Normalized Cross Correlation

Current biomedical research increasingly requires imaging large and thick 3D structures at high resolution. Prominent examples are the tracking of fine filaments over long distances in brain slices, or the localization of gene expression or cell migration in whole animals like Caenorhabditis elegans or zebrafish. To obtain both high resolution and a large field of view (FOV), a combination of multiple recordings ('tiles') is one of the options. Although hardware solutions exist for fast and reproducible acquisition of multiple 3D tiles, generic software solutions are missing to assemble ('stitch') these tiles quickly and accurately.

This paper is concerned with the tracking of partially or entirely occluded objects in a video sequence. We propose certain modifications to the template matching approach, which seem to fit the type of tracking data being considered in the present note. Specifically, we will use a nonstandard particle filtering method via the following two steps: The first step employs the normalized cross-correlation function as the likelihood. The second step is to resample, and to fuse the results of multiple cross-correlations of different patches of the given object, in order to refine the likelihood for the particle filter. Experimental results show that the method is reliable for noisy measurements, and provides accurate results in cases of occlusion or heavy shadows.

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject's iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject's unique iris pattern with high accuracy, security, and reliability.

Digital volume correlation is a new experimental technique that allows the measurement of the full-field strain tensor in three dimensions. We describe the addition of rotational degrees of freedom into the minimization problem for digital volume correlation in order to improve the overall performance of the strain measurement. A parameterization of rotations that is particularly suited to the minimization problem is presented, based on the angle-axis representation of finite rotations. The partial derivative of both a normalized cross-correlation coefficient and the sum-of-squares correlation coefficient are derived for use with gradient-based minimization algorithms. The addition of rotation is shown to greatly reduce the measurement error when even small amounts of rigid body rotation are present in an artificially rotated test volume. In an aluminum foam sample loaded in compression, including rotational degrees of freedom produced smoother contours of minimum principal strain. Renderings of the aluminum foam architecture in areas of low, medium and high rotation showed material deformation pattern in detail.

A doubletalk detector (DTD) is used with an echo canceler to sense when far-end speech is corrupted by near-end speech. Its role is to freeze the adaptation of the model filter when near-end speech is present in order to avoid divergence of the adaptive algorithm. Several authors have proposed to use the cross-correlation coefficient vector between the input signal vector x and the scalar output for a DTD. We show in this paper that this measure is not appropriate and propose a modified form that meets, in an optimal way, the needs for an efficient DTD. By extension, we also propose a definition of the normalized cross-correlation matrix between two vectors and show a link with the coherence function.

This paper proposes convenient methods to increase the dynamic speed range in particle-image velocimetry (PIV) measurements, which employ a charge-coupled device (CCD) camera binning option. Although the binning procedure decreases spatial resolution along specified dimensions, its ability to increase the camera frame rate and preserve the field-of-view are important advantages, which need to be thoroughly investigated. In order to demonstrate the advantages of the CCD binning option, we have carried out experiments both on static images of in-plane particle displacements and on dynamic images of real-fluid flows. In the first experiment, we have analyzed static images of 0.5, 1, and 1.9 µm size fluorescent polystyrene microparticles placed on thick quartz glass plate and illuminated by high-power LED. The in-plane images were captured at various CCD binning options, the distance between particles was then calculated using crosscorrelation analysis and a subpixel interpolation scheme based on a Gaussian filter. In the second experiment, the fluid flow in a 30 × 300 × 50 000 µm microchannel was recorded at various CCD binning modes, and then, evaluated using ensemble-averaged normalized cross-correlation analysis with Gaussian subpixel interpolation. Velocity profiles obtained at various CCD binning modes were compared with those obtained at the normal mode. Error analysis has shown that despite the loss of the spatial resolution, the advantages of pixel binning, specifically increased sensitivity and a total full-frame rate, can be useful in PIV measurements, especially in laminar-fluid flows.

A binary switch based on the detection of periodic vocal cord vibrations is proposed for individuals with multiple and severe disabilities. The system offers three major advantages over existing speech-based access technologies, namely, insensitivity to environment noise, increased robustness against user-generated artifacts such as coughs, and reduced exertion during prolonged usage periods. The proposed system makes use of a dual-axis accelerometer placed noninvasively in proximity of the vocal cords by means of a neckband. Periodic vocal cord vibrations are detected using the normalized cross-correlation function computed from anterior-posterior and superior-inferior accelerometry signals. Experiments with a participant with hypotonic cerebral palsy show the proposed system outperforming a popular commercial sound-based system in terms of sensitivity, task time, and user-perceived exertion.

Image correspondence and registration techniques have gained popularity in recent times due to advancement of utilization in digital media and its storage. The main problem associated with image processing is when it is applied to fields like robotic vision and machine vision. The problem is due to clutter, i.e. the same frame with different objects has to be matched. Hence there has been need for efficient techniques of Image Registration. This led to development of feature extraction techniques and template matching techniques. The normalized cross correlation technique is one of them. A classical solution for matching two image patches is to use the cross-correlation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty more weight can be put on the confident matches than those that are more uncertain. All the simulations have been performed using MATLAB tool.

A universally applicable reliability-guided digital image correlation (DIC) method is proposed for reliable image deformation measurement. The zero-mean normalized cross correlation (ZNCC) coefficient is used to identify the reliability of the point computed. The correlation calculation begins with a seed point and is then guided by the ZNCC coefficient. That means the neighbors of the point with the highest ZNCC coefficient in a queue for computed points will be processed first. Thus the calculation path is always along the most reliable direction, and possible error propagation of the conventional DIC method can be avoided. The proposed novel DIC method is universally applicable to the images with shadows, discontinuous areas, and deformation discontinuity. Two image pairs were used to evaluate the performance of the proposed technique, and the successful results clearly demonstrate its robustness and effectiveness.

This paper presents inspection of Printed Circuit Boards (PCBs) based on normalized crosscorrelation. Correlation gives the similarity measure of images. In this paper, Normalized Cross-Correlation has been used to differentiate between a defective and defect free printed circuit board. Different PCBs have been inspected using normalized cross-correlation in this paper and further defected PCBs have been used for detection of all possible defects. Image subtraction(one of referential methods) is used for detection of defects of PCBs. And after detection of defects using image subtraction method, the wrong hole defects, missing conductors defects, etching defects, break lines have been classified.

Standard X-ray images using conventional screen-film technique have a limited field of view and failed to visualize the entire long bone on a single image. To produce images with whole body parts, digitized images from the films that contain portions of the body parts are assembled using image stitching. This article presents a new medical image stitching method that uses minimum average correlation energy filters to identify and merge pairs of X-ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases that contain a total of 40 pairs of overlapping and nonoverlapping images. Then the experimental results are compared to those of the normalized cross correlation (NCC) method. It is found that the proposed method outperforms the NCC method in identifying both the overlapping and nonoverlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about five times shorter than that of the NCC method. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 166-171, 2012 Copyright © 2012 Wiley Periodicals, Inc.

http://onlinelibrary.wiley.com/doi/10.1002/ima.22018/full

Photo-response non-uniformity (PRNU) of digital sensors was recently proposed [1] as a unique identification fingerprint for digital cameras. The PRNU extracted from a specific image can be used to link it to the digital camera that took the image. Because digital camcorders use the same imaging sensors, in this paper, we extend this technique for identification of digital camcorders from video clips. We also investigate the problem of determining whether two video clips came from the same camcorder and the problem of whether two differently transcoded versions of one movie came from the same camcorder. The identification technique is a joint estimation and detection procedure consisting of two steps: (1) estimation of PRNUs from video clips using the Maximum Likelihood Estimator and (2) detecting the presence of PRNU using normalized cross-correlation. We anticipate this technology to be an essential tool for fighting piracy of motion pictures. Experimental results demonstrate the reliability and generality of our approach.

In this paper we present a new method of signal processing for robust speech recognition using two microphones. The method, loosely based on the human binaural hearing system, consists of passing the speech signals detected by two microphones through bandpass filtering. We develop a spatial masking function based on normalized cross-correlation, which provides rejection of off-axis interfering signals. To obtain improvements in reverberant environments, a temporal masking component, which is closely related to our previously-described de-reverberation technique known as SSF. We demonstrate that this approach provides substantially better recognition accuracy than conventional binaural sound-source separation algorithms.

Global features describe the image content by a small number of numerical values, which are usually combined into a vector of less than 1,024 components. Since color is not present in most medical images, grey-scale and texture features are analyzed in order to distinguish medical imagery from various modalities. The reference data is collected arbitrarily from radiological routine. Therefore, all anatomical regions and biological systems are present and all images have been captured in various directions. The ground truth is established by manually reference coding with respect to a mono-hierarchical unambiguous coding scheme. Based on 6,335 images, experiments are performed for 54 and 57 categories or 70 and 81 categories focusing on radiographs only or considering all images, respectively. A maximum classification accuracy of 86% was obtained using the winner-takes-all rule and a one nearest neighbor classifier. If the correct category is only required to be within the 5 or 10 best matches, we yield a best rate of 98% using normalized cross correlation of small image icons.

This article investigates the possibility of extracting gastric motility (GM) information from finger photoplethysmographic (PPG) signals non-invasively. Now-a-days measuring GM is a challenging task because of invasive and complicated clinical procedures involved. It is well-known that the PPG signal acquired from finger consists of information related to heart rate and respiratory rate. This thread is taken further and effort has been put here to find whether it is possible to extract GM information from finger PPG in an easier way and without discomfort to the patients. Finger PPG and GM (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the rate of 100 Hz from eight healthy subjects for 30 min duration in fasting and postprandial states. In this study, we process the finger PPG signal and extract a slow wave that is analogous to actual EGG signal. To this end, we chose two advanced signal processing approaches: first, we perform discrete wavelet transform (DWT) to separate the different components, since PPG and EGG signals are non-stationary in nature. Second, in the frequency domain, we perform crossspectral and coherence analysis using autoregressive (AR) spectral estimation method in order to compare the spectral details of recorded PPG and EGG signals. In DWT, a lower frequency oscillation (%0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0-0.1953) Hz. Comparison of these two slow wave signals was done by normalized cross-correlation technique. Crosscorrelation values are found to be high (range 0.68-0.82, SD 0.12, R = 1.0 indicates exact agreement, p < 0.05) for all subjects and there is no significant difference in crosscorrelation between fasting and postprandial states. The coherence analysis results demonstrate that a moderate coherence (range 0.5-0.7, SD 0.13, p < 0.05) exists between EGG and PPG signal in the ''slow wave'' frequency band, without any significant change in the level of coherence in postprandial state. These results indicate that finger PPG signal contains GM-related information. The findings are sufficiently encouraging to motivate further exploration of finger PPG as a non-invasive source of GM-related information.

We use a satellite-based survey of glacier surface elevation changes, speeds and surface melt conditions between 2000 and 2011 to quantify mass loss from the Northern Patagonian Icefield (NPI), Chile. A history of ice elevation change is found by differencing ASTER Digital Elevation Models (DEMs) relative to a void-filled version of the DEM collected by the Shuttle Radar Topography Mission (SRTM) in February 2000. Thinning rates have accelerated at lower elevations, while above the Equilibrium Line Altitude (ELA) recent thinning rates are not significantly different from those observed in previous studies. A volumetric change of − 4.06 ± 0.11 km 3 /yr is found by summing surface elevation changes over all glaciers in the NPI. This is regarded as a lower bound because volume loss due to frontal retreat and sub-aqueous melting is not included. This volume change is converted to a mass loss of 3.40 ± 0.07 Gt/yr, taking into account density differences above and below the equilibrium line. We find that the NPI is providing at least 0.009 ± 0.0002 mm/yr to ongoing sea level change, in agreement with previous estimates. Sub-pixel offset tracking of ASTER image pairs is used to produce glacier displacements over intervals typically ranging from 6 days to 18 months. The first whole icefield composite velocity map is presented. The terminus of the San Rafael Glacier, on the western side of the NPI sustained a peak calving front speed of 19.7 ± 1.2 m/day for 7 days in the austral fall of 2007. Passive microwave observations indicate that the surface of the accumulation basin of the glacier was wet during this period of rapid motion, suggesting that the fast speed may be a consequence of a rise in sub-glacial water pressure, sourced from melt or rainwater making its way to the glacier bed and reducing basal friction. Several other outlet glaciers, including the Benito and HPN1 glaciers, on the isolated western side of the icefield accelerated while thinning between 2007 and 2011. Marked acceleration also occurred after 2007 at the front of the San Quintin Glacier, the largest outlet glacier of the NPI. The paucity of thickness measurements at the outlet glaciers means it is impossible to accurately gauge the mass flux of the icefield and thus give a full estimate of the mass balance.

We present an automatic traffic monitoring approach using data of an airborne wide angle camera system. This camera, namely the "3K-Camera", was recently developed at the German Aerospace Center (DLR). It has a coverage of 8 km perpendicular to the flight direction at a flight height of 3000 m with a resolution of 45 cm and is capable to take images at a frame rate of up to 3 fps. Based on georeferenced images obtained from this camera system, a near real-time processing chain containing road extraction, vehicle detection, and vehicle tracking was developed and tested. The road extraction algorithms handle a-priori information provided by a road database for a first guess of the location of the roads. Two different techniques can be used for road extraction. In the first method, roadside features are found by using an edge detector based on ISEF filtering, selecting the steepest edge, which is normally the edge between the tarry roads and the vegetation. The second method extracts roads by searching the roadside markings using a dynamical threshold operator and a line detector. Vehicle detection then is limited to the road areas found by the road extraction algorithms. It is based on an edge detector, a k-means clustering of the edges, and on geometrical constraints, e.g. vehicle size. Vehicle tracking is performed by matching detected vehicles in pairs of consecutive images. For this matching the normalized cross correlation is calculated for each detected car within a limited search area. The algorithms for road extraction, vehicle detection and vehicle tracking proved to be quite sophisticated, enabling car detection and tracking rates with a completeness of 70 % and a correctness of up to 90 % on images obtained from a flight height of 1000 m.

In this paper, we present two different double-talk detection schemes for Acoustic Echo Cancellation (AEC). First, we present a novel normalized detection statistic based on the cross-correlation coefficient between the microphone signal and the cancellation error. The decision statistic is designed in such a way that it meets the needs of an optimal double-talk detector. We also show that the proposed detection statistic converges to the recently proposed normalized crosscorrelation based double-talk detector [1], the best known crosscorrelation based detector. Next, we present a new hybrid doubletalk detection scheme based on a cross-correlation coefficient and two signal detectors. The hybrid algorithm not only detects doubletalk but also detects and tracks any echo-path variations efficiently. We compare our results with other cross-correlation based doubletalk detectors to show their effectiveness.

In the current scenario excessive use of digital media attracted the attention of hackers, attackers and malicious users. In digital media certain types of vulnerabilities are always available, attackers use these vulnerabilities to attack on confidentiality, security, authenticity of digital media. In this paper we have introduced a novel watermarking method based on the properties of Discrete Cosine Transform (DCT) and Permuted Box (P-Box). Using P-Box security of watermark is enhanced up to a great level making it more robust against different image processing attacks. We have used YCbCr color space for watermarking because of its popularity and use in digital photography and video. In the proposed method DCT transform is applied on Cr component of YCbCr and a binary secret image is embedded to middle frequency DCT coefficients of cover image. This proposed method is compared with various previous methods and result shows the effectiveness of the method in terms of robustness, capacity and watermarking attacks.

Detection of meeting events is one of the most important tasks in multimodal analysis of planning meetings. Speaker detection is a key step for extraction of most meaningful meeting events. In this paper, we present an approach of speaker localization using combination of visual and audio information in multimodal meeting analysis. When talking, people make a speech accompanying mouth movements and hand gestures. By computing correlation of audio signals, mouth movements, and hand motion, we detect a talking person both spatially and temporally. Three kinds of features are extracted for speaker localization. Hand movements are expressed by hand motion efforts; audio features are expressed by computing 12 mel-frequency cepstral coefficients from audio signals, and mouth movements are expressed by normalized cross-correlation coefficients of mouth area between two successive frames. A time delay neural network is trained to learn the correlation relationships, which is then applied to perform speaker localization. Experiments and applications in planning meeting environments are provided.

This paper presents an inexpensive framework for 3-D seabed mosaic reconstruction, based on an asynchronous stereo vision system when simplifying motion assumptions are used. In order to achieve a metric reconstruction some knowledge about the scene is recovered by a simple and reliable calibration step. The major issue in calibration come from the asynchronism that complicate the proper frames selection. To overcome this problem a stereo frames selection based on Epipolar Gap Evaluation (EGE) is proposed. Stereo disparity maps are evaluated by using both local and global approaches. To deal with brightness constancy model violation, zero-mean normalized cross-correlation is used as similarity measure in local approach, whereas a histogram equalization is necessary in global approach in order to improve min-cut based algorithms. Experimental results validate the proposed framework, allowing to define 3-D mosaics having visual quality similar to those obtained by using specialized hardware.

A key component for hands-free, full-duplex, communication technology is the echo canceler. An echo canceler consists primarily of an adaptive ÿlter and a control device called the double-talk detector. We derive a test statistic based on a normalized cross-correlation vector for the multichannel frequency-domain adaptive algorithm. The advantages of this approach are: low computational complexity, simplicity of implementation, and better performance than the classical Geigel algorithm.

Silkworm sex identification is one of the important processes in the sericulture industry because it can assist in effectively separating strong and healthy silkworm pupae from the weak ones. In this paper, we study and show that a desired moderate accuracy and fast response time in silkworm gender identification can be realized by deploying a widely-used and simple normalized cross correlation (NCC)-based pattern matching operation in our optical penetration-based silkworm pupa gender identification structure. Other key features are ease of implementation, adaptive learning ability, and low component counts. Experimental proof of concept is performed by using three 636-nm wavelength light emitting diodes, one 1600 Â 1200-pixel web camera, an 8-bit microcontroller, a notebook computer, and our Lab-View program. There are 25 female and 20 male silkworm pupae under the study. Experimental results show that male silkworm pupae can be completely identified under the NCC-based pattern matching performed in both the Cartesian and polar coordinate systems. For the female silkworm pupa, the best measured accuracies of 80% and 56% are obtained by performing the NCC-based pattern matching operations in the Cartesian and polar coordinate systems, respectively. The NCC-based pattern matching operation in the Cartesian coordinate system also offers a measured 13-ms response time, which is twice faster than in the polar coordinate system. The moderate measured total accuracy of 88% and fast silkworm gender identification shows high potential for the deployment in the sericulture industry.

The hydroxyl nightglow layer is an excellent tracer of the dynamical processes occurring within the mesosphere. A new stereo-imaging method is applied that not only measures the altitude of the airglow layer but also provides a three-dimensional map of the OH-layer centroid heights. A campaign was conducted in July 2006 in Peru to obtain NIR images of the OH nightglow layer which were simultaneously taken for two sites separated by 645 km: Cerro Cosmos (12109 0 08.2 00 S, 75133 0 49.3 00 W, altitude 4630 m) and Cerro Verde Tellolo (16133 0 17.6 00 S, 71139 0 59.4 00 W, altitude 2330 m). Data represented by pairs of images obtained during the nights of July 26-27 and 28-29 are analyzed to yield satellite-type views of the wave field. These are obtained by application of an inversion algorithm. In calculating the normalized cross-correlation parameter for the intensity, three-dimensional maps of the OH nightglow layer surface are retrieved. The mean altitude of the emission profile barycenter is found to be at 87.1 km on July 26 and 89.5 km on July 28. In these two cases the horizontal wavelengths determined are 21.1 and 24.6 km with periods of 18 and 34 min, respectively. A panoramic view of the OH nightglow emission obtained on July 29 at 8 h51-9 h26 UT is presented, in which the overall direction of the waves is found to be N-NW to S-SE, azimuth 1501-3301 (counted from South). The wave kinetic energy density at the OH nightglow layer altitude is 3.9 Â 10 À4 W/kg, which is comparable to the values derived from partial reflection radiowave data.

Subpixel image registration is the key to successful multi-angle remote sensing image applications such as image fusion, superresolution and classification. However, multi-angle remote sensing images pose some difficulties for automatic image registration, namely, 1) precisely locating control points (CPs) is difficult as large view angle images are susceptible to resolution change and blurring; and 2) local geometric distortion caused by variations in platform stability makes rigid transform models such as the projective model unreliable. In this paper we propose a two-stage automatic registration scheme for multi-angle remote sensing imagery. In the first step, CPs are gathered via the scale invariant feature transform (SIFT). However, CPs collected by SIFT may be too few or unevenly distributed. Therefore, another CPs collecting procedure based on normalized cross-correlation follows. In order to eliminate outliers in the CPs a geometric constraint is utilized; after outlier elim...

Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the market every day. Some of these have very severe Size, Weight and Power constraints whereas other devices have to handle very high computational loads. Some require both these conditions to be met simultaneously. Current imaging architectures and digital image processing solutions will not be able to meet these ever increasing demands. There is a need to develop novel imaging architectures and image processing solutions to address these requirements. In this work we propose analog signal processing as a solution to this problem. The analog processor is not suggested as a replacement to a digital processor but it will be used as an augmentation device which works in parallel with the digital processor, making the system faster and more efficient. In order to show the merits of analog processing the highly computational Normalized Cross Correlation algorithm is implemented. We propose two novel modifications to the algorithm and a new imaging architecture which, significantly reduces the computation time.

In this paper, a method for robust image registration based on M-estimator Correlation Coefficient (MCC) is presented. A real valued correlation mask function is computed using Huber and Tukey's robust statistics and is used as a similarity measure for registering image windows. The mask function suppresses the influence of outlier points and makes the registration algorithm robust to noisy pixels, brightness fluctuations and presence of occluding objects. The superiority of the proposed algorithm, in terms of registration performance and computation time is demonstrated through experimental studies on different types of real world images.

This paper presents a mutual-information based optimization algorithm for improving piecewise-linear (PWL) image registration. PWL-registration techniques, which are well-suited for registering images of the same scene with relative local distortions, divide the images in conjugate triangular patches that are individually mapped through affine transformations. For this process to be accurate, each pair of corresponding image triangles must be the projections of a planar surface in space; otherwise, the registration incurs in errors that appear in the resultant registered image as local distortions (distorted shapes, broken lines, etc.). Given an initial triangular mesh onto the images, we propose an optimization algorithm that, by swapping edges, modifies the mesh topology looking for an improvement in the registration. For detecting the edges to be swapped we employ a cost function based on the mutual information (MI), a metric for registration consistency more robust to image radi...

Usually, the stereo correspondence for a feature point in the first image is obtained by searching in a predefined region of the second image, based on the epipolar line and the maximum disparity. The reduction of the search space can increase the performance of the matching process, in the co text of the execution time and the accuracy. For the edge-based stereo correspondence, we obtain the noticeable reduction in the search space. Considering the maximum of the disparity gradient in the real scene, we formulated the relation between the maximum search space in the second images with respect to the relative displacement of the continuous edges (as the feature points) in the successive scan lines of the first images. Then we developed some very fast stereo matching algorithms, based on the normalized cross correlation criteria (NCC) for different sizes of the matching block.

A novel LED modeling algorithm for precise three-dimensional light pattern simulation is proposed and demonstrated. We propose to use normalized cross correlation to verify the validity of the simulation in onedimensional intensity patterns as well as two-dimensional irradiance patterns in various midfield distances and to provide feedback to achieve a successful model. The model is demonstrated to obtain an average of 99% in normalized cross correlation between the simulation light pattern and experimental measurement for a truncated inverse pyramid LED.

The invariance of the similarity measure in photometric distortions as well as its capability in producing subpixel accuracy are two desired and often required features in most stereo vision applications. In this paper we propose a new correlation-based measure which incorporates both mentioned requirements. Specifically, by using an appropriate interpolation scheme in the candidate windows of the matching image, and using the classical zero mean normalized cross correlation function, we introduce a suitable measure. Although the proposed measure is a nonlinear function of the sub-pixel displacement parameter, its maximization results in a closed form solution, resulting in reduced complexity for its use in matching techniques. Application of the proposed measure in a number of benchmark stereo pair images reveals its superiority over existing correlation-based techniques used for subpixel accuracy.

The influence of missing array elements on aberration correction based on time delay estimation using radio frequency signals of neighboring elements is experimentally investigated. Normalized cross correlation and sum of absolute differences are employed as the cost functions in aberration estimation. Their performance is tested through various measurements using radio frequency data acquired with a 3.3 MHz, 64-element phased array. Variation of cost functions and phase estimation error are obtained for different combinations of number of missing elements, amount of aberration, and noise level. For a particular combination of these parameters, a set of B-scan images is reconstructed and presented to examine the effects of residual phase errors on image quality.

Visual Target Tracking (VTT) has been implemented in the new Mars Exploration Rover (MER) Flight Software (FSW) R9.2 release, which is now running on both Spirit and Opportunity rovers. Applying the normalized cross-correlation (NCC) algorithm with template image magnification and roll compensation on MER Navcam images, VTT tracks the target and enables the rover to approach the target within a few cm over a 10 m traverse. Each VTT update takes 1/2 to 1 minute on the rovers, 2-3 times faster than one Visual Odometry (Visodom) update. VTT is a key element to achieve a target approach and instrument placement over a 10-m run in a single sol in contrast to the original baseline of 3 sols. VTT has been integrated into the MER FSW so that it can operate with any combination of blind driving, Autonomous Navigation (Autonav) with hazard avoidance, and Visodom. VTT can either guide the rover towards the target or simply image the target as the rover drives by. Three recent VTT operational c...

Accurate registration of multi-temporal remote sensing images is essential for various change detection applications. Mutual information (MI) has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histogram to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multi-temporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual information based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures namely mean squared difference (MSD) and normalized cross correlation (NCC) used for the registration of multi-temporal remote sensing images.

Usually, the stereo correspondence for a feature point in the first image is obtained by searching in a predefined region of the second image, based on the epipolar line and the maximum disparity. The reduction of the search space can increase the performance of the ...

Automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (CT) chest scans is one of most important problems of today's medical image analysis. We propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. The separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3D and 2D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. Experiments with 200 spiral low dose CT (LDCT) scans confirm the accuracy of our approach.

This work implements a novel hybrid method for detection and tracking of biological cells of "in vitro" samples (Goobic, 1 2005). The method is able to detect and track cells based on image processing, nonlinear filters and normalized cross correlation (ncc) and it is tested on a full sequence of 1080 images of cell cultures. In addition of the cell speed, Cell tracking differentiate itself from tracking other kinds of tracking because cells show: mitosis, apthosis, overlapping and migration (Liao, 2 1995). Image processing provides an excellent tool to improve cell recognition and background elimination, set as a priori task on this work and conveniently implemented by a Fourier analysis. The normal cross correlation was developed in the Fourier space to reduce time processing. The problem of the target detection was formulated as a nonlinear joint detection/estimation problem on the position parameters. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori likelihood of the existence of the target in a given space-time resolution cell. The shapes of the targets are random and according to the sequence, the targets change of shape almost every frame. However, the cross correlation result is based on the target shape matching, not in the position; and the system is invariant to rotation. Nonlinear filter makes a robust cell tracking method by producing a sharper correlation peak and reducing the false positives in the correlation. These false positives may also be reduced by using image preprocessing. Fourier and nonlinear filtering implementation showed the best results for the proposed cell tracking method presenting the best time consumption and the best cell localization.

Implantable cardioverter defibrillators (ICDs) detect and defibrillate ventricular fibrillation (VF) and ventricular tachycardia (VT). Other therapies which use less energy are also available to terminate VT. Previous studies have shown that ICD rate schemes often misdiagnose VT as VE In this study, an improved VF classification scheme was designed and tested, which employs the classic rate criteria plus paired signal concordance (PSC); PSC uniquely detects VF where VT and VF rates overlap (220-340 ms). TWo signals from a bipolar pair (1 cm) recorded in a unipolar sense exhibit similar signal shape for concordant rhythms, such as sinus rhythm and VT, and disconcordance for VE Once the rate criterion is met, PSC is measured by the peak normalized crosscorrelation coefficient calculated over the depolarization. Variability, measured by a modified range, determined the contextual diagnosis over a passage. Sinus rhythm (20), VT (12), VF (22), atrial fibrillation (10), sinus rhythm with ventricular premature depolarizations , and polymorphic VT (4) passages were recorded from 38 patients. Rate-PSC was tested with unfiltered, digitized signals (1-500 Hz, 1,000 samples per second) and with filtered, downsampled signals (1-50 Hz, 100 samples per second). Sensitivity values, or percentage of correct VF detection, and specificity values, or detection of all other rhythms, were generated and compared with simulations of three commercial ICDs programmed to similar settings as rate-PSC and to nominal settings. The sensitivity values for rate-PSC with unfiltered and with filtered signals and for ICDs with 220 ms and with nominal settings were I00%, 100%, 48-80%, and 100%, respectively; the corresponding specificity values were 95%, 83%, 93%, and 7-13%, respectively. It was concluded that the rate-PSC scheme was able to reliably separate VF from other rhythms, even rhythms that have a variable morphology or variable rate. With the confidence of accurate VF detection, use of low-energy therapies for non-VF rhythms will increase device longevity and enhance patient comfort.

In this paper, we propose a front-end framework for 3D human face reconstruction and recognition at a distance. A stereo acquisition system is built and deployed to capture stereo pairs of subjects at different distances. Three main issues are addressed to achieve accurate face reconstruction, which leads to good recognition; Different illumination conditions between the stereo pair due to larger baseline and further distances, where a fast similarity measure based on normalized cross correlation is shown to tackle such problem. Due to the non-convexity nature of a human face, concave regions introduce occluded regions where cubic Splines are used to estimate the disparity. Disparity discontinuities are introduced due to the sparse nature of stereo reconstruction, where surface fitting is performed at prominent facial points. We present our database of 99 subjects at different ranges where reconstruction and recognition results are presented.

This paper presents an efficient algorithm to achieve accurate sub-pixel matchings for calculating correspondences between stereo images based on a path-based matching algorithm. Compared to point-by-point stereo matching algorithms, pathbased algorithms resolve local ambiguities by maximizing the cross correlation (or other measurements) along a path, which can be implemented efficiently using dynamic programming. An effect of global matching criterion is that cross correlations at all pixels can contribute to the criterion; since cross correlation can change significantly even with sub-pixel changes, to achieve sub-pixel accuracy, it is no longer sufficient to first find the path that maximizes the criterion at integer pixel locations and then refine to sub-pixel accuracy. In this paper, by writing bilinear interpolation using integral images, we show that cross correlations at all sub-pixel locations can be computed efficiently and thus lead to a sub-pixel accuracy path based matching algorithm. Our results show the feasibility of the method and illustrate the significant improvement over existing path-based matching methods.

In this paper, a multiscale wavelet-based algorithm for matching stand-alone shapes is developed. The algorithm uses the Dyadic Wavelet Transform (DWT) to decompose a shape's boundary into multi-scale levels. Features are extracted by calculating the curve moment invariants of the approximation coefficients. If the measured dissimilarity is small, then the shapes are globally similar. Local similarity is investigated by calculating the normalized cross correlation of the 1-D triangle area representation of the detail coefficients. The presented algorithm not only finds similar shapes, but it also can easily distinguish between seemingly similar shapes. The algorithm is invariant to the affine transformation and to the starting point variation of the shape contour.

The present work investigates the potential of neural adaptive learning to solve the correspondence problem within a two-frame adaptive area matching approach. A novel method is proposed based on the use of the Zero Mean Normalized Cross Correlation Coefficient integrated within a neural network model which use least-mean-square delta rule for training. Two experiments were conducted for evaluating the neural model proposed. The first aimed to produce dense disparity maps based on the analysis of standard test images. The second experiment, conducted in biomedical field, aimed to model 3D surfaces from a varied set of SEM (Scanning Electron Microscope) stereoscopic image pairs.

We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. CT is an established tool for the diagnosis of coronary artery diseases (CADs). Dual-energy digital radiography could be a cost-effective alternative for screening coronary artery calcification. In order to utilize CT as the &quot;gold standard&quot; to evaluate the ability of DR images for the detection and localization of calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, i.e. Gaussian-weighted projection, threshold-based projection, and average-based projection. We tested normalized cross correlation (NCC) and normalized mutual information (NMI) as similarity measurement. We used the Downhill Simplex me...

In this paper, we present a method for handling double-talk. This approach uses a robust fast recursive least-squares algorithm (FRLS) and the normalized cross-correlation double-talk detector (NCC DTD). The NCC DTD is developed into a fast version, called FNCC, by reusing computational results from the FRLS algorithm. The major advantage of this detector is that it is much less dependent on the actual echo path attenuation than, e.g., the Geigel DTD. This makes it much more suitable for the acoustic application.