Fast Normalized Cross Correlation Based on Adaptive Multilevel Winner Update

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Template matching is used for many applications in image processing. Cross Correlation is the basic statistical approach to image registration. It is used for template matching or pattern recognition. Template can be considered a sub-image from the reference image, and the image can be considered as a sensed image. The objective is to establish the correspondence between the reference image and sensed image. It gives the measure of the degree of similarity between an image and template. This paper describes medical image registration by template matching based on Normalized Cross-Correlation (NCC) using Cauchy-Schwartz inequality. The algorithm for template matching using NCC is implemented in MATLAB. The algorithm does the template matching and uses the Cauchy-Schwartz’s inequality to simplify the procedure. The developed algorithm is robust for similarity measure. An experimental result with medical images registration with noise and without noise is shown in the results section.

Normalized cross-correlation has been used extensively for many signal processing applications, but the traditional normalized correlation operation does not meet speed requirements for time-critical applications. In this paper, a new fast algorithm for the computation of the normalized cross-correlation (NCC) without using multiplications is presented. For a search window of size M and a template of size N the fast NCC requires only approximately 2N?(M?N+1) additions/subtractions without multiplications. Simulation results with 100,000 test signals show that the use of the fast NCC instead of the traditional approaches for the determination of the degree of similarity between a test signal and a reference signal (template) brings about a significant improvement in terms of false negative rate, identification rate and computational cost without a significant increase in false positive rate, especially when the signal-to-noise ratio (SNR) is higher than 3 dB.

Auto Die Bonder (ADB) is often encountered in the IC package field, whose key technical difficulty lies in quickly and accurately locating the bonding positions of the chips. In this paper, a normalized cross correlation (NCC) algorithm based on image pixels has been used to address this problem. By using iterated algorithm with simplified expression, the modified NCC (MNCC) is remarkably accelerated compared to the traditional one. Afterwards, the MCNN algorithm has been examined by experiments to locate the lead-frame pads, and the result shows its superiority in efficiency with satisfactory positioning accuracy.

As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored. We calculated the normalized cross-correlation (NCC) between single-unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster. Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.

This paper deals with problems of surface object recognition in urban environmental conditions in infrared sequences using the template matching technique. Target recognition is always necessary in the selection and tracking the defined objects. The objects feature is changed in dependence on time and space condition such as weather condition, urban environment, etc. In this article, we proposed a method for targets recognition using template matching technique in infrared (IR) video sequences. A user simply chooses the given object at some point during detection. On the basis of feature of selected object, the algorithm employed the template matching techniques to find the object. The designed algorithm was tested in program MATLAB and MATLAB - SIMULINK. The paper will deal with a new algorithm using template matching. The idea of the paper will approach to reduce processing time and increase precision for object detection and selection in using template matching technique. There are many similarities criteria for template matching that are sum of absolute differences (SAD), locally scaled sum of absolute difference (LSAD), zero - mean sum of absolute differences (ZSAD), sum of squared differences (SSD), etc. In this paper we have employed the similarity criteria normalized cross correlation. The cross correlation is not invariant to changes in image intensity such as lighting conditions, and the range of correlation coefficient is dependent on the size of the feature, while we can normalize for the effect of changing intensity and template size by using normalized cross correlation. The basic principle of the algorithm is based on the assumption the object is selected at time t with the center of mass T and we also know the greatest relative velocity between the camera and the target ν ct . Thus, the greatest distance which the target can move after the interval Δt will be limited in the circle with the center T at time t and radius R. That means the size of compared image will be smaller than the size of original frame in infrared video sequence. Therefore, the computational time will be faster. In order to reduce the unnecessary computational time of the template matching technique in whole frame, we need to keep the requirement of the selected maximum size of the target is smaller than a half of the size of the frame. It means that the maximum size of the image ƒ(x, y) is equal to the size of the frame in video sequence.

Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.

A stereovision algorithm is proposed for visual odometry to estimate motion of mobile robot by providing feature pair sequence. It is composed of feature extracting, matching and tracking. Firstly, corners are extracted as features by Harris operator and grid-based optimizing. In feature matching and tracking, serious problems are caused by variable illumination between stereo images. An improved Moravec's Normalized Cross Correlation (MNCC) algorithm is presented to reduce illumination affect in computing correspondence of corners. On current stereo image pair, extracted corners are matched by correlation-based bidirectional algorithm and outliers are rejected by epipolar constraint. Matched corners are tracked in pre-estimated search windows. The computational cost is greatly reduced by limiting number of corners, pre-estimating search window and feature local-updating. Simulation results validate that our algorithm is efficient and reliable.

Extraction of micro-vibration information from a single-shot speckle image is realized base on the row-by-row exposure mechanism of a rolling shutter camera and the high intensity of laser speckle. Normalized cross correlation and cubic spline interpolation algorithm is firstly used between nonoverlapping gray scales of adjacent rows to infer micro-displacement information. With number of rows set as 342, wide frequency range from 300 Hz to 20 kHz sinusoid signals are recovered with relative errors smaller than 2%. For low frequency signals from 40 Hz to 300 Hz, the relative errors of the extracted frequency are smaller than 5%. The effects of number of sampling cycles (NSC) and sampling points in unit period on the system performance are investigated and high accuracy is obtained with NSC of 4 and sampling points of 3 per signal period. In a single shot image, spatial multi points vibration detection scheme is presented and experimentally realized. The representative signals of 5 kHz, 10 kHz and 20 kHz are successfully extracted in the multidimensional scenes of 1D, 2D and 3D respectively, and the relative errors are all within 0.5%. This scheme can be potentially used in air surveillance for wide range remote sensing of interested object or sounding.

Prior research studies have shown that the peak signal to noise ratio (PSNR) is the most frequent watermarked image quality metric that is used for determining the levels of strength and weakness of watermarking algorithms. Conversely, normalised cross correlation (NCC) is the most common metric used after attacks were applied to a watermarked image to verify the strength of the algorithm used. Many researchers have used these approaches to evaluate their algorithms. These strategies have been used for a long time, however, which unfortunately limits the value of PSNR and NCC in reflecting the strength and weakness of the watermarking algorithms. This paper considers this issue to determine the threshold values of these two parameters in reflecting the amount of strength and weakness of the watermarking algorithms. We used our novel watermarking technique for embedding four watermarks in intermediate significant bits (ISB) of six image files one-by-one through replacing the image pixels with new pixels and, at the same time, keeping the new pixels very close to the original pixels. This approach gains an improved robustness based on the PSNR and NCC values that were gathered. A neural network model was built that uses the image quality metrics (PSNR and NCC) values obtained from the watermarking of six grey-scale images that use ISB as the desired output and that are trained for each watermarked image’s PSNR and NCC. The neural network predicts the watermarked image’s PSNR together with NCC after the attacks when a portion of the output of the same or different types of image quality metrics (PSNR and NCC) are obtained. The results indicate that the NCC metric fluctuates before the PSNR values deteriorate.

The biomechanical properties of soft tissues vary with pathological phenomenon. Ultrasound elasticity imaging is a noninvasive method used to analyze the local biomechanical properties of soft tissues in clinical diagnosis. However, the echo signal-to-noise ratio (eSNR) is diminished because of the attenuation of ultrasonic energy by soft tissues. Therefore, to improve the quality of elastography, the eSNR and depth of ultrasound penetration must be increased using chirp-coded excitation. Moreover, the low axial resolution of ultrasound images generated by a chirp-coded pulse must be increased using an appropriate compression filter. The main aim of this study is to develop an ultrasound elasticity imaging system with chirp-coded excitation using a Tukey window for assessing the biomechanical properties of soft tissues. In this study, we propose an ultrasound elasticity imaging system equipped with a 7.5-MHz single-element transducer and polymethylpentene compression plate to measure strains in soft tissues. Soft tissue strains were analyzed using cross correlation (CC) and absolution difference (AD) algorithms. The optimal parameters of CC and AD algorithms used for the ultrasound elasticity imaging system with chirp-coded excitation were determined by measuring the elastographic signal-to-noise ratio (SNRe) of a homogeneous phantom. Moreover, chirp-coded excitation and short pulse excitation were used to measure the elasticity properties of the phantom. The elastographic qualities of the tissue-mimicking phantom were assessed in terms of Young’s modulus and elastographic contrast-to-noise ratio (CNRe). The results show that the developed ultrasound elasticity imaging system with chirp-coded excitation modulated by a Tukey window can acquire accurate, high-quality elastography images.

The performance of the “weighted Fourier phase slope” centroiding algorithm at the subpupil image of a Shack–Hartmann wavefront sensor for point-like astronomical guiding sources is explored. This algorithm estimates the image’s displacement in the Fourier domain by directly computing the phase slope at several spatial frequencies, without the intermediate step of computing the phase; it then applies optimized weights to the phase slopes at each spatial frequency obtained by a Bayesian estimation method. The idea was inspired by cepstrum deconvolution techniques, and this relationship is illustrated. The algorithm’s tilt estimation performance is characterized and contrasted with other known centroiding algorithms, such as thresholded centre of gravity (TCoG) and cross correlation (CC), first through numerical simulations at the subpupil level, then at the pupil level, and finally at the laboratory test bench. Results show a similar sensitivity to that of the CC algorithm, which is superior to that of the TCoG algorithm when large fields of view are necessary, i.e., in an open-loop configured adaptive optics system, thereby increasing the guide star limiting magnitude by 0.6 to 0.7 mag. On the other side, its advantage over the CC algorithm is its lower computational cost by approximately an order of magnitude.

Pulse wave velocity (PWV) has been shown to be a good predictor of cardiovascular risk. Measuring PWV normally constitutes quantifying the transit time (TT) between proximally and distally measured blood pressure or velocity waveforms. However various factors affect our ability to measure PWV accurately making it a topic of continued attention. Primarily, the selection of the algorithm used to measure TT has been shown to have a remarkable effect upon accuracy. Other factors pertain to the quality and nature of the waveform data. The quality of the data (assuming good sensor function) worsens with increasing signal noise and decreasing sampling frequency, and the nature of the waveform data refers to waveform shape (e.g. pressure vs. velocity) or shape mismatch between the distal and proximal waveforms. Papaioannou et al.’s letter reflects upon an article our group published in the Annals of Biomedical Engineering (2013 Dec; 41(12):2617-29) which assessed current algorithms’ accuracies and variabilities due to waveform signal noise, temporal resolution, shape and shape mismatch. We used waveform data generated by a one-dimensional (1-D) arterial model for which the theoretical TT was known a priori, thus algorithm accuracy could be explicitly measured. Furthermore, the same algorithms were applied to beat-to-beat clinical data to investigate beat-to-beat variability in PWV measurement. Based on this study we proposed a set of recommendations for choosing the most accurate algorithm for a given waveform set. The algorithms tested included two variations of the traditional foot-to-foot method, a least squares fit of the systolic rise, and cross correlation of the entire waveform. The software developed for this study containing all algorithms tested was made available to the public in the interest of continued discussion upon this topic. We agree with the Papainnou et al.’s assertion that pressure and velocity waveforms may need to be addressed differently. Certainly the results we presented in our article (Fig. 4) explicitly show differences between algorithm accuracies when applied to pairs of pressure or velocity waveforms. Furthermore, these differences were outlined as a warning in Point 4 of the algorithm selection recommendations. We found wave shape mismatch between the proximal and distal wave pairs to be a crucial factor in determining which algorithm to use. For example, cross correlation is by definition a comparison of wave shapes weighted by local magnitude. In our article we measured waveshape similarity using a normalised cross-correlation coefficient (CCCoefficient) where a value of 1.0 would indicate identical shapes. Papainnou et al. were interested to know the CCCoefficient for the carotid and femoral arterial waveforms as these are the standard measurements made by clinical devices such as the SphygmoCor system. The CCCoefficient of the carotid-and-femoral-artery pressure waves was 0.78 for the numerical data and 0.84 ± 0.10 for our patient cohort (n = 20). These results indicate substantial wave shape mismatch in clinical assessment. Measuring beat-to-beat variation in carotid femoral PWV in our patient cohort was done retrospectively using 10 s tonometric signals measured sequentially at the carotid, and then the femoral artery. As each signal was measured concurrently with the ECG, individual waveforms could be dissected from the 10 s signal and aligned in time with reference to the R-wave of the ECG. Beat-to-beat variation was therefore assessed as the variation in measured PWV from as many waveAddress correspondence to Nicholas R. Gaddum, King’s College London, British Heart Foundation Centre, St. Thomas’ Hospital, 4th Floor Lambeth Wing, Westminster Bridge Road, London SE1 7EH, UK. Electronic mail: nickgaddum@gmail.com Annals of Biomedical Engineering, Vol. 42, No. 6, June 2014 ( 2014) pp. 1145–1147 DOI: 10.1007/s10439-014-1007-7

A new measure of similarity, suitable for both signal processing and image processing applications, is presented. The measure of similarity ( Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> ) is an F-distributed test statistic that quantifies the degree of alignment, correlation or coincidence between two or more (time-or space-dependent) waveforms. The test statistic may be used to automate detection, classification, localisation, association and registration operations. When used to estimate time-of-arrival differences, which is typically done using cross correlation or beam formation, the technique provides an efficient means of: maintaining a low and constant false-alarm rate, detecting weak signals in noise and reducing angular errors. The method is applied to the problem of acoustic source detection and localisation. Real data are used to compare the method with cross correlation and normalised cross correlation. The new method yields prominent and narrow peaks, at the true source locations, with false peaks due to misalignment, misassociation, and background noise, suppressed. When processing continuous data streams, the test statistic is computed efficiently in the time domain.

An experiment was conducted on a full-scale jet engine to investigate the aerodynamic noise generating regions in the free jet. Cross-correlation measurements were made between the static pressure fluctuations and the farfield radiated sound. These measurements were made for two different static pressure probe positions and a large number of farfield positions (at various angles). In addition, each test geometry was run for four different jet exit velocities. The measured, normalized cross-correlation functions varied between 0.004 and 0.155. A new Q-function, based on the above normalized cross correlation is defined and plotted. This function represents the source strength per unit volume within the jet region. This Q-function shows dependence on the probe position, the angular position of the farfield microphone, and the jet exit Mach number. Third-octave analyses of both the probe signal and the farfield radiated sound were made. The results show that cross-correlation techniques are a valuable tool in the investigation of the aerodynamic noise generating regions of an actual jet engine.