bf s _ 3 a spectral and spatial measure of local perceived sharpness in natural images this paper presents an algorithm designed to measure the local perceived sharpness in an image our method utilizes both spectral and spatial properties of the image for each block we measure the slope of the magnitude spectrum and the total spatial variation these measures are then adjusted to account for visual perception and then the adjusted measures are combined via a weighted geometric mean the resulting measure i e s3 spectral and spatial sharpness yields a perceived sharpness map in which greater values denote perceptually sharper regions this map can be collapsed into a single index which quantifies the overall perceived sharpness of the whole image we demonstrate the utility of the s3 measure for within image and across image sharpness prediction no reference image quality assessment of blurred images and monotonic estimation of the standard deviation of the impulse response used in gaussian blurring we further evaluate the accuracy of s3 in local sharpness estimation by comparing s3 maps to sharpness maps generated by human subjects we show that s3 can generate sharpness maps which are highly correlated with the human subject maps

brief state space representation for fractional order controllers this article proposes an infinite dimensional state space realization for linear filters with transfer function c d s c 0 1 s w b 1 s w h d where 0 w b w h and 0

two soft thresholding based iterative algorithms for image deblurring iterative regularization algorithms such as the conjugate gradient algorithm for least squares problems cgls and the modified residual norm steepest descent mrnsd algorithm are popular tools for solving large scale linear systems arising from image deblurring problems these algorithms however are hindered by a semi convergence behavior in that the quality of the computed solution first increases and then decreases in this paper in order to overcome the semi convergence behavior we propose two iterative algorithms based on soft thresholding for image deblurring problems one of them combines cgls with a denoising technique like soft thresholding at each iteration and another combines mrnsd with soft thresholding in a similar way we prove the convergence of mrnsd and soft thresholding based algorithm numerical results show that the proposed algorithms overcome the semi convergence behavior and the restoration results are slightly better than those of cgls and mrnsd with their optimal stopping iterations

measure of image sharpness using eigenvalues this paper deals with the design and implementation of a novel image sharpness metric based on the statistical approach this sharpness metric is derived by modelling the image sharpness problem as a generalized eigenvalues problem this problem is solved using rayleigh quotient optimization where relevant statistical information of an image is extracted and then represented through a series of eigenvalues the novelty of this paper comes from the application of eigenvalues in image sharpness metric formulation to provide robust assessment with the presence of various blur and noisy conditions firstly the input image is normalized by its energy to minimize the effects caused by image contrast secondly the covariance matrix is computed from this normalized image before it is diagonalized using singular values decomposition svd to obtain a series of eigenvalues finally the image sharpness of the normalized image is determined by the trace of the first several eigenvalues the performance of the proposed metric is gauged by comparing with several objective image sharpness metrics experimental results using synthetic and real images with known and unknown distortion conditions show the robustness and feasibility of the proposed metric in providing relative image sharpness in particular the proposed metric provides wider working range and more precise prediction consistency under all tested deformation conditions although it is slightly expensive in terms of computation than other metrics

sliding mode control of uncertain fractional order systems with delay abstractthis paper presents a sliding mode control smc based framework to design a stabilizing controller for uncertain fractional order time delay systems fotds using the lambert w function te

robust stability analysis of linear time delay systems by lambert w function some extreme point results robust stability conditions are derived for linear time delay systems using lambert w function the characteristic quasi polynomials of the systems are assumed to be factorized it is proven that if uncertainties in the coefficients of the quasi polynomial are set in appropriate regions in the complex plane we can enjoy extreme point results finite number of stability checks at some points of the boarder of the regions suffice the strength of lambert w function approach lies in the fact that the function is implemented on some standard software packages such as mathematica maple or matlab which afford to compute the function value very easily the above two points make the stability test for the class of uncertain time delay systems quite practical

a novel generalized dct based jnd profile based on an elaborate cm jnd model for variable block sized transforms in monochrome images in this paper we propose a new dct based just noticeable difference jnd profile incorporating the spatial contrast sensitivity function the luminance adaptation effect and the contrast masking cm effect the proposed jnd profile overcomes two limitations of conventional jnd profiles 1 the cm jnd models in the conventional jnd profiles employed simple texture complexity metrics which are not often highly correlated with perceived complexity especially for unstructured patterns so we proposed a new texture complexity metric that considers not only contrast intensity but also structureness of image patterns called the structural contrast index we also newly found out that as the structural contrast index of a background texture pattern increases the modulation factors for cm jnd show a bandpass property in frequency based on this observation a new cm jnd is modeled as a function of dct frequency and the proposed structural contrast index showing significantly high correlations with measured cm jnd values and 2 while the conventional dct based jnd profiles are only applicable for specific transform block sizes our proposed dct based jnd profile is first designed to be applicable to any size of transform by deriving a new summation effect function which can also be appropriately applied for quad tree transform of high efficiency video coding for the overall performance the proposed dct based jnd profile shows more tolerance for distortions with better perceptual quality than other jnd profiles under comparison

a ground truth based vanishing point detection algorithm a bayesian probability based vanishing point detection algorithm is presented which introduces the use of multiple features and training with ground truth data to determine vanishing point locations the vanishing points of 352 images were manually identified to create ground truth data each intersection is assigned a probability of being coincident with a ground truth vanishing point based upon conditional probabilities of a number of features the results of this algorithm are demonstrated to be superior to the results of a similar algorithm where each intersection is considered to be of equal importance the advantage of this algorithm is that multiple features derived from ground truth training are used to determine vanishing point location

a no reference image blur metric based on the cumulative probability of blur detection cpbd this paper presents a no reference image blur metric that is based on the study of human blur perception for varying contrast values the metric utilizes a probabilistic model to estimate the probability of detecting blur at each edge in the image and then the information is pooled by computing the cumulative probability of blur detection cpbd the performance of the metric is demonstated by comparing it with existing no reference sharpness blurriness metrics for various publicly available image databases

blind image quality assessment based on high order statistics aggregation blind image quality assessment biqa research aims to develop a perceptual model to evaluate the quality of distorted images automatically and accurately without access to the non distorted reference images the state of the art general purpose biqa methods can be classified into two categories according to the types of features used the first includes handcrafted features which rely on the statistical regularities of natural images these however are not suitable for images containing text and artificial graphics the second includes learning based features which invariably require large codebook or supervised codebook updating procedures to obtain satisfactory performance these are time consuming and not applicable in practice in this paper we propose a novel general purpose biqa method based on high order statistics aggregation hosa requiring only a small codebook hosa consists of three steps first local normalized image patches are extracted as local features through a regular grid and a codebook containing 100 codewords is constructed by k means clustering in addition to the mean of each cluster the diagonal covariance and coskewness i e dimension wise variance and skewness of clusters are also calculated second each local feature is softly assigned to several nearest clusters and the differences of high order statistics mean variance and skewness between local features and corresponding clusters are softly aggregated to build the global quality aware image representation finally support vector regression is adopted to learn the mapping between perceptual features and subjective opinion scores the proposed method has been extensively evaluated on ten image databases with both simulated and realistic image distortions and shows highly competitive performance to the state of the art biqa methods

extracting straight lines this paper presents a new approach to the extraction of straight lines in intensity images pixels are grouped into line support regions of similar gradient orientation and then the structure of the associated intensity surface is used to determine the location and properties of the edge the resulting regions and extracted edge parameters form a low level representation of the intensity variations in the image that can be used for a variety of purposes the algorithm appears to be more effective than previous techniques for two key reasons 1 the gradient orientation rather than gradient magnitude is used as the initial organizing criterion prior to the extraction of straight lines and 2 the global context of the intensity variations associated with a straight line is determined prior to any local decisions about participating edge elements

a no reference image sharpness metric based on structural information using sparse representation as a ubiquitous image distortion blur casts non trivial influence on image visual quality many image sharpness assessment methods have been proposed in the views of edge information gradient map frequency spectrum or other natural image statistics features in this paper we propose a no reference image sharpness metric based on structural information using sparse representation sr we observe that the dictionary atoms learned by sr algorithm convey clear structural information considering the distinct sensibility of human visual system hvs to different structures we use the learned dictionary to encode the patches of the blurry image to embed the locality of the representation a multi scale spatial max pooling scheme is incorporated the final sharpness score is given by an efficient linear support vector regression svr model we evaluate our approach on three public databases i e live ii tid2008 and csiq the experiments demonstrate that the proposed method achieves competitive performance compared with the state of the art blind image sharpness assessment algorithms

utilizing image scales towards totally training free blind image quality assessment a new approach to blind image quality assessment biqa requiring no training is proposed in this paper the approach is named as blind image quality evaluator based on scales and works by evaluating the global difference of the query image analyzed at different scales with the query image at original resolution the approach is based on the ability of the natural images to exhibit redundant information over various scales a distorted image is considered as a deviation from the natural image and bereft of the redundancy present in the original image the similarity of the original resolution image with its down scaled version will decrease more when the image is distorted more therefore the dissimilarities of an image with its low resolution versions are cumulated in the proposed method we dissolve the query image into its scale space and measure the global dissimilarity with the co occurrence histograms of the original and its scaled images these scaled images are the low pass versions of the original image the dissimilarity called low pass error is calculated by comparing the low pass versions across scales with the original image the high pass versions of the image in different scales are obtained by wavelet decomposition and their dissimilarity from the original image is also calculated this dissimilarity called high pass error is computed with the variance and gradient histograms and weighted by the contrast sensitivity function to make it perceptually effective these two kinds of dissimilarities are combined together to derive the quality score of the query image this method requires absolutely no training with the distorted image pristine images or subjective human scores to predict the perceptual quality but uses the intrinsic global change of the query image across scales the performance of the proposed method is evaluated across six publicly available databases and found to be competitive with the state of the art techniques

brief analysis of fractional delay systems of retarded and neutral type this paper analyses several properties linked to the robust control of fractional differential systems with delays the bibo stability of both retarded and neutral fractional delay systems is related to the location of their poles in the particular case of retarded systems we give some properties of the poles of the system and the singular values of its hankel operator

no reference image quality assessment in the spatial domain we propose a natural scene statistic based distortion generic blind no reference nr image quality assessment iqa model that operates in the spatial domain the new model dubbed blind referenceless image spatial quality evaluator brisque does not compute distortion specific features such as ringing blur or blocking but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of naturalness in the image due to the presence of distortions thereby leading to a holistic measure of quality the underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model no transformation to another coordinate frame dct wavelet etc is required distinguishing it from prior nr iqa approaches despite its simplicity we are able to show that brisque is statistically better than the full reference peak signal to noise ratio and the structural similarity index and is highly competitive with respect to all present day distortion generic nr iqa algorithms brisque has very low computational complexity making it well suited for real time applications brisque features may be used for distortion identification as well to illustrate a new practical application of brisque we describe how a nonblind image denoising algorithm can be augmented with brisque in order to perform blind image denoising results show that brisque augmentation leads to performance improvements over state of the art methods a software release of brisque is available online http live ece utexas edu research quality brisque release zip for public use and evaluation

on the positive effect of delay on the rate of convergence of a class of linear time delayed systems this article is a comprehensive study of a long observed phenomenon of increase in the stability margin and so the rate of convergence of a class of linear systems due to time delay we use the lambert w function to determine 1 in what systems the delay can lead to increase in the rate of convergence 2 the exact range of time delay for which the rate of convergence is greater than that of the delay free system and 3 an estimate on the value of the delay that leads to the maximum rate of convergence for the special case when the system matrix eigenvalues are all negative real numbers we expand our results to show that the rate of convergence in the presence of delay depends only on the eigenvalues with minimum and maximum real parts moreover we determine the exact value of the maximum rate of convergence and the corresponding maximizing time delay we demonstrate our results through a numerical example on the practical application in accelerating an agreement algorithm for networked systems by using a delayed feedback

optimal depth estimation by combining focus measures using genetic programming three dimensional 3d shape reconstruction is a fundamental problem in machine vision applications shape from focus sff is one of the passive optical methods for 3d shape recovery that uses degree of focus as a cue to estimate 3d shape in this approach usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence however the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects to address this problem we develop optimal composite depth ocd function through genetic programming gp for accurate depth estimation the ocd function is constructed by optimally combining the primary information extracted using one or more focus measures the genetically developed composite function is then used to compute the optimal depth map of objects the performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing sff methods moreover it is found that the heterogeneous function is more effective than homogeneous function

a note on the use of the lambert w function in the stability analysis of time delay systems the lambert w function is defined to be the multivalued inverse of the function w we w z this function has been used in an extremely wide variety of applications including the stability analysis of fractional order as well as integer order time delay systems the latter application is based on taking the mth power and or nth root of the transcendental characteristic equation tce and representing the roots of the derived tce s in terms of w functions in this note we re examine such an application of using the lambert w function through actually computing the root distributions of the derived tces of some chosen orders it is found that the rightmost root of the original tce is not necessarily a principal branch lambert w function solution and that a derived tce obtained by taking the mth power of the original tce introduces superfluous roots to the system with these observations some deficiencies displayed in the literature chen y q moore k l 2002a analytical stability bound for delayed second order systems with repeating poles using lambert w function automatica 38 5 891 895 chen y q moore k l 2002b analytical stability bound for a class of delayed fractional order dynamic systems nonlinear dynamics 29 1 4 191 200 are pointed out moreover we clarify the correct use of lambert w function to stability analysis of a class of time delay systems this will actually enlarge the application scope of the lambert w function which is becoming a standard library function for various commercial symbolic software packages to time delay systems

a no reference objective image sharpness metric based on the notion of just noticeable blur jnb this work presents a perceptual based no reference objective image sharpness blurriness metric by integrating the concept of just noticeable blur into a probability summation model unlike existing objective no reference image sharpness blurriness metrics the proposed metric is able to predict the relative amount of blurriness in images with different content results are provided to illustrate the performance of the proposed perceptual based sharpness metric these results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content

automatic parameter selection for denoising algorithms using a no reference measure of image content across the field of inverse problems in image and video processing nearly all algorithms have various parameters which need to be set in order to yield good results in practice usually the choice of such parameters is made empirically with trial and error if no ground truth reference is available some analytical methods such as cross validation and stein s unbiased risk estimate sure have been successfully used to set such parameters however these methods tend to be strongly reliant on restrictive assumptions on the noise and also computationally heavy in this paper we propose a no reference metric q which is based upon singular value decomposition of local image gradient matrix and provides a quantitative measure of true image content i e sharpness and contrast as manifested in visually salient geometric features such as edges in the presence of noise and other disturbances this measure 1 is easy to compute 2 reacts reasonably to both blur and random noise and 3 works well even when the noise is not gaussian the proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms ample simulated and real data experiments support our claims furthermore tests using the tid2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions

stability of linear time invariant fractional delay systems of retarded type in the space of delay parameters this paper presents a new method for assessing the bounded input bounded output stability of a class of fractional delay systems with commensurate orders and multiple commensurate delays of retarded type in the proposed method first by mapping the principal sheet of the riemann surface and a pseudo delay transformation an auxiliary polynomial is generated then this auxiliary polynomial is employed to find roots of the characteristic equation on the imaginary axis the properties of the root path close to these roots are used to identify intervals of delay values in which the system is stable the obtained results are illustrated via some numerical examples

analysis of focus measure operators for shape from focus shape from focus sff has widely been studied in computer vision as a passive depth recovery and 3d reconstruction method one of the main stages in sff is the computation of the focus level for every pixel of an image by means of a focus measure operator in this work a methodology to compare the performance of different focus measure operators for shape from focus is presented and applied the selected operators have been chosen from an extensive review of the state of the art the performance of the different operators has been assessed through experiments carried out under different conditions such as image noise level contrast saturation and window size such performance is discussed in terms of the working principles of the analyzed operators

toward a no reference image quality assessment using statistics of perceptual color descriptors analysis of the statistical properties of natural images has played a vital role in the design of no reference nr image quality assessment iqa techniques in this paper we propose parametric models describing the general characteristics of chromatic data in natural images they provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions the established models capture the correlation of chromatic data between spatially adjacent pixels by means of color invariance descriptors the use of color invariance descriptors is inspired by their relevance to visual perception since they provide less sensitive descriptions of image scenes against viewing geometry and illumination variations than luminances in order to approximate the visual quality perception of chromatic distortions we devise four parametric models derived from invariance descriptors representing independent aspects of color perception 1 hue 2 saturation 3 opponent angle and 4 spherical angle the practical utility of the proposed models is examined by deploying them in our new general purpose nr iqa metric the metric initially estimates the parameters of the proposed chromatic models from an input image to constitute a collection of quality aware features qaf thereafter a machine learning technique is applied to predict visual quality given a set of extracted qafs experimentation performed on large scale image databases demonstrates that the proposed metric correlates well with the provided subjective ratings of image quality over commonly encountered achromatic and chromatic distortions indicating that it can be deployed on a wide variety of color image processing problems as a generalized iqa solution

hybrid no reference natural image quality assessment of noisy blurry jpeg2000 and jpeg images in this paper we propose a new image quality assessment method based on a hybrid of curvelet wavelet and cosine transforms called hybrid no reference hnr model from the properties of natural scene statistics the peak coordinates of the transformed coefficient histogram of filtered natural images occupy well defined clusters in peak coordinate space which makes nr possible compared to other methods hnr has three benefits 1 it is an nr method applicable to arbitrary images without compromising the prediction accuracy of full reference methods 2 as far as we know it is the only general nr method well suited for four types of filters noise blur jpeg2000 and jpeg compression and 3 it can classify the filter types of the image and predict filter levels even when the image is results from the application of two different filters we tested hnr on very intensive video image database our image library and laboratory for image video engineering a public library results are compared to the state of the art methods including peak snr structural similarity visual information fidelity and so on

an efficient approach for face recognition based on common eigenvalues in this paper a simple technique is proposed for face recognition among many human faces it is based on the polynomial coefficients covariance matrix and algorithm on common eigenvalues the main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors a symmetric matrix is calculated using the polynomial coefficients based companion matrices of two compared images the nullity of a calculated symmetric matrix is used as similarity measure for face recognition the value of nullity is very small for dissimilar images and distinctly large for similar face images the feasibility of the propose approach is demonstrated on three face databases i e the orl database the yale database b and the feret database experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination

reduced reference quality assessment based on the entropy of dwt coefficients of locally weighted gradient magnitudes perceptual image quality assessment iqa attempts to use computational models to estimate the image quality in accordance with subjective evaluations reduced reference iqa rriqa methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images finding a balance between the number of rr features and accuracy of the estimated image quality is essential and important in iqa in this paper we propose a training free low cost rriqa method that requires a very small number of rr features six rr features the proposed rriqa algorithm is based on the discrete wavelet transform dwt of locally weighted gradient magnitudes we apply human visual system s contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner the rr features are computed by measuring the entropy of each dwt subband for each scale and pooling the subband entropies along all orientations resulting in l rr features one average entropy per scale for an l level dwt extensive experiments performed on seven large scale benchmark databases demonstrate that the proposed rriqa method delivers highly competitive performance as compared with the state of the art rriqa models as well as full reference ones for both natural and texture images the matlab source code of redlog and the evaluation results are publicly available online at https http lab engineering asu edu ivulab software redlog

technical communique analytical stability bound for delayed second order systems with repeating poles using lambert function w by using lambert function the analytical stability bound is obtained in this paper for delayed second order systems with repeatable poles an example is presented to illustrate the obtained analytical result

universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning universal blind image quality assessment iqa metrics that can work for various distortions are of great importance for image processing systems because neither ground truths are available nor the distortion types are aware all the time in practice existing state of the art universal blind iqa algorithms are developed based on natural scene statistics nss although nss based metrics obtained promising performance they have some limitations 1 they use either the gaussian scale mixture model or generalized gaussian density to predict the nongaussian marginal distribution of wavelet gabor or discrete cosine transform coefficients the prediction error makes the extracted features unable to reflect the change in nongaussianity ng accurately the existing algorithms use the joint statistical model and structural similarity to model the local dependency ld although this ld essentially encodes the information redundancy in natural images these models do not use information divergence to measure the ld although the exponential decay characteristic edc represents the property of natural images that large small wavelet coefficient magnitudes tend to be persistent across scales which is highly correlated with image degradations it has not been applied to the universal blind iqa metrics and 2 all the universal blind iqa metrics use the same similarity measure for different features for learning the universal blind iqa metrics though these features have different properties to address the aforementioned problems we propose to construct new universal blind quality indicators using all the three types of nss i e the ng ld and edc and incorporating the heterogeneous property of multiple kernel learning mkl by analyzing how different distortions affect these statistical properties we present two universal blind quality assessment models nss global scheme and nss two step scheme in the proposed metrics 1 we exploit the ng of natural images using the original marginal distribution of wavelet coefficients 2 we measure correlations between wavelet coefficients using mutual information defined in information theory 3 we use features of edc in universal blind image quality prediction directly and 4 we introduce mkl to measure the similarity of different features using different kernels thorough experimental results on the laboratory for image and video engineering database ii and the tampere image database2008 demonstrate that both metrics are in remarkably high consistency with the human perception and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes for various types of distortions

quality assessment of sharpened images challenges methodology and objective metrics most of the effort in image quality assessment qa has been so far dedicated to the degradation of the image however there are also many algorithms in the image processing chain that can enhance the quality of an input image these include procedures for contrast enhancement deblurring sharpening up sampling denoising transfer function compensation and so on in this paper possible strategies for the qa of sharpened images are investigated this task is not trivial because the sharpening techniques can increase the perceived quality as well as introduce artifacts leading to the quality drop over sharpening here the framework specifically adapted for the qa of sharpened images and objective metrics comparison in this context is introduced however the framework can be adopted in other qa areas as well the problem of selecting the correct procedure for subjective evaluation was addressed and a subjective test on blurred sharpened and over sharpened images was performed in order to demonstrate the use of the framework the obtained ground truth data were used for testing the suitability of the state of the art objective quality metrics for the assessment of sharpened images the comparison was performed by novel procedure using rank order correlation analyses which is found more appropriate for the task than standard methods furthermore seven possible augmentations of the no reference s3 metric adapted for sharpened images are proposed the performance of the metric is significantly improved and also superior over the rest of the tested quality criteria with respect to the subjective data

selecting the optimal focus measure for autofocusing and depth from focus a method is described for selecting the optimal focus measure with respect to gray level noise from a given set of focus measures in passive autofocusing and depth from focus applications the method is based on two new metrics that have been defined for estimating the noise sensitivity of different focus measures the first metric the autofocusing uncertainty measure aum is useful in understanding the relation between gray level noise and the resulting error in lens position for autofocusing the second metric autofocusing root mean square error arms error is an improved metric closely related to aum aum and arms error metrics are based on a theoretical noise sensitivity analysis of focus measures and they are related by a monotonic expression the theoretical results are validated by actual and simulation experiments for a given camera the optimally accurate focus measure may change from one object to the other depending on their focused images therefore selecting the optimal focus measure from a given set involves computing all focus measures in the set

a numerical method for stability windows and unstable root locus calculation for linear fractional time delay systems this paper aims to provide a numerical algorithm able to locate all unstable poles and therefore the characterization of the stability as a function of the delay for a class of linear fractional order neutral systems with multiple commensurate delays we start by giving the asymptotic position of the chains of poles and the conditions for their stability for a small delay when these conditions are met the root continuity argument and some simple substitutions allow us to determine the locations where some roots cross the imaginary axis providing therefore the complete characterization of the stability windows the same method can be extended to provide the position of all unstable poles as a function of the delay

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

camera calibration by vanishing lines for 3 d computer vision a novel approach to camera calibration by vanishing lines is proposed calibrated parameters include the orientation position and focal length of a camera a hexagon is used as the calibration target to generate a vanishing line of the ground plane from its projected image it is shown that the vanishing line includes useful geometric hints about the camera orientation parameters and the focal length from which the orientation parameters can be solved easily and analytically and the camera position parameters can be calibrated by the use of related geometric projective relationships the simplicity of the target eliminates the complexity of the environment setup and simplifies the feature extraction in relevant image processing the calibration formulas are also simple to compute experimental results show the feasibility of the proposed approach

edge modeling by two blur parameters in varying contrasts this paper presents a method of modeling edge profiles with two blur parameters and estimating and predicting those edge parameters with varying brightness combinations and camera to object distances cod first the validity of the edge model is proven mathematically then it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images estimation of the two blur parameters for each observed edge profile is performed with a brute force method to find parameters that produce global minimum errors then using the estimated blur parameters actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method i e kriging the predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark side edge brightness and light side edge brightness following a certain global trend this is similar across varying cods the proposed edge model is compared with a one blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile the comparison results suggest that the proposed edge model has superiority over the one blur parameter edge model in most cases where edges have varying brightness combinations

fractional order systems and pi sup spl lambda d sup spl mu controllers dynamic systems of an arbitrary real order fractional order systems are considered the concept of a fractional order pi sup spl lambda d sup spl mu controller involving fractional order integrator and fractional order differentiator is proposed the laplace transform formula for a new function of the mittag leffler type made it possible to obtain explicit analytical expressions for the unit step and unit impulse response of a linear fractional order system with fractional order controller for both open and closed loops an example demonstrating the use of the obtained formulas and the advantages of the proposed pi sup spl lambda d sup spl mu controllers is given

a robust line extraction method by unsupervised line clustering abstract this paper describes a new method of extracting straight lines based on unsupervised line clustering it is assumed that each line support region lsr in an image is composed of pixels that share similar gradient orientation values therefore by an appropriate partitioning of gradient space the sets of parallel lines can be more easily extracted previous works on partitioning gradient space however relied on ad hoc methods and cannot be used as reliable tools for the extraction of the number of clusters in gradient space in order to handle such a clustering issue the bhattacharyya distance is introduced to define a measure for cluster separability and thereafter to estimate the number of inherent clusters subsequent to the clustering stage each extracted line support region undergoes a consistency test to evaluate its validity in terms of uncertainty descriptors for the consistency test an entropy based line selection scheme is formulated and a theory from robust statistics is adopted the feasibility of the proposed line extraction method is assessed by considering the issue of vanishing point detection

technical communique the lambert w function and the spectrum of some multidimensional time delay systems in this note we find an explicit expression for the eigenvalues of a retarded time delay system with one delay for the special case that the system matrices are simultaneously triangularizable which includes the case where they commute using matrix function definitions we define a matrix version of the lambert w function from which we form the expression we prove by counter example that some expressions in other publications on lambert w for time delay systems do not always hold

fuzzy relation equations for coding decoding processes of images and videos we adopt fuzzy relation equations with continuous triangular norms for compression decompression processes of grey images colour images in the rgb space and frames of videos by comparing the results of the reconstructed images with standard methods like jpeg and mpeg 4 any image is subdivided in blocks and each block is coded decoded using arbitrary fuzzy sets as coders in the fuzzy equations we evaluate the peak signal to noise ratio psnr on the decompressed images for several values of the compression rates as measure of the quality of images and frames reconstructed the original frames of a video are classified in intra frames and predictive frames by using a similarity measure based on the well known lukasiewicz t norm

analyzing the effect of jpeg compression on local variance of image intensity the local variance of image intensity is a typical measure of image smoothness it has been extensively used for example to measure the visual saliency or to adjust the filtering strength in image processing and analysis however to the best of our knowledge no analytical work has been reported about the effect of jpeg compression on image local variance in this paper a theoretical analysis on the variation of local variance caused by jpeg compression is presented first the expectation of intensity variance of 8 times 8 non overlapping blocks in a jpeg image is derived the expectation is determined by the laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the jpeg compression second some interesting properties that describe the behavior of the local variance under different degrees of jpeg compression are discussed finally both the simulation and the experiments are performed to verify our derivation and discussion the theoretical analysis presented in this paper provides some new insights into the behavior of local variance under jpeg compression moreover it has the potential to be used in some areas of image processing and analysis such as image enhancement image quality assessment and image filtering

image sharpness assessment based on local phase coherence sharpness is an important determinant in visual assessment of image quality the human visual system is able to effortlessly detect blur and evaluate sharpness of visual images but the underlying mechanism is not fully understood existing blur sharpness evaluation algorithms are mostly based on edge width local gradient or energy reduction of global local high frequency content here we understand the subject from a different perspective where sharpness is identified as strong local phase coherence lpc near distinctive image features evaluated in the complex wavelet transform domain previous lpc computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale space here we propose a flexible framework that allows for lpc computation in arbitrary fractional scales we then develop a new sharpness assessment algorithm without referencing the original image we use four subject rated publicly available image databases to test the proposed algorithm which demonstrates competitive performance when compared with state of the art algorithms

controlling the false discovery rate a practical and powerful approach to multiple testing summary the common approach to the multiplicity problem calls for controlling the familywise error rate fwer this approach though has faults and we point out a few a different approach to problems of multiple significance testing is presented it calls for controlling the expected proportion of falsely rejected hypotheses the false discovery rate this error rate is equivalent to the fwer when all hypotheses are true but is smaller otherwise therefore in problems where the control of the false discovery rate rather than that of the fwer is desired there is potential for a gain in power a simple sequential bonferronitype procedure is proved to control the false discovery rate for independent test statistics and a simulation study shows that the gain in power is substantial the use of the new procedure and the appropriateness of the criterion are illustrated with examples

adaptive image coding with perceptual distortion control this paper presents a discrete cosine transform dct based locally adaptive perceptual image coder which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate the new coder uses a locally adaptive perceptual quantization scheme based on a tractable perceptual distortion metric our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion the derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet the desired target perceptual distortion the proposed coding scheme is flexible in that it can be easily extended to work with any subband based decomposition in addition to block based transform methods compared to existing perceptual coding methods the proposed perceptual coding method exhibits superior performance in terms of bit rate and distortion control coding results are presented to illustrate the performance of the presented coding scheme

calculation of the rightmost characteristic root of retarded time delay systems via lambert w function abstract generally it is not easy to analyze the stability of time delay systems especially when the systems are of high order or they have multiple delays for retarded time delay systems the stability can be determined by the rightmost characteristic root this paper presents a case study on the calculation of the rightmost root three practical time delay systems are discussed the first system is an oscillator with delayed state feedback the second one is a delayed neural network based on the fitzhugh nagumo model for neural cells and the third one is a car model of suspension with a delayed sky hook damper by using the lambert w function the rightmost root becomes a root of a function associated with the principal branch of the lambert w function then the rightmost root is located by using newton raphson s scheme or halley s accelerating scheme some suggestions for successful application of the proposed method are given

depth from defocus a spatial domain approach a new method named stm is described for determining distance of objects and rapid autofocusing of camera systems stm uses image defocus information and is based on a new spatial domain convolution deconvolution transform the method requires only two images taken with different camera parameters such as lens position focal length and aperture diameter both images can be arbitrarily blurred and neither of them needs to be a focused image therefore stm is very fast in comparison with depth from focus methods which search for the lens position or focal length of best focus the method involves simple local operations and can be easily implemented in parallel to obtain the depth map of a scene stm has been implemented on an actual camera system named sparcs experiments on the performance of stm and their results on real world planar objects are presented the results indicate that the accuracy of stm compares well with depth from focus methods and is useful in practical applications the utility of the method is demonstrated for rapid autofocusing of electronic cameras

a motion compression reconstruction method based on max t norm composite fuzzy relational equations a motion compression reconstruction method based on max t norm composite fuzzy relational equations mcf is proposed where into intra pictures i pictures and predictive pictures p pictures of the original motion are compressed by uniform and non uniform coders respectively the non uniform coders of the proposed method can preserve edge information of p pictures on the compressed image to perform an effective compression reconstruction of the p pictures a design method of non uniform coders is proposed based on an overlap level of fuzzy sets and a fuzzy equalization an experiment using 10 p pictures confirms that the root mean square error of the reconstructed images obtained by the proposed non uniform coders is decreased to 89 4 of that one of the uniform coders under the condition that compression rate the ratio between the file size of compressed image and original one is 0 0057 two test motions tennis and woman 100 frames are compressed and reconstructed by the proposed mcf

water reflection recognition based on motion blur invariant moments in curvelet space water reflection a typical imperfect reflection symmetry problem plays an important role in image content analysis existing techniques of symmetry recognition however cannot recognize water reflection images correctly because of the complex and various distortions caused by the water wave hence we propose a novel water reflection recognition technique to solve the problem first we construct a novel feature space composed of motion blur invariant moments in low frequency curvelet space and of curvelet coefficients in high frequency curvelet space second we propose an efficient algorithm including two sub algorithms low frequency reflection cost minimization and high frequency curvelet coefficients discrimination to classify water reflection images and to determine the reflection axis through experimenting on authentic images in a series of tasks the proposed techniques prove effective and reliable in classifying water reflection images and detecting the reflection axis as well as in retrieving images with water reflection