Uncertainty Model for Template Feature Matching

To analyze the noise characteristics on the power line, a practical noise model including Impulsive Noise (IN) and background noise is proposed. The parameters of IN are derived from measurements of household appliances, while the background noise is modeled based on the superimposed noise from multiple noise sources. In addition, an algorithm to calculate the disturb rate of IN is proposed, and the characteristic between actual noise model and the proposed noise model is compared. Simulation results show that the proposed noise model is very akin to actual power line noise. The results can be used to analyze, simulate, and mitigate the effect of the noise on Power Line Communication (PLC) systems.

The total variation (TV) minimization models are widely used in image processing, mainly due to their remarkable ability in preserving edges. There are many methods for solving the TV model. These methods, however, seldom consider the positivity constraint one should impose on image-processing problems. In this paper we develop and implement a new approach for TV image restoration. Our method is based on the multiplicative iterative algorithm originally developed for tomographic image reconstruction. The advantages of our algorithm are that it is very easy to derive and implement under different image noise models and it respects the positivity constraint. Our method can be applied to various noise models commonly used in image restoration, such as the Gaussian noise model, the Poisson noise model, and the impulsive noise model. In the numerical tests, we apply our algorithm to deblur images corrupted by Gaussian noise. The results show that our method give better restored images than the forward–backward splitting algorithm.

An integrated positioning system was developed to control the deep water installation (2,910 ft) and to provide long term X Y Z position monitoring of a submersible platform and its mooring system. After review of requirements and tentative installation procedures, a set of system performance requirements were created to guide system design and development. Development was done with frequent project team reviews to allow inclusion of refinements in positioning system requirements and installation procedures. Software simulation hardware testing, and operational simulations were performed. System field configuration and operations to install and monitor the unmanned semi-submersible platform is presented. INTRODUCTION This paper addresses the design, development, and use of a deepwater integrated positioning accuracy to perform system Installations. In addition, this system is used to perform long term position motion monitoring in very deep water. A critical problem to very deep water floating system installations is the ability to optimize structures and theirs mooring systems for survivability, ease of installation and overall cost. As installations move into very deep water (Le. greater than 2,000 ft) a significant cost saving can be realized by optimizing mooring system design. It is the author's belief that if installation control and positioning accuracies can be provided with a high degree of confidence and accuracy, deep water mooring system designs can be made more efficient, resulting in reduced costs of hardware, installation equipment, and installation time. The Naval Civil Engineering Laboratory (NCEL) is conducting a research and development program focusing on small deepwater semi-submersibles [1]. A principal effort in this research is the Motion Measurement Experiment (MME) which is an at-sea testing program involving a large-scale small semisubmersible moored in 2910 ft. of water [2]. A development effort began in late 1985 for a positioning system, and a navigation and survey plan for the MME. The subject of this paper, the MOSNAV 300 Integrated Navigation System was developed as a result of NCEL's project requirements. The positioning system was designed to perform two major independent operations:position vessels and systems to install moorings and the platform,perform unattended long term monitoring of platform X Y Z displacements and possible anchor movements. Figure 1 presents the final platform site installation system configuration. Significant aspects of the installation sequence included,real-time positioning of two independently operated Installation vessels,monitoring of the suspended mooring segment between the two vesselshigh accuracy real-time point positioning and orientation of each mooring segment on the seafloor. All operations were managed from a single command and control room onboard the Navy installation vessel. Important functions of the unattended X,Y,Z position data collection system include the ability to monitor anchor posltl0ns relative to the seafloor transponder array and acoustic telemetry of seafloor positioning data to a collection package onboard the platform.

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.

This article reports a method for integrating a low-cost positioning system, constructed using a Garmin N17 global positioning system (GPS) and an integrated inertial sensor unit, consisting of three single-axis microelectromechanical system (MEMS) gyros and one triaxial MEMS accelerometer, for autonomous off-highway vehicle use. Based on a vehicle position—velocity—attitude (PVA) model, a data fusion algorithm was formulated to extract more accurate and reliable positioning information from the raw data sensed by the GPS and inertial sensor unit. The developed integrated positioning system (IPS) was evaluated on an agricultural utility vehicle on three different sites. A real-time kinematic (RTK) differential GPS unit, capable of providing 2–3cm dynamic positioning accuracy while the position dilution of precision is low enough, was installed on the test vehicle to provide accurate positioning references in those evaluation tests. Results obtained from those tests showed that, when the vehicle was travelling on paved roads near buildings and/or under the trees, the maximum positioning error of the developed IPS was 0.50m, and that this maximum error level was reduced to 0.30m when the vehicle was travelling in open fields. The IPS could provide a position update rate at 50Hz; even the GPS could provide only a 1Hz update rate. Test results also revealed that this system could continuously provide accurate position signals when the GPS signal is lost for up to 30s. This research verified that a low-cost IPS could provide satisfactory position information for autonomous off-highway vehicle uses.

A digital halftone of blue noise characteristics is preferred as dots in the halftone of a constant input should be isotropically and homogeneously distributed. In practice, the placement of dots is constrained by a sampling grid and hence aliasing happens when the input gray level is in the middle range. To solve this problem, Lau et al. suggested replacing isolated dots by dot clusters to maintain the principal frequency of the output to be 1/2 when this happens. However, this model does not take into account that due to the stochastic nature of the dot distribution there is a considerable amount of energy distributed around the principal frequency and it causes aliasing problems even when the principal frequency of the output is 1/2 . Here, we present a new noise model that takes this factor into account. A halftoning algorithm is then proposed to generate halftones that satisfy the new noise model. By comparing its performance with that of some other algorithms that are proposed based on the traditional blue noise model and Lau et al.’s noise model, one can see that the proposed noise model can be a better model to describe the noise characteristics of a high-quality halftone.

Generalized signal-dependent noise model and parameter estimation for natural images

International audience

An improved noise model for pseudomorphic high electron mobility transistors (PHEMT) based on the combination of the artificial neural network (ANN) and conventional equivalent circuit modeling technique is presented. The frequency dispersion of the gate noise model parameter P, drain noise model parameter R, and the correlation coefficient C have been taken into account by using an ANN model. The influence of the gate leakage current can be accommodated by using the proposed noise model. The noise model parameters are determined directly from on wafer noise parameters measurement based on the noise correlation matrix technique. Good prediction for noise parameters and significant improvements of the accuracy of noise parameters are obtained up to 26 GHz for 2 × 40 μm gate width (number of gate fingers × unit gate width) 0.25 μm Double Heterojunction δ-doped PHEMTs over a wide range of bias points. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

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. >

The resolution of remote-sensing images is directly related to the application value of data products. Due to differences in imaging characteristics between digital cameras and remote-sensing cameras, the existing network models cannot get the best super resolution (SR) results of satellite images. In response to the requirements of remote-sensing image production, we propose a single image super-resolution (SISR) reconstruction method for specific type of remote-sensing satellite. First, we measure and model the imaging degradation phenomenon of remote-sensing satellite, including the image blur and noise model. Then, high-quality aerial images are down-sampled and degraded to construct paired training image datasets. We chose the most popular Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) as the basic structure and optimized the number of Residual-in-Residual Dense Block (RRDB) modules to further improve the processing efficiency. Finally, we perform a series of quantitative measurements of the SR image results, including image interpretation capability, reconstruction accuracy, ground resolution distance, and data processing efficiency, using higher resolution remote-sensing images as the benchmark. The experimental results demonstrate that our proposed method has higher interpretation capability and reconstruction accuracy for the SR processing of specific type remote-sensing satellite. Our proposed method is evaluated within a real satellite image product, that demonstrated it has the capability of pipeline super-resolution processing of remote sensing data products.

An exact series representation for a nearfield spherically isotropic noise model is introduced. The methodology uses the spherical harmonics expansion of the wavefield at a sensor to obtain the correlation between two sensors due to the nearfield isotropic noise field. The result is useful in nearfield application of sensor arrays. The proposed noise model can be utilized effectively to apply well established farfield array processing algorithms for nearfield applications. Specifically, any signal processing criterion based on farfield isotropic noise correlation can be reformulated with nearfield noise with this representation. A simple array gain optimization is used to demonstrate the new noise model.

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.

The introduction of unsupervised methods in denoising has shown that unpaired noisy data can be used to train denoising networks, which can not only produce high quality results but also enable us to sample multiple possible diverse denoising solutions. However, these systems rely on a probabilistic description of the imaging noise–a noise model. Until now, imaging noise has been modelled as pixel-independent in this context. While such models often capture shot noise and readout noise very well, they are unable to describe many of the complex patterns that occur in real life applications. Here, we introduce a novel learning-based autoregressive noise model to describe imaging noise and show how it can enable unsupervised denoising for settings with complex structured noise patterns. We show that our deep autoregressive noise models have the potential to greatly improve denoising quality in structured noise datasets. We showcase the capability of our approach on various simulated datasets and on real photo-acoustic imaging data.KeywordsDenoisingDeep learningAutoregressiveNoiseDiverse solutionsVAEPhotoacoustic imaging

In this study, a hierarchical clustering matching (HCM) algorithm is proposed to match features with ambiguity due to repetitive patterns in visual odometry. Visual odometry is a real-time system that estimates the motions of camera setups, in which feature matching is a key step for tracking and relocalization. However, it is still difficult to remove outliers fast and reliably when a high proportion of outliers exist. The proposed HCM algorithm solves this problem by clustering accurate matches hierarchically. Dubious matches excluded from any clusters are removed during the iterations, which finally converges when new clusters no longer generate. Local geometric consistency and descriptor of features are both considered to be a metric to link two clusters using a centroid linkage criterion. Experimental results demonstrate that the proposed method works well on solving the problem mentioned above. Compared to state-of-the-art methods on feature matching, HCM performs much better on efficiency with comparable accuracy.