Normalized Cross Correlation

In the modern time interacting with digital world become standard life activity, human need a way to protect properties as individuals or corporals, and we do that by embedding a digital mark to the target, and this technique call digital watermarking. But there still is a chance to manipulate or even remove this marks we embed for protection with various attacks like adding noises, compression-decompression or bits manipulations, and that why companions, individuals, laboratories are still developing new methods to embed this marks and make them more robust and more hard to detect for others. There are so many methods for digital watermarking, so we chose the least significant bits watermarking (LSB-watermarking) to provide an invisible digital watermarking, and on top of that we proceed with the blind LSB-watermarking metho d so that we don't get bind to the original image, and for our attack we chose compression joint photographic experts group (JPEG) compression because it’s the most used method for image and videos compression along with singular value decomposition (SVD) to make our mark as robust as possible. And the results we gain from our method are promising and it did give as high quality digital watermarking.

This paper focuses on the utilization of digital video/images as cover to hide data . The proposed steganography algorithm based on color histograms for data embedding into Video clips directly, where each pixel in each video frame is divided in two parts, the number of bits which will be embedded in the right part are counted in the left part of the pixel. This algorithm is characterized by the ability of hiding larger size of data and the ability of extracting the written text without errors, besides it gives a high level of authentication to guarantee integrity of the video/ images before being extracted. Furthermore, the data were embedded inside the video/ images randomly which gave the video/ images a higher security and resistance against extraction by attackers.

Efficient data sorting is important for searching and optimization algorithms in high time demanding fields such as image and multi-media data processing. To accelerate the data sorting algorithm applied in practical normalized crosscorrelation image matching, a novel high-speed parallel sorting scheme based on field programmable gate array (FPGA) is proposed in this paper. When the FPGA chip used has low available logic resource for sorting, the scheme is further extended with random access memory (RAM) as indicators for sorted data to sort large data set. Function and timing simulation with Quartus II 8.0 and practical experiment of the parallel sorting scheme based on the FPGA chip applied in normalized cross-correlation image matching sub-system have shown that this scheme can effectively improve the speed performance with more logic resources usage.

An automated visual printed circuit board (PCB) inspection is an approach used to counter difficulties occurred in human’s manual inspection that can eliminate subjective aspects and then provides fast, quantitative, and dimensional assessments. A printed circuit board (PCB) is a basic component of many electronic devices. The quality of PCBs will have a significant effect on the performance of many electronic products. Presently, there has been a lot of work concentrating on the detection and classification of defects on PCB. There are so many approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey the various algorithms and techniques are examined. A summary of commercial PCB inspection system is also presented.

In gesture and sign language video sequences, hand motion tends to be rapid, and hands frequently appear in front of each other or in front of the face. Thus, hand location is often ambiguous, and naive color-based hand tracking is insufficient. To improve tracking accuracy, some methods employ a prediction-update framework, but such methods require careful initialization of model parameters, and tend to drift and lose track in extended sequences. In this paper, a temporal filtering framework for hand tracking is proposed that can initialize and reset itself without human intervention. In each frame, simple features like color and motion residue are exploited to identify multiple candidate hand locations. The temporal filter then uses the Viterbi algorithm to select among the candidates from frame to frame. The resulting tracking system can automatically identify video trajectories of unambiguous hand motion, and detect frames where tracking becomes ambiguous because of occlusions or overlaps. Experiments on video sequences of several hundred frames in duration demonstrate the system's ability to track hands robustly, to detect and handle tracking ambiguities, and to extract the trajectories of unambiguous hand motion.

We present an effective intensity-based method for rigid registration of a patient preoperative CT to its intraoperative situation with a few fluoroscopic X-ray images obtained with a tracked C-arm. It improves upon existing methods and overcomes most of their intrinsic speed, robustness, and accuracy problems. For speed, we generate Digitally Reconstructed Radiographs on small, dynamically selected regions of interest from precomputed ray gray levels in expected viewing directions, and use a multiresolution hierarchy of fluoroscopic X-ray images. For robustness and accuracy, we use a two-step comparison measure: Normalized Cross Correlation followed by Variance Weighted Sum of Local Normalized Correlation. To avoid local minima, we use a genetic search method. Our experiments on simulated, in-vitro, and cadaver data show an overall mean Target Registration Error of 0.8mm (max=1.5mm), 95% of the time, computed in 20-100 seconds under realistic conditions.

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

Cardiac motion has been tracked using various methods, which vary in their invasiveness and dimensionality. One such noninvasive modality for cardiac motion tracking is ultrasound. Threedimensional ultrasound motion tracking has been demonstrated using detected data at low volume rates. However, the effects of volume rate, kernel size, and data type (raw and detected) have not been sufficiently explored. First comparisons are made within the stated variables for 3-D speckle tracking. Volumetric data were obtained in a raw, baseband format using a matrix array attached to a high parallel receive beam count scanner. The scanner was used to acquire phantom and human in vivo cardiac volumetric data at 1000-Hz volume rates. Motion was tracked using phase-sensitive normalized cross-correlation. Subsample estimation in the lateral and elevational dimensions used the grid-slopes algorithm. The effects of frame rate, kernel size, and data type on 3-D tracking are shown. In general, the results show improvement of motion estimates at volume rates up to 200 Hz, above which they become stable. However, peak and pixel hopping continue to decrease at volume rates higher than 200 Hz. The tracking method and data show, qualitatively, good temporal and spatial stability (for independent kernels) at high volume rates.

Cardiac motion has been tracked using various methods, which vary in their invasiveness and dimensionality. One such noninvasive modality for cardiac motion tracking is ultrasound. Threedimensional ultrasound motion tracking has been demonstrated using detected data at low volume rates. However, the effects of volume rate, kernel size, and data type (raw and detected) have not been sufficiently explored. First comparisons are made within the stated variables for 3-D speckle tracking. Volumetric data were obtained in a raw, baseband format using a matrix array attached to a high parallel receive beam count scanner. The scanner was used to acquire phantom and human in vivo cardiac volumetric data at 1000-Hz volume rates. Motion was tracked using phase-sensitive normalized cross-correlation. Subsample estimation in the lateral and elevational dimensions used the grid-slopes algorithm. The effects of frame rate, kernel size, and data type on 3-D tracking are shown. In general, the results show improvement of motion estimates at volume rates up to 200 Hz, above which they become stable. However, peak and pixel hopping continue to decrease at volume rates higher than 200 Hz. The tracking method and data show, qualitatively, good temporal and spatial stability (for independent kernels) at high volume rates.

Stereophotogrammetry is finding increased use in clinical breast surgery, both for breast reconstruction after oncological procedures and cosmetic augmentation and reduction. The ability to visualize and quantify morphological features of the breast facilitates pre-operative planning and post-operative outcome assessment. The contour outlining the lower half of the breast is important for the quantitative assessment of breast aesthetics. Based on this inferior breast contour, relevant morphological measures, such as breast symmetry, volume, and ptosis, can be determined. In this paper, we present an approach for automatically detecting the inferior contour of the breast in 3D images. Our approach employs surface curvature analysis and is able to detect the breast contour with high accuracy, achieving an average error of 1.64 mm and a dice coefficient in the range of 0.72-0.87 when compared with the manually annotated contour (ground truth). In addition, the detected contour is used to facilitate the detection of the lowest visible point on the breast, which is an important landmark for breast morphometric analysis.

Data security is the essential in the today’s world of internet and networking. In
any organization information is critical. In today’s world people are ready to spent
thousands and lacks of money in order to ensure high level of information security. In
spite of spending such a huge amount, still the objective of securing the information is
not achieved as the data some how gets in the hands of hacker. As the technology for
securing the data is advancing, hackers are also keeping pace with this technology.
Hackers now make use of certain algorithm or other techniques to decode the data
encoded by the senders. One of the ways to ensure security is to ensure that data is not
visible to the hacker. This can be done by hiding the message itself behind some other
objects. Here we are achieving this data security concept through the technique of
Steganography.

India, among the agriculture-based economy grows wide variety of rice along with other crops. These varieties have different commercial values as they are different in their features. It becomes extremely challenging to classify rice varieties which have similar features but are different in their quality. This study considers four varieties of similar looking rice which conform to be Sona-Masuri. A total of 4180 images are considered to extract 56 features including textural, red, green, blue (RGB) and hue, saturation, value (HSV) color and wavelet decomposition. Machine learning (ML) models like support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT) and voting classifiers are developed for feature dataset and convolutional neural network (CNN) model for image dataset. The results obtained for every model are obtained using statistical methods and the results are expressed in a table for accuracy, precision, recall and F1-score. A classification accuracy of 72.48% is obtained for SVM using polynomial kernel trick by considering all 56 features. The customized CNN model is designed with three convolution layers has resulted in 97.13% of training accuracy and 87.5% of validation accuracy. Based on the results obtained, it is witnessed that the ML models employed in this study to classify rice types with similar appearances have practical applications.

This paper describes a methodology for obtaining a high resolution dense point cloud using Kinect (J. Smisek and Pajdla, 2011) and HD cameras. Kinect produces a VGA resolution photograph and a noisy point cloud. But high resolution images of the same scene can easily be obtained using additional HD cameras. We combine the information to generate a high resolution dense point cloud. First, we do a joint calibration of Kinect and the HD cameras using traditional epipolar geometry (R. . Then we use the sparse point cloud obtained from Kinect and the high resolution information from the HD cameras to produce a dense point cloud in a registered frame using graph cut optimization. Experimental results show that this approach can significantly enhance the resolution of the Kinect point cloud.

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.

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.

We explore the possibility that the G2 gas cloud falling in toward SgrA * is the mass-loss envelope of a young T Tauri star. As the star plunges to smaller radius at 1000-6000 km s -1 , a strong bow shock forms where the stellar wind is impacted by the hot X-ray emitting gas in the vicinity of SgrA * . For a stellar mass-loss rate of 4 × 10 -8 M yr -1 and wind velocity 100 km s -1 , the bow shock will have an emission measure (EM = n 2 vol) at a distance ∼10 16 cm, similar to that inferred from the IR emission lines. The ionization of the dense bow shock gas is potentially provided by collisional ionization at the shock front and cooling radiation (X-ray and UV) from the post shock gas. The former would predict a constant line flux as a function of distance from SgrA * , while the latter will have increasing emission at lesser distances. In this model, the star and its mass-loss wind should survive pericenter passage since the wind is likely launched at 0.2 AU and this is much less than the Roche radius at pericenter (∼3 AU for a stellar mass of 2 M ). In this model, the emission cloud will probably survive pericenter passage, discriminating this scenario from others.

Estimation of fundamental matrices is important in 3D computer vision. It is well known that the estimation of fundamental matrices is sensitive to outliers-even a few of imprecise point correspondences may result in an estimated fundamental matrix inconsistent with the geometry setup of input images. In terms of interest points with localities, we have proposed a two-layer matching scheme that is a generalization of conventional normalized cross correlation (NCC), aiming to improve the precision of point correspondence. A locality of interest points means a set of interest points that are contiguous to each other in terms of 8-connectivity. The first layer of the matching scheme establishes locality correspondence, and the second layer refines point correspondence within matching localities. In this paper, we analyze a limitation of the similarity measure of localities proposed in our previous work and then we propose two new similarity measures to address the limitation. We test the two-layer matching scheme on images in Middlebury stereo datasets, with known fundamental matrices as the ground truth. From the experimental results, we observe the improvement of new measures of locality similarity, and we also observe that the estimated fundamental matrices are consistently close to the ground truth.

Purpose Large area traffic monitoring with high spatial and temporal resolution is a challenge that cannot be served by today available static infrastructure. Therefore, we present an automatic near real-time traffic monitoring approach using data of an airborne digital camera system with a frame rate of up to 3 fps. Methods By performing direct georeferencing on the obtained aerial images with the use of GPS/IMU data we are able to conduct near real-time traffic data extraction. The traffic processor consists mainly of three steps which are road extraction supported by a priori knowledge of road axes obtained from a road database, vehicle detection by edge extraction, and vehicle tracking based on normalized cross correlation. Results Traffic data is obtained with a correctness of up to 79% at a completeness of 68%. Conclusions With this system we are able to perform area-wide traffic monitoring with high actuality independent from any stationed infrastructure which makes the syste...

In the state-of-the-art PIV, image sensors are growing in size and PIV algorithms are increasing in spatial resolution capabilities. These technological advances allow for the simultaneous measurement of increasingly larger spatial scales ranges in a single PIV snapshot. To take fully advantage of the available spatial scales range, PIV setup constraints establish a coupling between the laser sheet thickness and the time between laser pulses. This condition did not apply with older technologies. The resulting value for the optimum laser sheet thickness is in a range where the superposed flow information in the direction of the imaging sensor introduces special characteristics errors at the small scale limit. This may compromise the measurement of flow quantities like dissipation and turbulent kinetic energy (tke), for which the contribution of the small and medium scales may be relevant. Within this scenario the assessment of the smallest scale resolved in the PIV measurement is nec...

In the past, the authors have addressed the tasks of assessing read-out and peak-locking errors separately. In this paper an improved approach is tested for assessing both errors simultaneously. The rationale is that, generally these errors are coupled in PIV images and the procedures described in previous works are not suitable to determine both errors together, especially when CCD cameras read-out error is relevant. The sources for each error are different enabling for any combination of relative magnitude between errors. The magnitude of the CCD read-out error seems to be related to camera operating conditions (temperature, humidity and sensor illumination) besides its own design, while peak-locking is related to the size of the tracer particle images, the truncation of particle images by window borders and the subpixel peak fitting or image deformation algorithms. The application of PIV in challenging conditions can generate situations where both errors combine with similar magnitude. For the error assessment in these conditions, the work proposes a methodology of general application to PIV. Once it is described, the application to selected synthetic and real cases is offered. The results indicate the possibility to asses bias errors in the order of 0.1 pixels for both sources (peak-locking and CCD read out) simultaneously. In addition, the determination of zones where these errors are not the dominant ones is presented.

Abstract: In this paper, efficient biometric security techniques for iris recognition system with high performance and high confidence are described. The system is based on an empirical analysis of the iris image and it is split in several steps using local image properties. The system steps are capturing iris patterns; determine the location of the iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting the iris code based on texture analysis using wavelet transforms; and classification of the iris code. The ...

Medical imaging is a vital component of large number of applications within current clinical settings. Image registration is a fundamental task in medical imaging. It is a process that overlays two or more medical images that are taken from different devices such as MRI, CT, PET and SPECT etc or taken at different angles. Integration of useful data obtained from different images is often required for medical diagnosis and procedure that brings spatial alignment among images is known as registration. We address the problem of image registration by adopting discrete cosine transformation (DCT) with a neural-network distance point learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training points from the medical image taken. The desired response of the network is in the form of a gray level value estimate of the current pixel using contour distance...

Internet revolution resulted in an explosive growth in multimedia applications. The rapid advancement of internet has made it easier to send the data/image accurate and faster to the destination. Watermarking biometric data is a still a relatively new issue, but it is of growing importance as more robust methods of verification and identification are being used. Using least significant bit technique isolating particular bits of pixel value in an image highlighting interesting aspects of that image. Higher order bits usually contain most of the significant visual images & lower order bits contain subtle details

PPG is a potential tool in clinical applications. Among such, the relationship between respiration and PPG signal has attracted attention in past decades. In this research, a bivariate AR spectral estimation method was utilized for the coherence analysis between these two signals. Ten healthy subjects participated in this research with signals measured at different respiratory rates. The results demonstrate that high coherence exists between respiration and PPG signal, whereas the coherence disappears in breath-holding experiments. These results imply that PPG signal reveals the respiratory information. The utilized method may provide an attractive alternative approach for the related researches.

A single-chip white light LED is commonly modeled by considering the phosphor coating as a homogeneous Lambertian light source. However, this approach leads to an incorrect optical simulation of phosphor-coated multi-chip LEDs due to the presence of a previously unreported spatial distribution of emission spots across the phosphor layer. We introduce "weighting" factors based on position-dependent light strength across the phosphor surface in order to improve the model accuracy. Following the modeling algorithm in the mid-field region, we have built up a precise and practical optical model by using Monte Carlo ray tracing and weighting factors. We measure the LED radiation distribution at several representative distances to test the model performance. In all cases, the accuracy is higher than 99.5% in normalized cross correlation between the simulated pattern and experimental measurement.

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.

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.

This paper analyzes the performance of sum of squared differences (SSD), sum of absolute differences (SAD), normalized cross correlation (NCC), zero mean normalized cross correlation (ZNCC) and several other proposed modified expressions of NCC. Experimental results on real images demonstrate that some of the proposed modified expressions of NCC are more efficient than conventional NCC for template matching. Three of the modified expressions of NCC perform similar to ZNCC however they are computationally less intensive. Modified expressions of NCC were also studied under different values of additive white Gaussian noise. Some of them perform better than ZNCC in terms of successfully found points and computation time for noisy images.

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The Internet as a whole does not use secure links, thus information in transit may be vulnerable to interruption as well. The important of reducing a chance of the information being detected during the transmission is being an issue in the real world now days. The Digital watermarking method provides for the quick and inexpensive distribution of digital information over the Internet. This method provides new ways of ensuring the sufficient protection of copyright holders in the intellectual property dispersion process. The property of digital watermarking images allows insertion of additional data in the image without altering the value of the image. This message is hidden in unused visual space in the image and stays below the human visible threshold for the image. Both seek to embed information inside a cover message with little or no degradation of the cover-object. In this paper investigate the following relevant concepts and terminology, history of watermarks and the properties of a watermarking system as well as a type of watermarking and applications. We are proposing edge detection using Gabor Filters. In this paper we are proposed least significant bit (LSB) substitution method to encrypt the message in the watermark image file. The benefits of the LSB are its simplicity to embed the bits of the message directly into the LSB plane of cover-image and many techniques using these methods. The LSB does not result in a human perceptible difference because the amplitude of the change is little therefore the human eye the resulting stego image will look identical to the cover image and this allows high perceptual transparency of the LSB. The spatial domain technique LSB substitution it would be able to use a pseudo-random number generator to determine the pixels to be used for embedding based on a given key. We are using DCT transform watermark algorithms based on robustness. The watermarking robustness have been calculated by the Peak Signal to Noise Ratio (PSNR) and Normalized cross correlation (NC) is used to quantify by the similarity between the real watermark and after extracting watermark.

Present ventricular rate-based arrhythmia detection algorithms lack specificity. Using a training set of 109 endocardial electrogram recordings, a sensitive and specific dual-chamber arrhythmia recognition algorithm has been developed. The algorithm uses atrial and ventricular rates, irregularity, degree of beat-to-beat similarity, and measure of electrogram complex distinctiveness to arrive at a diagnostic conclusion. A test set of 121 endocardial electrogram recordings obtained during provocative electrophysiology studies was then used for blinded validation of the algorithm. In normal rhythm, 1: i tachycardia, atrial tachycardia, atrial flutter, atrial fibrillation, ventricular tachycardia, and ventricular fibrillation, the percentages of sensitivity/specificity were, respectively, i 00/ 99, 100/99, 80/99, 89/98, 91/97, 92/100, and 100/98. Although ventricular rate alone can usually distinguish normal rhythm, ventricular tachycardia, and ventricular fibrillation, it is confounded by atrial arrhythmias and 1 : 1 tachycardias. When tested on a database, a ventricular rate-only algorithm resulted in sensitivity/specificity of 100/65, 90/78, and 100/99%, respectively, for these three rhythms. Therefore, the dual-chamber algorithm based on both temporal and morphologic measures provides better distinction of normal rhythm and ventricular tachycardia than existing methods, without sacrificing sensitivity.

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper we propose a novel algorithm for isolating lung nodules from spiral CT scans. The proposed algorithm is based on using four different types of deformable templates describing typical geometry and gray level distribution of lung nodules. These four types are (i) solid spherical model of large-size calcified and non-calcified nodules appearing in several successive slices; (ii) hollow spherical model of large lung cavity nodules; (iii) circular model of small nodules appearing in only a single slice; and (iv) semicircular model of lung wall nodules. Each template has a specific gray level pattern which is analytically estimated in order to fit the available empirical data. The detection combines the normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. This approach allows for isolating abnormalities which spread over several adjacent CT slices. Experiments with 200 patients' CT scans show that the developed techniques detect lung nodules more accurately than other known algorithms.

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

Motion estimation is the most challenging and time consuming stage in block based video codec. To reduce the computation time, many fast motion estimation algorithms were proposed and implemented. This paper proposes a quad-tree based Normalized Cross Correlation (NCC) measure for obtaining estimates of inter-frame motion. The measure operates in frequency domain using FFT algorithm as the similarity measure with an exhaustive full search in region of interest. NCC is a more suitable similarity measure than Sum of Absolute Difference (SAD) for reducing the temporal redundancy in video compression since we can attain flatter residual after motion compensation. The degrees of homogeneous and stationery regions are determined by selecting suitable initial fixed threshold for block partitioning. An experimental result of the proposed method shows that actual numbers of motion vectors are significantly less compared to existing methods with marginal effect on the quality of reconstructed...

The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.

In this paper, we present a new two-microphone approach that improves speech recognition accuracy when speech is masked by other speech. The algorithm improves on previous systems that have been successful in separating signals based on differences in arrival time of signal components from two microphones. The present algorithm differs from these efforts in that the signal selection takes place in the frequency domain. We observe that additional smoothing of the phase estimates over time and frequency is needed to support adequate speech recognition performance. We demonstrate that the algorithm described in this paper provides better recognition accuracy than timedomain-based signal separation algorithms, and at less than 10 percent of the computation cost.

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.