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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]

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]

Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed 11MM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a new method for generating different texture images is presented. This method involves a simple transform from a certain one-dimensional (1-D) signal to an expected two-dimensional (2-D) image. Unlike traditional methods, the input signal is generated by a simple 1-D function in our work instead of a sample texture. We first transform the 1-D input signal into frequency domain using fast Fourier transform. Based on the sufficient analysis in 2-D discrete cosine transform (DCT) domain, where each of the coefficients expresses a texture feature in a certain direction, the 2-D pseudo-DCT coefficients are then constructed by appropriately rearranging the Fourier coefficients in terms of their frequency components. Finally, the corresponding texture image can be produced by 2-D inverse DCT algorithm. We applied the proposed method to generate several stochastic textures (i.e., cloud, illumination, and sand), and several structural texture images. Experimental results indicate the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the sequential-readout structure of complementary metal-oxide semiconductor image sensor array, each scanline of the acquired image is exposed at a different time, resulting in the so-called electronic rolling shutter that induces geometric image distortion when the object or the video camera moves during image capture. In this paper, we propose an image processing technique using a planar motion model to address the problem. Unlike previous methods that involve complex 3-D feature correspondences, a simple approach to the analysis of inter- and intraframe distortions is presented. The high-resolution velocity estimates used for restoring the image are obtained by global motion estimation, Bézier curve fitting, and local motion estimation without resort to correspondence identification. Experimental results demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters. [ABSTRACT FROM AUTHOR]

This paper studies image deblurring problems using a total variation-based model, with a non-negativity constraint. The addition of the non-negativity constraint improves the quality of the solutions, but makes the solution process a difficult one. The contribution of our work is a fast and robust numerical algorithm to solve the non-negatively constrained problem. To overcome the nondifferentiability of the total variation norm, we formulate the constrained deblurring problem as a primal-dual program which is a variant of the formulation proposed by Chan, Golub, and Mulet for unconstrained problems. Here, dual refers to a combination of the Lagrangian and Fenchel duals. To solve the constrained primal-dual program, we use a semi-smooth Newton's method. We exploit the relationship between the semi-smooth Newton's method and the primal-dual active set method to achieve considerable simplification of the computations. The main advantages of our proposed scheme are: no parameters need significant adjustment, a standard inverse preconditioner works very well, quadratic rate of local convergence (theoretical and numerical), numerical evidence of global convergence, and high accuracy of solving the optimality system. The scheme shows robustness of performance over a wide range of parameters. A comprehensive set of numerical comparisons are provided against other methods to solve the same problem which show the speed and accuracy advantages of our scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for an optimal reduction of object description for object matching purposes. Our aim is to decrease the computation needs by considering simplified objects, thus reducing the number of pixels involved in the matching process. We develop the appropriate theoretical background based on centroidal Voronoi tessellations. Its use within the chamfer matching framework is also discussed. We present experimental results regarding the performance of this approach for 2-D contour and region-like object matching. As a special case, we investigate how the snake based representation of target objects can be employed in chamfer matching. The experimental results concern the use of object part matching for recognizing humans and show how the proposed simplification leads to valid replacements of the original templates. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing. [ABSTRACT FROM AUTHOR]

Even though numerous algorithms exist for estimating the three-dimensional (3-D) structure of a scene from its video, the solutions obtained are often of unacceptable quality. To overcome some of the deficiencies, many application systems rely on processing more data than necessary, thus raising the question: how is the accuracy of the solution related to the amount of data processed by the algorithm? Can we automatically recognize situations where the quality of the data is so bad that even a large number of additional observations will not yield the desired solution? Previous efforts to answer this question have used statistical measures like second order moments. They are useful if the estimate of the structure is unbiased and the higher order statistical effects are negligible, which is often not the case. This paper introduces an alternative information-theoretic criterion for evaluating the quality of a 3-D reconstruction. The accuracy of the reconstruction is judged by considering the change in mutual information (MI) (termed as the incremental MI) between a scene and its reconstructions. An example of 3-D reconstruction from a video sequence using optical flow equations and known noise distribution is considered and it is shown how the MI can be computed from first principles. We present simulations on both synthetic and real data to demonstrate the effectiveness of the proposed criterion. [ABSTRACT FROM AUTHOR]

This paper presents an interactive algorithm for segmentation of natural images. The task is formulated as a problem of spline regression, in which the spline is derived in Sobolev space and has a form of a combination of linear and Green's functions. Besides its nonlinear representation capability, one advantage of this spline in usage is that, once it has been constructed, no parameters need to be tuned to data. We define this spline on the user specified foreground and background pixels, and solve its parameters (the combination coefficients of functions) from a group of linear equations. To speed up spline construction, K-means clustering algorithm is employed to cluster the user specified pixels. By taking the cluster centers as representatives, this spline can be easily constructed. The foreground object is finally cut out from its background via spline interpolation. The computational complexity of the proposed algorithm is linear in the number of the pixels to be segmented. Experiments on diverse natural images, with comparison to existing algorithms, illustrate the validity of our method. [ABSTRACT FROM AUTHOR]

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Determination of optimal sensor configuration is an important issue in many remote imaging modalities, such as tomographic and interferometric imaging. In this paper, a statistical optimality criterion is defined and a search is performed over the space of candidate sensor locations to determine the configuration that optimizes the criterion over all candidates. To make the search process computationally feasible, a modified version of a previously proposed suboptimal backward greedy algorithm is used. A statistical framework is developed which allows for inclusion of several widely used image constraints. Computational complexity of the proposed algorithm is discussed and a fast implementation is described. Furthermore, upper bounds on the sum of the squared error of the proposed algorithm are derived. Connections of the method to the deterministic backward greedy algorithm for the subset selection problem are presented, and two application examples are described. Five compelling optimality criteria are considered, and their performance is investigated through numerical experiments for a tomographic imaging scenario. In all cases, it is verified that the configuration designed by the proposed algorithm performs better than wisely chosen alternatives. [ABSTRACT FROM AUTHOR]

This paper introduces a new class of fractional-order anisotropic diffusion equations for noise removal. These equations are Euler-Lagrange equations of a cost functional which is an increasing function of the absolute value of the fractional derivative of the image intensity function, so the proposed equations can be seen as generalizations of second-order and fourth-order anisotropic diffusion equations. We use the discrete Fourier transform to implement the numerical algorithm and give an iterative scheme in the frequency domain. It is one important aspect of the algorithm that it considers the input image as a periodic image. To overcome this problem, we use a folded algorithm by extending the image symmetrically about its borders. Finally, we list various numerical results on denoising real images. Experiments show that the proposed fractional-order anisotropic diffusion equations yield good visual effects and better signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Accurate estimation of noise and signal power is of fundamental interest in a wide variety of vision applications as it is critical to thresholding and decision processes. This paper proposes two methods for the estimation of nonstationary noise based upon models of image structure which locally separate signal from noise. The resulting algorithms are noniterative and thereby fast. The accuracy of the proposed and existing methods is compared, first separately and then in application to two common image processing tasks: edge and corner detection. It is demonstrated that the proposed model can be used to improve the stability of both, in the presence of contrast change and nonstationary noise. [ABSTRACT FROM AUTHOR]

The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm. [ABSTRACT FROM AUTHOR]

Recently, several efficient context-based arithmetic coding algorithms have been developed successfully for lossless compression of error-diffused images. In this paper, we first present a novel block- and texture-based approach to train the multiple-template according to the most representative texture features. Based on the trained multiple template, we next present an efficient texture- and multiple-template-based (TM-based) algorithm for lossless compression of error-diffused images. In our proposed TM-based algorithm, the input image is divided into many blocks and for each block, the best template is adaptively selected from the multiple-template based on the texture feature of that block. Under 20 testing error-diffused images and the personal computer with Intel Celeron 2.8-GHz CPU, experimental results demonstrate that with a little encoding time degradation, 0.365 s (0.901 s) on average, the compression improvement ratio of our proposed TM-based algorithm over the joint bilevel image group (JBIG) standard [over the previous block arithmetic coding for image compression (BACIC) algorithm proposed by Reavy and Boncelet is 24%] (19.4%). Under the same condition, the compression improvement ratio of our proposed algorithm over the previous algorithm by Lee and Park is 17.6% and still only has a little encoding time degradation (0.775 s on average). In addition, the encoding time required in the previous free tree-based algorithm is 109.131 s on average while our proposed algorithm takes 0.995 s; the average compression ratio of our proposed TM-based algorithm, 1.60, is quite competitive to that of the free tree-based algorithm, 1.62. [ABSTRACT FROM AUTHOR]

Most subsampled filter banks lack the feature of translation invariance, which is an important characteristic in denoising applications. In this paper, we study and develop new methods to convert a general multichannel, multidimensional filter bank to a corresponding translation-invariant (TI) framework. In particular, we propose a generalized algorithme à trous, which is an extension of the algorithme a trous introduced for 1-D wavelet transforms. Using the proposed algorithm, as well as incorporating modified versions of directional filter banks, we construct the TI contourlet transform (TICT). To reduce the high redundancy and complexity of the TICT, we also introduce semi-translation-invariant contourlet transform (STICT). Then, we employ an adapted bivariate shrinkage scheme to the STICT to achieve an efficient image denoising approach. Our experimental results demonstrate the benefits and potential of the proposed denoising approach. Complexity analysis and efficient realization of the proposed TI schemes are also presented. [ABSTRACT FROM AUTHOR]

Gabor filters are widely applied in image analysis and computer vision applications. This paper describes a fast algorithm for isotropic complex Gabor filtering that outperforms existing implementations. The main computational improvement arises from the decomposition of Gabor filtering into more efficient Gaussian filtering and sinusoidal modulations. Appropriate filter initial conditions are derived to avoid boundary transients, without requiring explicit image border extension. Our proposal reduces up to 39% the number of required operations with respect to state-of-the-art approaches. A full C++ implementation of the method is publicly available. [ABSTRACT FROM AUTHOR]

Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of low- quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Cramér-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration. [ABSTRACT FROM AUTHOR]

Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise. [ABSTRACT FROM AUTHOR]

The inverse halftoning algorithm is used to reconstruct a gray image from an input halftone image. Based on the recently published lookup table (LUT) technique, this paper presents a novel edge-based LUT method for inverse halftoning which improves the quality of the reconstructed gray image. The proposed method first uses the LUT-based inverse halftoning method as a Preprocessing step to transform the given halftone image to a base gray image, and then the edges are extracted and classified from the base gray image. According to these classified edges, a novel edge-based LUT is built up to reconstruct the gray image. Based on a set of 30 real training images with both low- and high-frequency contents, experimental results demonstrated that the proposed method achieves a better image quality when compared to the currently published two methods, by Chang et al. and Meşe and Vaidyanathan. [ABSTRACT FROM AUTHOR]

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM. [ABSTRACT FROM AUTHOR]

A common problem shared by several leading morphological shape representation algorithms is that there is much overlapping among the representative disks of the same size. A shape component represented by a group of connected disk centers sometimes uses many heavily overlapping representative disks to represent a relatively simple shape part. A shape component may also contain a large number of representative disks that form a complicated structure. In this paper, we introduce a generalized discrete morphological skeleton transform that uses eight structuring elements to generate skeleton subsets so that no two skeletal points from the same skeleton subset will be adjacent to each other. Each skeletal point will represent a shape part that is in general an octagon with four pairs of parallel opposing sides. The number of representative points needed to represent a given shape is significantly lower than that in the standard skeleton transform. A collection of shape components needed to build a structural representation is easily derived from the generalized skeleton transform. Each shape component covers a significant area of the given shape and severe overlapping is avoided. The given shape can also be accurately approximated using a small number of shape components. [ABSTRACT FROM AUTHOR]

We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the s-expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images. [ABSTRACT FROM AUTHOR]

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate. [ABSTRACT FROM AUTHOR]

We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms. [ABSTRACT FROM AUTHOR]

We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability. The target model is updated according to the similarity between the initial and current models, and this makes the tracker more robust. The proposed algorithm has been compared with other trackers using challenging image sequences, and it provides better performance. [ABSTRACT FROM AUTHOR]

Quality criteria for image coding are often based on mean square error. However, this is not always a relevant measure of visual quality at low bit rates. Here, we investigate the properties of a distortion measure based on the conditional differential entropy of the input signal given its quantized value. The proposed measure appears to be a correct representation of the amount of information lost by quantization. An adaptive bit allocation algorithm is proposed in order to take advantage of this criterion. Experimental results illustrate the behavior of the proposed distortion measure and exhibit interesting visual properties for low bit-rate subband image coding. [ABSTRACT FROM AUTHOR]

A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented. [ABSTRACT FROM AUTHOR]

We propose a new approach to image denoising, based on the image-domain minimization of an estimate of the mean squared error-Stein's unbiased risk estimate (SURE). Unlike most existing denoising algorithms, using the SURE makes it needless to hypothesize a statistical model for the noiseless image. A key point of our approach is that, although the (nonlinear) processing is performed in a transformed domain—typically, an undecimated discrete wavelet transform, but we also address nonorthonormal transforms—this minimization is performed in the image domain. Indeed, we demonstrate that, when the transform is a ‘tight’ frame (an undecimated wavelet transform using orthonormal filters), separate subband minimization yields substantially worse results. In order for our approach to be viable, we add another principle, that the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Armed with the SURE and LET principles, we show that a denoising algorithm merely amounts to solving a linear system of equations which is obviously fast and efficient. Quite remarkably, the very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the undecimated Haar wavelet coefficients show that the SURE-LET principle has a huge potential. [ABSTRACT FROM AUTHOR]

The human visual system is able to perceive colors as approximately constant. This ability is known as color constancy. In contrast, the colors measured by a sensor vary with the type of illuminant used. Color constancy is very important for digital photography and automatic color-based object recognition. In digital photography, this ability is known under the name automatic white balance. A number of algorithms have been developed for color constancy. We review two well-known color constancy algorithms, the gray world assumption and the Retinex algorithm and show how a color constancy algorithm may be integrated into the JPEG2000 framework. Since computer images are usually stored in compressed form anyway, little overhead is required to add color constancy into the processing pipeline. [ABSTRACT FROM AUTHOR]

The median filter is one of the basic building blocks in many image processing situations. However, its use has long been hampered by its algorithmic complexity of O(r) in the kernel radius. With the trend toward larger images and proportionally larger filter kernels, the need for a more efficient median filtering algorithm becomes pressing. In this correspondence, a new, simple, yet much faster, algorithm exhibiting O(1) runtime complexity is described and analyzed. It is compared and bench- marked against previous algorithms. Extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented, as well. [ABSTRACT FROM AUTHOR]

We introduce an optimal topdown algorithm for computing and representing level line trees of 2-D intensity images. The running time of the algorithm is O(n + t), where n is the size of the input image and t is the total length of all level lines. The properties of level line trees are also investigated. The efficiency of the algorithm is illustrated by experiments on images of different sizes and scenes. [ABSTRACT FROM AUTHOR]

An image coding algorithm, Progressive Resolution Coding (PROGRES), for a high-speed resolution scalable decoding is proposed. The algorithm is designed based on a prediction of the decaying dynamic ranges of wavelet subbands. Most interestingly, because of the syntactic relationship between two coders, the proposed method costs an amount of bits very similar to that used by uncoded (i.e., not entropy coded) SPIHT. The algorithm bypasses bit-plane coding and complicated list processing of SPIHT in order to obtain a considerable speed improvement, giving up quality scalability, but without compromising coding efficiency. Since each tree of coefficients is separately coded, where the root of the tree corresponds to the coefficient in LL subband, the algorithm is easily extensible to random access decoding. The algorithm is designed and implemented for both 2-D and 3-D wavelet subbands. Experiments show that the decoding speeds of the proposed coding model are four times and nine times faster than uncoded 2-D-SPIHT and 3-D-SPEHT, respectively, with almost the same decoded quality. The higher decoding speed gain in a larger image source validates the suitability of the proposed method to very large scale image encoding and decoding. In the Appendix, we explain the syntactic relationship of the proposed PROGRES method to uncoded SPIHT, and demonstrate that, in the lossless case, the bits sent to the code- stream for each algorithm are identical, except that they are sent in different order. [ABSTRACT FROM AUTHOR]

We describe a recursive algorithm for anisotropic 2-D Gaussian filtering, based on separating the filter into the cascade of three, rather two, 1-D filters. The filters operate along axes obtained by integer horizontal and/or vertical pixel shifts. This eliminates interpolation, which removes spatial inhomogeneity in the filter, and produces more elliptically shaped kernels. It also results in a more regular filter structure, which facilitates implementation in DSP chips. Finally, it improves matching between filters with the same eccentricity and width, but different orientations. Our analysis and experiments indicate that the computational complexity is similar to an algorithm that operates along two axes (<11 ms for a 512 × 512 image using a 3.2-CHz Pentium 4 PC). On the other hand, given a limited set of basis filter axes, there is an orientation dependent lower bound on the achievable aspect ratios. [ABSTRACT FROM AUTHOR]

In general, online signature capturing devices pro- vide outputs in the form of shape and velocity signals. In the past, strokes have been extracted while tracking velocity signal minimas. However, the resulting strokes are larger and complicated in shape and thus make the subsequent job of generating a discriminative template difficult. We propose a new stroke-based algorithm that splits velocity signal into various bands. Based on these bands, strokes are extracted which are smaller and more simpler in nature. Training of our proposed system revealed that low- and high-velocity bands of the signal are unstable, whereas the medium-velocity band can be used for discrimination purposes. Euclidean distances of strokes extracted on the basis of medium velocity band are used for verification purpose. The experiments conducted show improvement in discriminative capability of the proposed stroke-based system. [ABSTRACT FROM AUTHOR]

We propose an image hashing paradigm using visually significant feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to introduce randomness, which, in turn, reduces vulnerability to adversarial attacks. The proposed hash algorithm withstands standard benchmark (e.g., Stirmark) attacks, including compression, geometric distortions of scaling and small-angle rotation, and common signal-processing operations. Content changing (malicious) manipulations of image data are also accurately detected. Detailed statistical analysis in the form of receiver operating characteristic (ROC) curves is presented and reveals the success of the proposed scheme in achieving perceptual robustness while avoiding misclassification. [ABSTRACT FROM AUTHOR]

Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts. [ABSTRACT FROM AUTHOR]

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin. [ABSTRACT FROM AUTHOR]

A new image data-hiding technique is proposed. The proposed approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values, one can represent the hidden information properly. The proposed algorithm performs the modification progressively to ensure successful data extraction without the need for the original image at the receiver side. Two techniques are also presented for correcting scaling and rotation attacks. The first approach is an exhaustive search in nature, which is based on a training sequence that is inserted as part of the hidden information. The second approach uses wavelet maxima as image semantics for rotation and scaling estimation. Both algorithms have proved to be effective in correcting rotation and scaling distortion. [ABSTRACT FROM AUTHOR]

Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation. [ABSTRACT FROM AUTHOR]

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications. [ABSTRACT FROM AUTHOR]

Abstract-In image processing literature, thus far, researchers have assumed the perturbation in the data to be white (or uncorrelated) having a covariance matrix σ²I, i.e., assumption of equal variance for all the data samples and that no correlation exists between the data samples. However, there have been very few attempts to estimate noise characteristics under the assumption that there is a correlation between data samples. In this work, we propose a new and a novel approach for the simultaneous Bayesian estimation of the unknown colored or correlated noise (population) covariance matrix and the hyperparameters of the covariance model using the well-knownfacet model. We also estimate the facet model coefficients. We use the facet model because of its simple, yet elegant, mathematical formulation. We use the generalized inverted Wishart density as the prior model for the noise covariance matrix. We place a structure on the covariance matrix using the parameters of a correlation filter. These hyperparameters are estimated by a new extension of the expectation-maximization algorithm called the generalized constrained expectation maximization algorithm that we developed. [ABSTRACT FROM AUTHOR]

We consider a joint source-channel coding system that protects an embedded bitstream using a finite family of channel codes with error detection and error correction capability. The performance of this system may be measured by the expected distortion or by the expected number of correctly decoded source bits. Whereas a rate-based optimal solution can be found in linear time, the computation of a distortion-based optimal solution is prohibitive. Under the assumption of the convexity of the operational distortion-rate function of the source coder, we give a lower bound on the expected distortion of a distortion-based optimal solution that depends only on a rate-based optimal solution. Then, we propose a local search (LS) algorithm that starts from a rate-based optimal solution and converges in linear time to a local minimum of the expected distortion. Experimental results for a binary symmetric channel show that our LS algorithm is near optimal, whereas its complexity is much lower than that of the previous best solution. [ABSTRACT FROM AUTHOR]