the decoding stage. We illustrate the resulting performance improvements in terms of speech separation quality and speaker recognition accuracy.

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Can a compressive sampling expert system help to build a summary of a video in a composited picture? Digital Internet age has provided all with an information degree of freedom; but, comes with it, the societal trash being accumulated beyond analysts to sort through, to summary video automatically as a digital library category. While we wish preserve the spirit of democratic Smartphone-Internet to all, we provide an automation and unbiased tool called the compressive sampling expert system (CSpES) to summarize the video content at user's own discretion.

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral sup-port. In this paper, we consider the challenging problem of sub-Nyquist sampling of bandlimited or sparse signals. Can the new paradigm Compressive Sampling (CS) challenge Shannon-Nyquist? Our preliminary development goal is to study and evaluate to reduce the system process load and still provide sufficient and useful information. We also review radio frequency process and Shannon Nyquist Sampling Theorem.

This paper describes a new approach and its associated theoretical performance guarantees for supervised hyperspectral image classification from compressive measurements obtained by a Coded Aperture Snapshot Spectral Imaging System (CASSI). In one snapshot, the two-dimensional focal plane array (FPA) in the CASSI system captures the coded and spectrally dispersed source field of a three-dimensional data cube. Multiple snapshots are used to construct a set of compressive spectral measurements. The proposed approach is based on the concept that each pixel in the hyper-spectral image lies in a low-dimensional subspace obtained from the training samples, and thus it can be represented as a sparse linear combination of vectors in the given subspace. The sparse vector representing the test pixel is then recovered from the set of compressive spectral measurements and it is used to determine the class label of the test pixel. The theoretical performance bounds of the classifier exploit the distance preservation condition satisfied by the multiple shot CASSI system and depend on the number of measurements collected, code aperture pattern, and similarity between spectral signatures in the dictionary. Simulation experiments illustrate the performance of the proposed classification approach.

We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

A system of ambulatory, halter, electrocardiography (ECG) monitoring system has already been commercially available for recording and transmitting heartbeats data by the Internet. However, it enjoys the confidence with a reservation and thus a limited market penetration, our system was targeting at aging global villagers having an increasingly biomedical wellness (BMW) homecare needs, not hospital related BMI (biomedical illness). It was designed within SWaP-C (Size, Weight, and Power, Cost) using 3 innovative modules: (i) Smart Electrode (lowpower mixed signal embedded with modern compressive sensing and nanotechnology to improve the electrodes' contact impedance); (ii) Learnable Database (in terms of adaptive wavelets transform QRST feature extraction, Sequential Query Relational database allowing home care monitoring retrievable Aided Target Recognition); (iii) Smartphone (touch screen interface, powerful computation capability, caretaker reporting with GPI, ID, and patient panic button for programmable emergence procedure). It can provide a supplementary home screening system for the post or the pre-diagnosis care at home with a build-in database searchable with the time, the place, and the degree of urgency happened, using in-situ screening.

Many image scene analysis applications require a computational approach to visual attention. The foreground in these applications is typically sparse in spatial support. Compressive sampling enables an approach to reconstruct the sparse map of image regions that stand out from the background using fewer measurements. A convex optimization algorithm, for instance, can be used to recover the sparse map in the wavelet domain. Besides being sparse in the transform domain, the background of natural images has an interesting property that the amplitude of the averaged Fourier spectrum is approximately proportional to the inverse of the frequency. This further enables us to approximate an average background signal for extracting the out-of-ordinary foreground signal corresponding to objects of interest.

We present a novel fractal Iterated Function System (IFS based) data interpolation algorithm enabling compressive sampling of 1/f data sets. We avoid the classical inverse IFS parameter estimation problem by using a novel analytical function driven variant of the random Iterated Function System (IFS) algorithm. We attempt to optimize the parameters of the analytical driver equation to optimize the data reconstruction by minimizing errors using various state-of-the-art genetic algorithms. We demonstrate our encouraging results and detail our methods and findings.

Since its discovery more than 30 years ago, surface-enhanced Raman scattering (SERS) has been recognized as a highly sensitive detection technique for chemical and biological sensing and medical diagnostics. However, the practical application of this remarkably sensitive technique has not been widely accepted as a viable diagnostic method due to the difficulty in preparing robust and reproducible substrates that provide maximum SERS enhancement. Here, we demonstrate that the aligned silver nanorod (AgNR) array substrates engineered by the oblique angle deposition method are capable of providing extremely high SERS enhancement factors (>10⁸). The substrates are large area, uniform, reproducible, and compatible with general microfabrication process. The enhancement factor depends strongly on the length and shape of the Ag nanorods and the underlying substrate coating. By optimizing AgNR SERS substrates, we show that SERS is able to detect trace amount of toxins, virus, bacteria, or other chemical and biological molecules, and distinguish different viruses/bacteria and virus/bacteria strains. The substrate can be tailored into a multi-well chip for high throughput screening, integrated into fiber tip for portable sensing, incorporated into fluid/microfluidic devices for in situ real-time monitoring, fabricated onto a flexible substrate for tracking and identification, or used as on-chip separation device for ultra-thin layer chromatography and diagnostics. By combining the unique SERS substrates with a handheld Raman system, it can become a practical and portable sensor system for field applications. All these developments have demonstrated that AgNR SERS substrates could play an important role in the future for practical clinical, industrial, defense, and security sensing applications.

In the past 46 years, Intel Moore observed an exponential doubling in the number of transistors in every 18 months through the size reduction of individual transistor components since 1965. In this paper, we are exploring the nanotechnology impact upon the Law. Since we cannot break down the atomic size barrier, the fact implies a fundamental size limit at the atomic or Nanotechnology scale. This means, no more simple 18 month doubling as in Moore's Law, but other forms of transistor doubling may happen at a different slope in new directions. We are particularly interested in the Nano enhancement area. (i) 3-D: If the progress in shrinking the in-plane dimensions (2D) is to slow down, vertical integration (3D) can help increasing the areal device transistor density and keep us on the modified Moore's Law curve including the 3rd dimension. As the devices continue to shrink further into the 20 to 30 nm range, the consideration of thermal properties and transport in such nanoscale devices becomes increasingly important. (ii) Carbon Computing: Instead of traditional Transistors, the other types of transistors material are rapidly developed in Laboratories Worldwide, e.g. IBM Spintronics bandgap material and Samsung Nano-storage material, HD display Nanotechnology, which are modifying the classical Moore's Law. We shall consider the overall limitation of phonon engineering, fundamental information unit 'Qubyte' in quantum computing, Nano/Micro Electrical Mechanical System (NEMS), Carbon NanoTubes (CNTs), single layer Graphemes, single strip Nano-Ribbons, etc., and their variable degree of fabrication maturities for the computing and information processing applications.

The electrical conductivity properties of carbon nanotubes (CNTs) can be applied in the nanoelectronic devices to use in the nanoengineering applications. This paper investigates the low and high frequency-dependent electrical conductivity response of the single and multi walled carbon nanotubes (SWNTs and MWNTs) thin-films by analyzing the contactless THz time-domain spectroscopy (THz-TDS) technique and the extrapolation method.

This paper compares FPGA-based full pipelined multiplierless FIR filter design options. Comparison of Distributed Arithmetic (DA), Common Sub-Expression (CSE) sharing and n-dimensional Reduced Adder Graph (RAG-n) multiplierless filter design methods in term of size, speed, and A*T product are provided. Since DA designs are table-based and CSE/RAG-n designs are adder-based, FPGA synthesis design data are used for a realistic comparison. Superior results of a genetic algorithm based optimization of pipeline registers and non-output fundamental coefficients are shown. FIR filters (posted as open source by Kastner et al.) for filters in the length from 6 to 151 coefficients are used.

In this paper, we demonstrate that a wide class of machine learning problems can be formulated as general problems of multi-valued decision and classification. To reduce the sample complexity associated with the statistical learning and inference schemes, we propose the principle of probabilistic comparison, the inclusion principle and exact computational methods for constructing multistage procedures for the relevant multi-hypothesis testing problems.

Image fusion has been extensively studied in past two decades. By image fusion algorithms, a composite image (i.e., fused image) can be formed with several images from different sensors. The performance of image fusion methods can be assessed using subjective and/or objective measures. However, subjective evaluation involves human subjects, which significantly increases the cost of time and resource. In this paper, we will discuss objective evaluations of color image fusion algorithms. Given a reference color image and fused color images, we first convert the images into CIELab color space. Then we define four image metrics in CIELab space: the phase congruency metric (PCM), the image gradient magnitude metric (IGMM), the image contrast metric (ICM), and the color natural metric (CNM). Finally, with the four metrics, we propose an objective evaluation index (OEI) for a fused image to measure its similarity with the reference image. The larger the OEI value of a fused image is, the more similar the fused image is with the reference image. To validate the proposed metric, first the fused images are formed with different color fusion algorithms using a set of multispectral images (including visible color images, near infrared images, and long wave infrared images); and then the OEIs of fused images are calculated and compared. Experimental results show that the proposed objective evaluation index is very promising and fits well to subjective evaluation.

We wish to assist caretakers with a sensor monitoring systems for tracking the physiological changes of homealone patients. One goal is seeking biomarkers and modern imaging sensors like stochastic optical reconstruction microscopy (STORM), which has achieved visible imaging at the nano-scale range. Imaging techniques like STORM can be combined with a fluorescent functional marker in a system to capture the early transformation signs from wellness to illness. By exploiting both microscopic knowledge of genetic pre-disposition and the macroscopic influence of epigenetic factors we hope to target these changes remotely. We adopt dual spectral infrared imaging for blind source separation (BSS) to detect angiogenesis changes and use laser speckle imaging for hypertension blood flow monitoring. Our design hypothesis for the monitoring system is guided by the user-friendly, veteran-preferred "4-Non" principles (noninvasive, non-contact, non-tethered, non-stop-to-measure) and by the NIH's "4Ps" initiatives (predictive, personalized, preemptive, and participatory). We augment the potential storage system with the recent know-how of video Compressive Sampling (CSp) from surveillance cameras. In CSp only major changes are saved, which reduces the manpower cost of caretakers and medical analysts. This CSp algorithm is based on smart associative memory (AM) matrix storage: change features and detailed scenes are written by the outer-product and read by the inner product without the usual Harsh index for image searching. From this approach, we attempt to design an effective household monitoring approach to save healthcare costs and maintain the quality of life of seniors.

Spectral overlaps causing spectral interference are a key concern in elemental analysis by optical emission spectrometry. Spectral interferences are addressed using a variety of methods, including artificial neural networks (ANNs). In my lab, these methods are being developed using both experimentally-obtained results and spectral simulations. ANNs are being developed for inductively coupled plasma-atomic emission spectrometry (ICP-AES) and for optical emission measurements using microplasmas and portable emission spectrometers. In this paper, the application of ANNS for spectral interference correction is described in some detail.

DCE-MRI represents an important tool for detecting subtle kinetic changes in breast lesion tissue. Non-masslike breast lesions exhibit an atypical dynamical behavior compared to mass-like lesions and pose a challenge to a computer-aided diagnosis system. Yet the correct diagnosis of these tumors represents an important step towards early prevention. We apply Independent Component Analysis (ICA) on DCE-MRI images to extract kinetic tumor curves. We use a known empirical mathematical model to automatically identify the tumor curves from the ICA result. Filtering out noise, our technique is superior to traditional ROI-based analysis in capturing the kinetic characteristics of the tumor curves. These typical characteristics enable us to nd out the optimal number of independent components for ICA. Another benet of our method is the segmentation of tumor tissue which is superior to the segmentation from MR subtraction images. Our aim is a optimal extraction of tumor curves to provide a better basis for kinetic analysis and to distinguish between benign and malignant lesions, especially for the challenging non-mass-like breast lesions.

The evaluation of kinetic and/or morphologic characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have not well-dened and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these diculties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphologic descriptors separately, and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions. We additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

Spatio-temporal feature extraction represents a challenge however critical step for the differential diagnosis of non-mass-enhancing lesions. The atypical dynamical behavior of these lesions paired with non well-defined tumor borders requires novel approaches to obtain representative features for a subsequent automated diagnosis. We evaluate the performance of mappings of pixelwise kinetic features within a tumor, morphological descriptors based on Minkowski functionals and a novel technique, the Zernike velocity moments, to capture the joint spatio- temporal behavior within an image sequence. The highest sensitivity is achieved by the Zernike velocity moments proving thus that dynamical and morphological behavior can not be separately analyzed based on features extracted only for a distinct behavior or as a feature combination of these two but has to be a simultaneous measure of these. The present paper provides the most detailed automated diagnosis of non-mass-enhancing lesions so far in the literature.

We propose to enhance the decision making of pilot, co-pilot teams, over a range of vehicle platforms, with the aid of neuroscience. The goal is to optimize this collaborative decision making interplay in time-critical, stressful situations. We will research and measure human facial expressions, personality typing, and brainwave measurements to help answer questions related to optimum decision-making in group situations. Further, we propose to examine the nature of intuition in this decision making process. The brainwave measurements will be facilitated by a University of California, San Diego (UCSD) developed wireless Electroencephalography (EEG) sensing cap. We propose to measure brainwaves covering the whole head area with an electrode density of N=256, and yet keep within the limiting wireless bandwidth capability of m=32 readouts. This is possible because solving Independent Component Analysis (ICA) and finding the hidden brainwave sources allow us to concentrate selective measurements with an organized sparse source →s sensing matrix [Φ_s], rather than the traditional purely random compressive sensing (CS) matrix[Φ].

In bioinformatics, there are often a large number of input features. For example, there are millions of single nucleotide polymorphisms (SNPs) that are genetic variations which determine the dierence between any two unrelated individuals. In microarrays, thousands of genes can be proled in each test. It is important to nd out which input features (e.g., SNPs or genes) are useful in classication of a certain group of people or diagnosis of a given disease. In this paper, we investigate some powerful feature selection techniques and apply them to problems in bioinformatics. We are able to identify a very small number of input features sucient for tasks at hand and we demonstrate this with some real-world data.

The mission of ONR's Human and Bioengineered Systems Division is to direct, plan, foster, and encourage Science and Technology in cognitive science, computational neuroscience, bioscience and bio-mimetic technology, social/organizational science, training, human factors, and decision making as related to future Naval needs. This paper highlights current programs that contribute to future biomedical wellness needs in context of humanitarian assistance and disaster relief. ONR supports fundamental research and related technology demonstrations in several related areas, including biometrics and human activity recognition; cognitive sciences; computational neurosciences and bio-robotics; human factors, organizational design and decision research; social, cultural and behavioral modeling; and training, education and human performance. In context of a possible future with automated casualty evacuation, elements of current science and technology programs are illustrated.

Post Traumatic Stress Disorder (PTSD) sometimes develops following exposure to very stressful or traumatic events such as motor vehicle accidents, rape, and war. It is arguably the signature injury of the conflicts in Iraq and Afghanistan. Previous studies have demonstrated that PTSD sufferers exhibit autonomic hyper-responsiveness to both neutral and trauma-related stimuli. In this study, we propose using high resolution thermal imaging of sweat-pores to obtain a noncontact, remote, and quantifiable measure of the sympathetic autonomic nervous reactivity to guide diagnosis, assess response to treatment, and tease out important cues to suicidality as a PTSD comorbidity.

The goal of this paper and research effort is to develop a simple and clear apparatus and approach to quantify the effectiveness of sensor systems as it relates to their ability to penetrate camouflage and resolve skin depth. Over the last decade, several attempts have been made to leverage advances in Infrared (IR) imaging, made by the military, into medical sensing [1]. Several promising technologies have been evaluated and thus far determined to be lacking when compared to the current standards of care based on x-ray imaging [2]. While progress has been made this general class of technology has not generated wide spread interest from the medical community. This lack of interest is discouraging, especially when considering the great potential for good that would result in successfully demonstrating a truly passive tumor detection system based on thermal signatures. Recently, this team participated as part of a larger group in the development and testing of a novel class of algorithms using images from two separate IR spectra. This area of spectral fusing algorithms is called the Single Pixel-Blind Source Separation (SP-BSS). While the goal of experiment is not new, our results showed this approach provided potential improvements over more traditional thermography, particularly in the area of overcoming environmental noise. These promising results have motivated us to develop a method for running controlled experiments so that the equipment and algorithms can be optimized and the significant engineering challenges of frame registration, data standardization, and sensor optimization for wellness screening can be accomplished. Conducting these efforts using data from human subjects is both impractical and unwarranted at this time. We have developed a physics-physiological under-dermal model of internal vascular circulation that approximates not only a healthy human body (angiogenesis effect) but also a human body developing a tumor (neo-angiogenesis effect). This physical model also provides useful insight into the detection of objects concealed on a person. We call this dual use target an Under-Dermal Emulator.

Respiration rate is a key guide for evaluating the physiological state of an individual during triage. Recent work has shown that high resolution thermal cameras can passively and remotely obtain respiration signals under controlled environmental conditions. This paper introduces an automatic end-to-end respiration signal measurement (through signal detection) approach based on statistical computation of the image intensities around the human nostril area in a thermal video. A method is presented to detect and track the nostril area and to calculate statistical values of the pixel intensity around the nostril area and correlate the statistical values with respiration signals from a contact sensor such as transducer belt. Results are based upon data collected from 200 subjects across two different experiments. This work provides not only a new image processing tool for tracking facial ROIs in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for measuring respiration for security and medical applications.

This paper describes a trans-skull ultrasonic Doppler system for measuring the blood flow direction in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the fuzzy degree of blood flow by Doppler Effect, thereby it locates blood vessel. This Doppler Effect is examined by the center of gravity shift of the frequency magnitudes. In in-vitro experiment, a cow bone was employed as the skull, and three silicon tubes were done as blood vessels, and bubble in water as blood. We received the ultrasonic waves through a protein, the skull and silicon tubes in order. In the system, fuzzy degrees are determined with respect to the Doppler shift, amplitude of the waves and attenuation of the tissues. The fuzzy degrees of bone and blood direction are calculated by them. The experimental results showed that the system successfully visualized the skull and flow direction, compared with the location and flow direction of the phantom. Thus, it detected the flow direction by Doppler Effect under skull, and automatically extracted the region of skull and blood vessel.

This paper proposes a heart rate monitoring system for detecting autonomic nervous system by the heart rate variability using an air pressure sensor to diagnose mental disease. Moreover, we propose a human behavior monitoring system for detecting the human trajectory in home by an infrared camera. In day and night times, the human behavior monitoring system detects the human movement in home. The heart rate monitoring system detects the heart rate in bed in night time. The air pressure sensor consists of a rubber tube, cushion cover and pressure sensor, and it detects the heart rate by setting it to bed. It unconstraintly detects the RR-intervals; thereby the autonomic nervous system can be assessed. The autonomic nervous system analysis can examine the mental disease. While, the human behavior monitoring system obtains distance distribution image by an infrared camera. It classifies adult, child and the other object from distance distribution obtained by the camera, and records their trajectories. This behavior, i.e., trajectory in home, strongly corresponds to cognitive disorders. Thus, the total system can detect mental disease and cognitive disorders by uncontacted sensors to human body.

Health is quite important to be realized in our daily life. However, its idea covers wide area and has individual dependency. Activities in health care have been widely developed by medical, drag, insurance, food, and other types of industries mainly centering diseases. In this article, systems approach named Systems Health Care is introduced and discussed to generate new and precious values based on measurements in daily life to change lifestyle habits for realizing each health. Firstly, issues related to health such as its definitions are introduced and discussed by centering health rather than disease. In response to the discussions on health, Home and Medical Care is continuously introduced to point out the important role causality between life style and vital signal such as exercise and blood pressure based on detailed sampling time. Systems approaches of Systems Health Care are discussed from various points of views. Real applications of devices and services are used to make the studies and discussions deeper on the subjects of the article.

We describe the epipolar constraint that specifies the geometry of stereo vision. We consider the 3D structure reconstruction from multiple views through the new perspective of basing the reconstruction from directly estimated planar homographies instead of using techniques that are based on matched point pairs. Planar homography parameters can more accurately extract scene planar surfaces and directly solve for the 3D structure and camera motion parameters. The new method has the advantage that it integrates larger amount of information because the homography parameters are estimated directly from the intensities and not from an abstracted descriptor of the neighborhood. Because it does not rely on a transformation of an entire image region, the method is efficient.