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In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speedup of the algorithm using the methodology proposed in the paper. [ABSTRACT FROM AUTHOR]

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of this paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins, and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors. [ABSTRACT FROM AUTHOR]

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task [1] and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in [21. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on non- differentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using £1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios. [ABSTRACT FROM AUTHOR]

The majorize-minimize (MM) optimization technique has received considerable attention in signal and image processing applications, as well as in statistics literature. At each iteration of an MM algorithm, one constructs a tangent majorant function that majorizes the given cost function and is equal to it at the current iterate. The next iterate is obtained by minimizing this tangent majorant function, resulting in a sequence of iterates that reduces the cost function monotonically. A well-known special case of MM methods are expectation-maximization algorithms. In this paper, we expand on previous analyses of MM, due to Fessler and Hero, that allowed the tangent majorants to be constructed in iteration-dependent ways. Also, this paper overcomes an error in one of those earlier analyses. There are three main aspects in which our analysis builds upon previous work. First, our treatment relaxes many assumptions related to the structure of the cost function, feasible set, and tangent majorants. For example, the cost function can be nonconvex and the feasible set for the problem can be any convex set. Second, we propose convergence conditions, based on upper curvature bounds, that can be easier to verify than more standard continuity conditions. Furthermore, these conditions allow for considerable design freedom in the iteration-dependent behavior of the algorithm. Finally, we give an original characterization of the local region of convergence of MM algorithms based on connected (e.g., convex) tangent majorants. For such algorithms, cost function minimizers will locally attract the iterates over larger neighborhoods than typically is guaranteed with other methods. This expanded treatment widens the scope of the MM algorithm designs that can be considered for signal and image processing applications, allows us to verify the convergent behavior of previously published algorithms, and gives a fuller understanding overall of how these algorithms behave. [ABSTRACT FROM AUTHOR]

In this paper, we put forward a novel fusion framework that mixes together label fields instead of observation data as is usually the case. Our framework takes as input two label fields: a quickly estimated and to-be-refined segmentation map and a spatial region map that exhibits the shape of the main objects of the scene. These two label fields are fused together with a global energy function that is minimized with a deterministic iterative conditional mode algorithm. As explained in the paper, the energy function may implement a pure fusion strategy or a fusion-reaction function. In the latter case, a data-related term is used to make the optimization problem well posed. We believe that the conceptual simplicity, the small number of parameters, the use of a simple and fast deterministic optimizer that admits a natural implementation on a parallel architecture are among the main advantages of our approach. Our fusion framework is adapted to various computer vision applications among which are motion segmentation, motion estimation and occlusion detection. [ABSTRACT FROM AUTHOR]

Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field. [ABSTRACT FROM AUTHOR]

Spatially variant apodization (SVA) is a nonlinear sidelobe reduction technique that improves sidelobe level and preserves resolution at the same time. This method implements a bidimensional finite impulse response filter with adaptive taps depending on image information. Some papers that have been previously published analyze SVA at the Nyquist rate or at higher rates focused on strip synthetic aperture radar (SAR). This paper shows that traditional SVA techniques are useless when the sensor operates with a squint angle. The reasons for this behaviour are analyzed, and a new implementation that largely improves the results is presented. The algorithm is applied to simulated SAR images in order to demonstrate the good quality achieved along with efficient computation. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors. [ABSTRACT FROM AUTHOR]

A popular way to restore images comprising edges is to minimize a cost function combining a quadratic data-fidelity term and an edge-preserving (possibly nonconvex) regularization term. Mainly because of the latter term, the calculation of the solution is slow and cumbersome. Half-quadratic (HQ) minimization (multiplicative form) was pioneered by Geman and Reynolds (1992) in order to alleviate the computational task in the context of image reconstruction with nonconvex regularization. By promoting the idea of locally homogeneous image models with a continuous-valued line process, they reformulated the optimization problem in terms of an augmented cost function which is quadratic with respect to the image and separable with respect to the line process, hence the name "half quadratic." Since then, a large amount of papers were dedicated to HQ minimization and important results—including edge-preservation along with convex regularization and convergence—have been obtained. In this paper, we show that HQ minimization (multiplicative form) is equivalent to the most simple and basic method where the gradient of the cost function is linearized at each iteration step. In fact, both methods give exactly the same iterations. Furthermore, connections of HQ minimization with other methods, such as the quasi-Newton method and the generalized Weiszfeld's method, are straightforward. [ABSTRACT FROM AUTHOR]

Lines provide important information in images, and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. This paper presents a novel wide line detector using an isotropic nonlinear filter. Unlike most existing edge and line detectors which use directional derivatives, our proposed wide line detector applies a nonlinear filter to extract a line completely without any derivative. The detector is based on the isotropic responses via circular masks. A general scheme for the analysis of the robustness of the proposed wide line detector is introduced and the dynamic selection of parameters is developed. In addition, this paper investigates the relationship between the size of circular masks and the width of detected lines. A sequence of tests has been conducted on a variety of image samples and our experimental results demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Fourier transform is one of the most important transformations in image processing. A major component of this influence comes from the ability to implement it efficiently on a digital computer. This paper describes a new methodology to perform a fast Fourier transform (FFT). This methodology emerges from considerations of the natural physical constraints imposed by image capture devices (camera/eye). The novel aspects of the specific FFT method described include: 1) a bit-wise reversal re-grouping operation of the conventional FFT is replaced by the use of lossless image rotation and scaling and 2) the usual arithmetic operations of complex multiplication are replaced with integer addition. The significance of the FFT presented in this paper is introduced by extending a discrete and finite image algebra, named Spiral Honeycomb image Algebra (SHIA), to a continuous version, named SHIAC. [ABSTRACT FROM AUTHOR]

The shape-adaptive discrete cosine transform (SA-DCT) transform can be computed on a support of arbitrary shape, but retains a computational complexity comparable to that of the usual separable block-DCT (B-DCT). Despite the near-optimal decorrelation and energy compaction properties, application of the SA-DCT has been rather limited, targeted nearly exclusively to video compression. In this paper, we present a novel approach to image filtering based on the SA-DCT. We use the SA-DCT in conjunction with the Anisotropic Local Polynomial Approximation—Intersection of Confidence Intervals technique, which defines the shape of the transform's support in a pointwise adaptive manner. The thresholded or attenuated SA-DCT coefficients are used to reconstruct a local estimate of the signal within the adaptive-shape support. Since supports corresponding to different points are in general overlapping, the local estimates are averaged together using adaptive weights that depend on the region's statistics. This approach can be used for various image-processing tasks. In this paper, we consider, in particular, image denoising and image deblocking and deringing from block-DCT compression. A special structural constraint in luminance-chrominance space is also proposed to enable an accurate filtering of color images. Simulation experiments show a state-of-the-art quality of the final estimate, both in terms of objective criteria and visual appearance. Thanks to the adaptive support, reconstructed edges are clean, and no unpleasant ringing artifacts are introduced by the fitted transform. [ABSTRACT FROM AUTHOR]

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted (IR) MAD method in a series of iterations places increasing focus on ‘difficult’ observations, here observations whose change status over time is uncertain. The MAD method is based on the established technique of canonical correlation analysis: for the multivariate data acquired at two points in time and covering the same geographical region, we calculate the canonical variates and subtract them from each other. These orthogonal differences contain maximum information on joint change in all variables (spectral bands). The change detected in this fashion is invariant to separate linear (affine) transformations in the originally measured variables at the two points in time, such as 1) changes in gain and offset in the measuring device used to acquire the data, 2) data normalization or calibration schemes that are linear (affine) in the gray values of the original variables, or 3) orthogonal or other affine transformations, such as principal component (PC) or maximum autocorrelation factor (MAF) transformations. The IR-MAD method first calculates ordinary canonical and original MAD variates. In the following iterations we apply different weights to the observations, large weights being assigned to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights being assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original variables. To stabilize solutions to the (IR-)MAD problem, some form of regularization may be needed. This is especially useful for work on hyperspectral data. This paper describes ordinary two-set canonical correlation analysis, the MAD transformation, the iterative extension, and three regularization schemes. A simple case with real Landsat Thematic Mapper (TM) data at one point in time and (partly) constructed data at the other point in time that demonstrates the superiority of the iterative scheme over the original MAD method is shown. Also, examples with SPOT High Resolution Visible data from an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization. [ABSTRACT FROM AUTHOR]

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods. [ABSTRACT FROM AUTHOR]

Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community [1]. This would allow other researchers to easily report comparative results in the future. [ABSTRACT FROM AUTHOR]

In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its applications. The construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a flexible multiscale, multidirection, and shift-invariant image decomposition that can be efficiently implemented via the à trous algorithm. At the core of the proposed scheme is the nonseparable two-channel nonsubsampled filter bank (NSFB). We exploit the less stringent design condition of the NSFB to design filters that lead to a NSCT with better frequency selectivity and regularity when compared to the contourlet transform. We propose a design framework based on the mapping approach, that allows for a fast implementation based on a lifting or ladder structure, and only uses one-dimensional filtering in some cases. In addition, our design ensures that the corresponding frame elements are regular, symmetric, and the frame is close to a tight one. We assess the performance of the NSCT in image denoising and enhancement applications. In both applications the NSCT compares favorably to other existing methods in the literature. [ABSTRACT FROM AUTHOR]

This paper is based upon the observation that, when a bullet is fired, it creates characteristic markings on the cartridge case and projectile. From these markings, over 30 different features can be distinguished, which, in combination, produce a ‘fingerprint’ for a firearm. By analyzing features within such a set of firearm fingerprints, it will be possible to identify not only the type and model of a firearm, but also each and every individual weapon just as effectively as human fingerprint identification. A new analytic system based on the fast Fourier transform for identifying projectile specimens by the line-scan imaging technique is proposed in this paper. This paper develops optical, photonic, and mechanical techniques to map the topography of the surfaces of forensic projectiles for the purpose of identification. Experiments discussed in this paper are performed on images acquired from 16 various weapons. Experimental results show that the proposed system can be used for firearm identification efficiently and precisely through digitizing and analyzing the fired projectiles specimens. [ABSTRACT FROM AUTHOR]

This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images. [ABSTRACT FROM AUTHOR]

Contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their parameters must be specified manually to produce a satisfactory result for a given image. This paper describes a new automatic method for contrast enhancement. The basic procedure is to first group the histogram components of a low-contrast image into a proper number of bins according to a selected criterion, then redistribute these bins uniformly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG not only produces results superior to conventional contrast enhancement techniques, but is also fully automatic in most circumstances, and is applicable to a broad variety of images. An extension of GLG, selective GLG (SGLG), and its variations will be discussed in Part II of this paper. SGLG selectively groups and ungroups histogram components to achieve specific application purposes, such as eliminating background noise, enhancing a specific segment of the histogram, and so on. The extension of GLG to color images will also be discussed in Part II. [ABSTRACT FROM AUTHOR]

Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] × [-2, 2] with constraints for biaxial symmetry, diagonal (or 90° rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-SPCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply. [ABSTRACT FROM AUTHOR]

In this paper, we closely scrutinize the spatial and spectral properties of aperiodic halftoning schemes on rectangular and hexagonal sampling grids. Traditionally, hexagonal sampling grids have been shunned due to their inability to preserve the high-frequency components of blue-noise dither patterns at gray-levels near one-half, but as will be shown, only through the introduction of diagonal correlations between dots can even rectangular sampling grids preserve these frequencies. And by allowing the sampling grid to constrain the placement of dots, a particular algorithm may introduce visual artifacts just as disturbing as excess energy below the principal frequency. If, instead, the algorithm maintains radial symmetry by introducing a minimum degree of clustering, then that algorithm can maintain its grid defiance illusion fundamental to the spirit of the blue-noise model. As such, this paper shows that hexagonal grids are preferrable because they can support gray-levels near one-half with less required clustering of minority pixels and a higher principal frequency. Along with a thorough Fourier analysis of blue-noise dither patterns on both rectangular and hexagonal sampling grids, this paper also demonstrates the construction of a blue-noise dither array for hexagonal grids. EDICS: 4-QUAN Quantization and Halftoning. [ABSTRACT FROM AUTHOR]

Wavelet denoising methods have been proven useful for many one-and two-dimensional problems. Most existing methods can in principle be carried over to three-dimensional problems, such as the denoising of volumetric positron emission tombgraphy (PET) images, but they may not be sufficiently flexible in allowing some regions of an image to be denoised more aggressively than others. In this paper, we propose a semi-local paradigm for wavelet denoising. The semi-local paradigm involves the division of an image into suitable blocks, which are then individually denoised. To denoise the blocks, we use our modification of the generalized cross validation (GCV) technique of Jansen and Bultheel [1] to choose thresholding parameters; we also present risk estimators to guide some of the other choices involved in the implementation. Experltnents with phantom PET images show that the semi-local paradigm provides superior denoising compared to standard application of the GCV technique. An asymptotic analysis demonstrates that, under some regularity conditions, semi-local denoising is asymptotically consistent on the logarithmic scale. The paper concludes with a discussion on the nature of semi-local denoising and some topics for future research. [ABSTRACT FROM AUTHOR]

New breakthroughs in image coding possibly lie in signal decomposition through nonseparable basis functions that can efficiently capture edge characteristics, present in natural images. The work proposed in this paper provides an adaptive way of representing images as a sum of two-dimensional features. It presents a low bit-rate image coding method based on a matching pursuit (MP) expansion, over a dictionary built on anisotropic refinement and rotation of contour-like atoms. This method is shown to provide, at low bit rates, results comparable to the state of the art in image compression, represented here by JPEG2000 and SPIHT, with generally a better visual quality in the MP scheme. The coding artifacts are less annoying than the ringing introduced by wavelets at very low bit rate, due to the smoothing performed by the basis functions used in the MP algorithm. In addition to good compression performances at low bit rates, the new coder has the advantage of producing highly flexible streams. They can easily be decoded at any spatial resolution, different from the original image, and the bitstream can be truncated at any point to match diverse band-width requirements. The spatial adaptivity is shown to be more flexible and less complex than transcoding operations generally applied to state of the art codec bitstreams. Due to both its ability for capturing the most important parts of multidimensional signals, and a flexible stream structure, the image coder proposed in this paper represents an interesting solution for low to medium rate image coding in visual communication applications. [ABSTRACT FROM AUTHOR]

Watershed segmentation has recently become a popular tool for image segmentation. There are two approaches to implementing watershed segmentation: immersion approach and toboggan simulation. Conceptually, the immersion approach can be viewed as an approach that starts from low altitude to high altitude and the toboggan approach as an approach that starts from high altitude to low altitude. The former seemed to be more popular recently (e.g., Vincent and Soille), but the latter had its own supporters (e.g., Mortensen and Barrett). It was not clear whether the two approaches could lead to exactly the same segmentation result and which approach was more efftcient. In this paper, we present two "order-invariant" algorithms for watershed segmentation, one based on the immersion approach and the other on the toboggan approach. By introducing a special RIDGE label to achieve the property of order-invariance, we find that the two conceptually opposite approaches can indeed obtain the same segmentation result. When running on a Pentium-Ill PC, both of our algorithms require only less than 1/30 s for a 256 x 256 image and 1/5 s for a 512 x 512 image, on average. What is more surprising is that the toboggan algorithm, which is less well known in the computer vision community, turns out to run faster than the immersion algorithm for almost all the test images we have used, especially when the image is large, say, 512 x 512 or larger. this paper also gives some explanation as to why the toboggan algorithm can be more efficient in most cases. [ABSTRACT FROM AUTHOR]

This paper presents a generalized Bayesian frame-work for relevance feedback in content-based image retrieval. The proposed feedback technique is based on the Bayesian learning method and incorporates a time-varying user model into the formulation. We define the user model with two terms: a target query and a user conception. The target query is aimed to learn the common features from relevant images so as to specify the user's ideal query. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion. Therefore, at each feedback step, the learning process updates not only the target distribution, but also the target query and the matching criterion. In addition, another objective of this paper is to conduct the relevance feedback on images represented in region level. We formulate the matching criterion using a weighting scheme and proposed a region clustering technique to determine the region correspondence between relevant images. With the proposed region clustering technique, we derive a representation in region level to characterize the target query. Experiments demonstrate that the proposed method combined with time-varying user model indeed achieves satisfactory results and our proposed region-based techniques further improve the retrieval accuracy. [ABSTRACT FROM AUTHOR]

The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years, several pioneering works suggested such a separation be based on variational fore mutation and others using independent component analysis and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms. The method combines the basis pursuit denoising (BPDN) algorithm and the total-variation (TV) regularization scheme. The basic idea presented in this paper is the use of two appropriate dictionaries, one for the representation of textures and the other for the natural scene parts assumed to be piecewise smooth. Both dictionaries are chosen such that they lead to sparse representations over one type of image-content (either texture or piecewise smooth). The use of the BPDN with the two amalgamed dictionaries leads to the desired separation, along with noise removal as a by-product. As the need to choose proper dictionaries is generally hard, a TV regularization is employed to better direct the separation process and reduce ringing artifacts. We present a highly efficient numerical scheme to solve the combined optimization problem posed by our model and to show several experimental results that validate the algorithm's performance. [ABSTRACT FROM AUTHOR]

Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results. [ABSTRACT FROM AUTHOR]

This paper describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs. We introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization. The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (e.g., at least four-fold) and arbitrary rotations. Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain. In this paper, we demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain. We achieve subpixel accuracy through the use of nonlinear least squares optimization. The registration process yields the eight parameters of the perspective transformation that best aligns the two input images. Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to represent an image by "verge points," which are defined as high-curvature points on the image surface. This representation offers a compact and reversible way to preserve the essence of the original image. Various applications, such as compression, edge detection, image enhancement, and image editing, can be achieved based on this representation. In this paper, the whole procedure for verge point representation is presented. Based on these verge points, image reconstruction can be easily achieved via iterative linear interpolation. These extracted verge points with compatible properties are further linked into verge curves to offer more compact representation. Progressive representation is also developed based on a multiscale extraction scheme. Some potential applications are then presented to demonstrate the versatility of this representation. [ABSTRACT FROM AUTHOR]

This paper introduces an improved structure for directional filter banks (DFBs) that preserves the visual information in the subband domain. The new structure achieves this out- come while preserving both the efficient polyphase implementation and the exact reconstruction property. The paper outlines a step-by-step framework in which to examine the DFB, and within this framework discusses how, through the insertion of post-sampling matrices, visual distortions can be removed. In addition to the efficient tree structure, attention is given to the form and design of efficient linear phase filters. Most notably, linear phase hR prototype filters are presented, together with the design details. These filters can enable the DFB to have more than a three-fold improvement in complexity reduction over quadrature mirror filters (QMFs). [ABSTRACT FROM AUTHOR]

This paper advances a new framework for chromatic filtering of color images. The chromatic content of a color image is encoded in the CIE u' v' chromaticity coordinates whereas the achromatic content is encoded as CIE Y tristimulus value. Within the u' v' chromaticity diagram, colors are added according to the well-known center of gravity law of additive color mixtures, which is generalized here into a nonlinear filtering scheme for processing the two chromatic signals u' and v'. The achromatic channel Y can be processed with traditional filtering schemes, either linear or nonlinear, depending on the specific task at hand. The most interesting characteristics of the new filtering scheme are: 1) the elimination of color smearing effects along edges between bright and dark areas; 2) the possibility of processing chromatic components in a noniterative fashion through linear convolution operations; and 3) the consequent amenability to computationailly efficient implementations with fast Fourier transform. The paper includes several examples with both synthetic and real images where the performance of the new filtering method is compared with that of other color image processing algorithms. [ABSTRACT FROM AUTHOR]

Image sequence restoration has been steadily gaining in importance with the increasing prevalence of visual digital media. The demand for content increases the pressure on archives to automate their restoration activities for preservation of the cultural heritage that they hold. There are many defects that affect archived visual material and one central issue is that of Dirt and Sparkle, or "Blotches." Research in archive restoration has been conducted for more than a decade and this paper places that material in context to highlight the advances made during that time. The paper also presents a new and simpler Bayesian framework that achieves joint processing of noise, missing data, and occlusion. [ABSTRACT FROM AUTHOR]

A widespread use of three-dimensional (3-D) models in cultural heritage application requires low cost equipment and technically simple modeling procedures. In this context, methods for automatic 3-D modeling of textured objects will play a central role. Such methods need fully automatic techniques for 3-D views registration and for the removal of texture artifacts. This paper proposes a contribution in this direction based on image processing approaches. The proposed procedure is very robust and simple. It does not require special equipment or skill in order to make textured 3-D models. The results of this paper, originally conceived to address the costs issues of cultural heritage modeling, can be profitably exploited also in other modeling applications. [ABSTRACT FROM AUTHOR]

The large volumes of artistic visual data available to museums, art galleries, and online collections motivate the need for effective means to retrieve relevant information from such repositories. This paper proposes a decision making framework for content-based retrieval of art images based on a combination of low- level features. Traditionally, the similarity among two images has been calculated as a weighted distance between two feature vectors. This approach, however, may not be mathematically and computationally appropriate and does not provide enough flexibility in modeling user queries. This paper proposes a framework that generalizes a wide set of previous approaches to similarity calculation Including the weighted distance approach. In this framework, Image similarities are obtained through a decision making process based on low-level feature distances using fuzzy theory. The analysis and results of this paper indicate that the aggregation technique presented here provides an effective, general, and flexible tool for similarity calculation based on the combination of Individual descriptors and features. [ABSTRACT FROM AUTHOR]

Wavelet shrinkage estimators, in general, make the additive normal noise assumption and disregard the nonlinear nature of contamination. In this paper, we develop Bayesian wavelet shrinkage estimators (based on the power transformations in the Linear model) to accommodate a broad class of noise models in image processing applications. We intend to admit under one roof, the widespread additive model, the product models common in imaging (such as in Synthetic Aperture Radar (SAR) imagery), as well as noise that may exist amid these two extremes. Tactful prior elicitation in this model, such as the simultaneous assignment of mixture priors for wavelet coefficients and the transformation, imparts flexibility and ample insight into the underlying structure. The model permits estimation with unknown noise structure for reasonably unimodal and well-behaved (on the tails) distributions, wherein it can outperform common shrinkage estimators. Extensions with multiple transformations and Markov Random Field priors are also considered, for adaptation to local variations in contamination. Modern Markov chain Monte Carlo (MCMC) Bayesian computation has been used for simulations and several examples are reported in the paper. [ABSTRACT FROM AUTHOR]

In this paper, the Cramér-Rao Lower Bound (CRLB) of image registration error using an isotropic fiducial mark is derived. The derived CRLB is a functional of the intensity profile of the fiducial mark. Following the development of the CRLB, a new method for designing an isotropic fiducial mark, suitable for digital image registration is presented. A parameterization method of the fiducial intensity profile is introduced which guarantees no aliasing effect when the fiducial mark is digitized with proper sampling rate. A method for computing the fiducial intensity profile, based on minimization of the CRLB registration error, and subject to certain practical constraints is developed. For imaging systems with significant low-pass effect, it is proposed in this paper to pre-emphasize the high frequency components of the fiducial mark by converting the designed gray-scale fiducial marks into binary fiducial marks. Experimental results show that the designed fiducial mark can provide very accurate registration results and that the registration accuracy is independent of its location. [ABSTRACT FROM AUTHOR]

An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these functions separately with structure and texture filling-in algorithms. The first function used in the decomposition is of bounded variation, representing the underlying image structure, while the second function captures the texture and possible noise. The region of missing information in the bounded variation image is reconstructed using image in-painting algorithms, while the same region in the texture image is filled-in with texture synthesis techniques. The original image is then reconstructed adding back these two sub-images. The novel contribution of this paper is then in the combination of these three previously developed components, image decomposition with in-painting and texture synthesis, which permits the simultaneous use of filling-in algorithms that are suited for different image characteristics. Examples on real images show the advantages of this proposed approach. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to pattern recognition in Synthetic Aperture Radar (SAR) images. A visual analysis of the images provided by NASA's Magellan mission to Venus has revealed a number of zones showing polygonal-shaped faults on the surface of the planet. The goal of this paper is to provide a method to automate the identification of such zones. The high level of noise in SAR images and its multiplicative nature make automated image analysis difficult and conventional edge detectors like those based on gradient images, inefficient. We present a scheme based on an improved watershed algorithm and a two-scale analysis. The method described extracts potential edges in the SAR image, analyzes the patterns obtained, and decides whether or not the image contains a "polygon area." This scheme can also be applied to other SAR or visual images, for instance in observation of Mars and Jupiter's satellite Europa. [ABSTRACT FROM AUTHOR]

In this paper, a new orthogonal wavelet representation of multivalued images is presented. The idea for this representation is based on the concept of maximal gradient of multivalued images. This concept is generalized from gradients toward linear vector operators in the image plane with equal components along rows and columns. Using this generalization, the pyramidal dyadic wavelet transform algorithm using quadrature mirror filters is modified to be applied to multivalued images. This results in a representation of a single image, containing multiscale detail information from all component images involved. This representation leads to multiple applications ranging from multispectral image fusion to color and multivalued image enhancement, denoising and segmentation. In this paper, the representation is applied for fusion of images. More in particular, we will introduce a scheme to merge high spatial resolution greylevel images with low spatial resolution multivalued images to improve spatial resolution of the latter while preserving spectral resolution. Two applications are studied: demosaicing of color images and merging of multispectral remote sensing images. [ABSTRACT FROM AUTHOR]

This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images. [ABSTRACT FROM AUTHOR]

This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a constrained piecewise linear one. The optimal fitting of this approximation to comparagram data is then used in a re-parameterized version of the camera's comparametric function to estimate the exposure difference between images. Doing this allows the inherently nonlinear problem of joint domain and range registration to be performed using a computationally attractive least squares formalism. The paper first presents the range registration process and then describes the strategy for performing the joint registration. The models used allow for the pair-wise registration of images taken from a camera that can automatically adjust its exposure as well as tilt, pan, rotate and zoom about its optical center. Results concerning the joint registration as well as range-only registration are provided to demonstrate the method's effectiveness. [ABSTRACT FROM AUTHOR]

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. To date, a wealth of approaches to motion segmentation have been proposed. Many of them suffer from the local nature of the models used. Global models, such as those based on Markov random fields, perform, in general, better. In this paper, we propose a new approach to motion segmentation that is based on a global model. The novelty of the approach is twofold. First, inspired by recent work of other researchers we formulate the problem as that of region competition, but we solve it using the level set methodology. The key features of a level set representation, as compared to active contours, often used in this context, are its ability to handle variations in the topology of the segmentation and its numerical stability. The second novelty of the paper is the formulation in which, unlike in many other motion segmentation algorithms, we do not use intensity boundaries as an accessory; the segmentation is purely based on motion. This permits accurate estimation of motion boundaries of an object even when its intensity boundaries are hardly visible. Since occasionally intensity boundaries may prove beneficial, we extend the formulation to account for the coincidence of motion and intensity boundaries. In addition, we generalize the approach to multiple motions. We discuss possible discretizations of the evolution (PDE) equations and we give details of an initialization scheme so that the results could be duplicated. We show numerous experimental results for various formulations on natural images with either synthetic or natural motion. [ABSTRACT FROM AUTHOR]

In this paper, we examine the problem of locating an object in an image when size and rotation are unknown. Previous work has shown that with known geometric parameters, an image restoration method can be useful by estimating a delta function at the object location. When the geometric parameters are unknown, this method becomes impractical because the likelihood surface to be minimized across size and rotation has numerous local minima and areas of zero gradient. In this paper, we propose a new approach where a smooth approximation of the template is used to minimize a well-behaved likelihood surface. A coarse-to-fine approximation of the original template using a diffusion-like equation is used to create a library of templates. Using this library, we can successively perform minimizations which are locally well-behaved. As detail is added to the template, the likelihood surface gains local minima, but previous estimates place us within a wellbehaved "bowl" around the global minimum, leading to an accurate estimate. Numerical experiments are shown which verify the value of this approach for a wide range of values of the geometric parameters. [ABSTRACT FROM AUTHOR]

Motion characteristics of human actions can be represented by the position variation of skeleton joints. Traditional approaches generally extract the spatial–temporal representation of the skeleton sequences with well-designed hand-crafted features. In this paper, in order to recognize actions according to the relative motion between the limbs and the trunk, we propose an end-to-end hierarchical RNN for skeleton-based action recognition. We divide human skeleton into five main parts in terms of the human physical structure, and then feed them to five independent subnets for local feature extraction. After the following hierarchical feature fusion and extraction from local to global, dimensions of the final temporal dynamics representations are reduced to the same number of action categories in the corresponding data set through a single-layer perceptron. In addition, the output of the perceptron is temporally accumulated as the input of a softmax layer for classification. Random scale and rotation transformations are employed to improve the robustness during training. We compare with five other deep RNN variants derived from our model in order to verify the effectiveness of the proposed network. In addition, we compare with several other methods on motion capture and Kinect data sets. Furthermore, we evaluate the robustness of our model trained with random scale and rotation transformations for a multiview problem. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency. [ABSTRACT FROM PUBLISHER]

Deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependence among different image regions. However, such dependence is very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information in sequential data, and they only require a limited number of network parameters. Thus, we proposed the hierarchical RNNs (HRNNs) to encode the contextual dependence in image representation. In HRNNs, each RNN layer focuses on modeling spatial dependence among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two RNN models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost and 2) hierarchical long-short term memory recurrent network, which performs better than HSRN with the price of higher computational cost. In this paper, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical RNNs (C-HRNNs) for image classification. C-HRNNs not only utilize the discriminative representation power of CNNs, but also utilize the contextual dependence learning ability of our HRNNs. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve the state-of-the-art results on Places 205, SUN 397, and MIT indoor, and the competitive results on ILSVRC 2012. [ABSTRACT FROM PUBLISHER]

Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method. [ABSTRACT FROM AUTHOR]

This paper investigates the use of a qualitative inverse scattering method known as the linear sampling method (LSM) for imaging underwater scenes using limited aperture receiver configurations. The LSM is based on solving a set of unstable integral equations known as the far-field equations and whose stability breaks down even further for under-sampled observation aperture data. Based on the results of a recent study concerning multi-frequency LSM imaging, we propose an iterative inversion method that is founded upon a compressive sensing framework. In particular, we leverage multi-frequency diversity in the data by imposing a partial frequency variation prior on the solution which we show is justified when the frequency bandwidth is sampled finely enough. We formulate an alternating direction method of multiplier approach to minimize the proposed cost function. Proof of concept is established through numerically generated data as well as experimental acoustic measurements taken in a shallow pool facility at the U.S Naval Research Laboratory. [ABSTRACT FROM AUTHOR]

During recent years, blind image quality assessment (BIQA) has been intensively studied with different machine learning tools. Existing BIQA metrics, however, do not design for stereoscopic images. We believe this problem can be resolved by separating 3D images and capturing the essential attributes of images via deep neural network. In this paper, we propose a blind deep quality evaluator (DQE) for stereoscopic images (denoted by 3D-DQE) based on monocular and binocular interactions. The key technical steps in the proposed 3D-DQE are to train two separate 2D deep neural networks (2D-DNNs) from 2D monocular images and cyclopean images to model the process of monocular and binocular quality predictions, and combine the measured 2D monocular and cyclopean quality scores using different weighting schemes. Experimental results on four public 3D image quality assessment databases demonstrate that in comparison with the existing methods, the devised algorithm achieves high consistent alignment with subjective assessment. [ABSTRACT FROM AUTHOR]

Face images convey rich information which can be perceived as a superposition of low-complexity components associated with attributes, such as facial identity, expressions, and activation of facial action units (AUs). For instance, low-rank components characterizing neutral facial images are associated with identity, while sparse components capturing non-rigid deformations occurring in certain face regions reveal expressions and AU activations. In this paper, the discriminant incoherent component analysis (DICA) is proposed in order to extract low-complexity components, corresponding to facial attributes, which are mutually incoherent among different classes (e.g., identity, expression, and AU activation) from training data, even in the presence of gross sparse errors. To this end, a suitable optimization problem, involving the minimization of nuclear-and \ell 1 -norm, is solved. Having found an ensemble of class-specific incoherent components by the DICA, an unseen (test) image is expressed as a group-sparse linear combination of these components, where the non-zero coefficients reveal the class(es) of the respective facial attribute(s) that it belongs to. The performance of the DICA is experimentally assessed on both synthetic and real-world data. Emphasis is placed on face analysis tasks, namely, joint face and expression recognition, face recognition under varying percentages of training data corruption, subject-independent expression recognition, and AU detection by conducting experiments on four data sets. The proposed method outperforms all the methods that are compared with all the tasks and experimental settings. [ABSTRACT FROM AUTHOR]