This PDF file contains the editorial “JEI 2013 List of Reviewers” for JEI Vol. 23 Issue 01

We propose an intraprediction scheme that enables the pipelined processing to reduce processing time in the high-efficiency video coding (HEVC) encoder. In the standard HEVC encoder, a rough mode decision (RMD) is used to reduce the complexity and processing time for intraprediction. However, because the RMD of the current block cannot be performed before the reconstruction of the previous block, intraprediction cannot be pipelined to reduce processing time. To remove this dependency, our proposed scheme uses the original data instead of the reconstructed data of the previous block in the RMD processing. By removing the data dependency, our proposed scheme allows block processing to be pipelined while maintaining consistency with the standard. Performance analysis results show that our proposed scheme reduces the processing time by up to 49.8%, with almost no loss in coding performance, compared to the HEVC test model 10.0.

Jaggies (staircasing effects) along slanted lines or curved edges are omnipresent in digital imaging. They are so widespread in digital display devices that very often they are associated with the modern “computerized” world (and sometimes even intentionally introduced into artworks such as logos, advertisements, etc. to convey a modern “pixelized” look). Although this subject is not new, it still remains an important issue in the design of modern digital display and printing devices. In the classical literature, jaggies are often considered as aliasing artifacts; and yet some other references consider them instead as reconstruction artifacts. The present tutorial revisits this question and tries to elucidate the real nature of this phenomenon using Fourier-based considerations. It shows that the jaggies can be either aliasing artifacts due to poor sampling in capture, or the result of poor reconstruction; and it explains the implications thereof on the elimination of unwanted jaggies.

This special section was made possible by the enthusiasm of the research community in stereoscopic displays and applications, a level of interest that has now sustained the associated SPIE/IS&T Conference into its 25th year. This is a noteworthy achievement for such a young field in which there is now a depth and breadth of research that sustains a vibrant international group of researchers.

When a directional backlight to each eye alternates synchronously with the alternation of left-eye and right-eye images on the display panel, the viewer can see a stereoscopic image without wearing special goggles. One way to realize a directional backlight is to place a convex lens array in front of dot matrix light sources to generate collimated light. To implement this method, however, defocusing and field curvature of the lens should be taken into account. The viewing zone of an autostereoscopic display with a directional backlight using a convex lens array is analyzed based on optical simulations.

Automatic prediction of visual discomfort for stereoscopic videos is important to address the viewing safety issues in stereoscopic displays. In literature, disparity and motion characteristics have been known as major factors of visual discomfort caused by stereoscopic content. For developing an algorithm to accurately predict visual discomfort in stereoscopic videos, this study investigates how to combine the individual prediction values of disparity- and motion-induced discomforts into an overall prediction value of visual discomfort. To that end, subjective experiments were performed with various stereoscopic videos, and four possible combination methods were compared based on the results of subjective experiments. The combination methods used in this study were the weighted summation, multiplication, Minkowski summation, and max combination methods. Experimental results showed that the Minkowski summation combination with a high exponent and max combination yielded the best accuracy in predicting the overall level of visual discomfort. The results indicated that the overall level of perceived discomfort could be dominantly affected by the most significant discomfort factor, i.e., the winner-takes-all mechanism. Our results could be useful for the future development of an accurate and reliable algorithm for visual discomfort prediction.

In recent years, the problem of acquiring omnidirectional stereoscopic imagery of dynamic scenes has gained commercial interest, and consequently, new techniques have been proposed to address this problem. The goal of many of these new panoramic methods is to provide practical solutions for acquiring real-time omnidirectional stereoscopic imagery for human viewing. However, there are problems related to mosaicking partially overlapped stereoscopic snapshots of the scene that need to be addressed. Among these issues are the conditions to provide a consistent depth illusion over the whole scene and the appearance of undesired vertical disparities. We develop an acquisition model capable of describing a variety of omnistereoscopic imaging systems and suitable to study the design constraints of these systems. Based on this acquisition model, we compare different acquisition approaches based on mosaicking partial stereoscopic views of the scene in terms of their depth continuity constraints and the appearance of vertical disparities. This work complements and extends our previous work in omnistereoscopic imaging systems by proposing a mathematical framework to contrast different acquisition strategies to create stereoscopic panoramas using a small number of stereoscopic images.

We present a technique for achieving tracked vertical parallax for multiple users using a variety of autostereoscopic projector array setups, including front- and rear-projection and curved display surfaces. This hybrid parallax approach allows for immediate horizontal parallax as viewers move left and right and tracked parallax as they move up and down, allowing cues such as three-dimensional (3-D) perspective and eye contact to be conveyed faithfully. We use a low-cost RGB-depth sensor to simultaneously track multiple viewer head positions in 3-D space, and we interactively update the imagery sent to the array so that imagery directed to each viewer appears from a consistent and correct vertical perspective. Unlike previous work, we do not assume that the imagery sent to each projector in the array is rendered from a single vertical perspective. This lets us apply hybrid parallax to displays where a single projector forms parts of multiple viewers’ imagery. Thus, each individual projected image is rendered with multiple centers of projection, and might show an object from above on the left and from below on the right. We demonstrate this technique using a dense horizontal array of pico-projectors aimed into an anisotropic vertical diffusion screen, yielding 1.5 deg angular resolution over 110 deg field of view. To create a seamless viewing experience for multiple viewers, we smoothly interpolate the set of viewer heights and distances on a per-vertex basis across the array’s field of view, reducing image distortion, cross talk, and artifacts from tracking errors.

Depth perception is an important component of many augmented reality applications. It is, however, subject to multiple error sources. In this study, we investigated depth judgments with a stereoscopic video see-through head-mounted display for the purpose of designing depth cueing for systems that operate in an individual’s action space. In the experiment, we studied the use of binocular disparity and relative size to improve relative depth judgments of augmented objects above the ground plane. The relative size cue was created by adding auxiliary augmentations to the scene according to constraints described in the section on the underlying theory. The results showed that binocular disparity and relative size improved depth judgments over the distance range. This indicates that for accurate depth judgments, additional depth cues should be used to facilitate stereoscopic perception within an individual’s action space.

Cell visualization is an important area of scientific and educational visualization. There is already a number of astonishing animations illustrating the structural and functional properties of biological cells available in the Internet. However, these visualizations usually do not take advantage of three-dimensional (3-D) stereoscopic techniques. The stereoscopic visualization of the microcosmos cell—invisible to the human eye—bears high potential for educational as well as scientific approaches. Using open source tools it will be shown that it is possible to generate statically rendered as well as interactive stereoscopic cell visualizations. First, the 3-D modeling software Blender in conjunction with Schneider’s stereoscopic camera plug-in will be used to generate a stereoscopic cell animation. While static renderings have an advantage in that the stereoscopic effect can be optimized for spectators, interactive stereoscopic visualizations always have to adjust and optimize the stereoscopic effect for users who can freely navigate through space. Cell visualization is paradigmatic for this problem because the scale differences from the mesoscopic to the molecular level account for a factor of 100,000. Therefore, two stereoscopic approaches of the CELLmicrocosmos project will be introduced, which address the stereoscopic scaling problem. The stereoscopic quality was positively evaluated by 20 students.

A coherence scanning tomographic imaging system with an innovative signal correction method is presented for a critical dimension (CD) measurement of thin film transistor liquid crystal display patterns having multiple focus positions within a single field of view. To facilitate the analyzing of coherence signals, a simulation model based on fast Fourier transform method is proposed, and its simulated result is compared with the coherence signals from the actual experiments. The comparison shows that the majority of frequency characteristics from simulation modeling results are matched with the experimental results. However, in many edge regions, discrepancies in frequency characteristics between the two results are observed. For the interpretation of signals, those are different from the simulation modelling, in that the intensity of its pixels has been corrected by an innovatively proposed connected neighborhoods window method with multiple window sizes. By using this combination of tomographic imaging and edge correction methods, the repeatability of the CD measurement of multiple focus position samples is significantly enhanced compared to the results with a plain two-dimensional optics. The proposed method is also compared with the autofocus methods including gradient magnitude method and frequency domain method and other tomographic imaging methods, including the phase shift method and the Hilbert transform method to show the advantages in the processing time.

Video stabilization is a critical step for improving the quality of videos captured by unmanned aerial vehicles. However, the complicated scenarios in the video and the need for instantaneously presenting a stabilized image posed significant challenges to the existing methods. In this work, an instantaneous video stabilization method for unmanned aerial vehicles is proposed. This new approach serves several purposes: smoothing the video motion in both two-dimensional and three-dimensional (3-D) scenes, decreasing the lags in response, and instantaneously providing the stabilized image to users. For each input frame, our approach regenerates four short motion trajectories by applying interframe transformations to the four corners of the image rectangle. An adaptive filter is then performed to smooth motion trajectories and suppress the lags in response simultaneously. Finally, at the stage of image composition, the quality of image is considered for selecting a visually plausible stabilized video. Experiments show that our approach can stabilize various videos without the need for user interaction or costly 3-D reconstruction, and it works as an instant-process for videos from an online source.

Conventional graph embedding framework uses the Euclidean distance to determine the similarities of neighbor samples, which causes the graph structure to be sensitive to outliers and lack physical interpretation. Moreover, the graph construction suffers from the difficulty of neighbor parameter selection. Although sparse representation (SR) based graph embedding methods can automatically select the neighbor parameter, the computational cost of SR is expensive. On the other hand, most discriminant projection methods fail to perform feature selection. In this paper, we present a novel joint discriminant analysis and feature selection method that employs regularized least square for graph construction and l_2,1-norm minimization on projection matrix for feature selection. Specifically, our method first uses the regularized least square coefficients to measure the intraclass and interclass similarities from the viewpoint of reconstruction. Based on this graph structure, we formulate an object function with scatter difference criterion for learning the discriminant projections, which can avoid the small sample size problem. Simultaneously, the l_2,1-norm minimization on projection matrix is applied to gain row-sparsity for selecting useful features. Experiments on two face databases (ORL and AR) and COIL-20 object database demonstrate that our method not only achieves better classification performance, but also has lower computational cost than SR.

Victim identification in mass disasters is a pressing need at present. Since teeth are the most robust parts of the human body and also unique, they can be used as a key component if sufficient dental records are available. This research also aims at dental photographs if there is a nonavailability of radiographs in profusion. In order to reduce the search space, the teeth are initially classified as molar, premolar, and canine using a linear support vector machine and numbered using the universal numbering system. Since the postmortem records are detained for a long span after antemortem records, other issues like missing teeth are considered here. For individual identification, contour information has been proven as a useful measure so far. In addition to contour and skeleton, the texture information of an individual tooth is also incorporated in this paper. The prediction of contour and skeleton using angle and distance parameters is the novelty of this study. The cumulative matching characteristic curve and accuracy curves are plotted to validate the performance of this algorithm. The proposed method identifies the person accurately and signifies that it could give a substantial assistance to forensic odontologist during victim identification.

Optical blur due to lens aberrations and defocus has been demonstrated in some cases to be spatially varying across the image plane. However, existing models in the literature for the point-spread function (PSF) corresponding to this blur are either parameterized and spatially invariant or spatially varying but ad-hoc and discretely defined. A parameterized model is developed and presented for a spatially varying PSF due to lens aberrations and defocus in an imaging system. The model is motivated from an established theoretical framework in physics and is demonstrated to be able to unify a set of hundreds of PSF observations across the image plane into a single 10-parameter model. The accuracy of the model is demonstrated with simulations and measurement data collected by two separate research groups.

The original presentation of Retinex, a spatial color correction and image enhancement algorithm modeling the human vision system, as proposed by Land and McCann in 1964, uses paths to explore the image in search of a local reference white point. The interesting results of this algorithm have led to the development of many versions of Retinex. They follow the same principle but differ in the way they explore the image, with, for example, random paths, random samples, convolution masks, and variational formulations. We propose an alternative way to explore local properties of Retinex, replacing random paths by traces of a specialized swarm of termites. In presenting the spatial characteristics of the proposed method, we discuss differences in path exploration with other Retinex implementations. Experiments, results, and comparisons are presented to test the efficacy of the proposed Retinex implementation.

Vessel centerline detection is very important in many medical applications. In the noise and low-contrast regions, most existing methods may only produce an incomplete and disconnected extraction of the vessel centerline if no user guidance is provided. A robust and automatic method is described for extraction of the vessel centerline. First, we perform small vessel enhancement by processing with a set of line detection filters, corresponding to the 13 orientations; for each voxel, the highest filter response is kept and added to the image. Second, we extract vessel centerline segment candidates by a thinning algorithm. Finally, a global optimization algorithm is employed for grouping and selecting vessel centerline segments. We validate the proposed method quantitatively on a number of synthetic data sets, the liver artery and lung vessel. Comparisons are made with two state-of-the-art vessel centerline extraction methods and manual extraction. The experiments show that our method is more accurate and robust that these state-of-the-art methods and is, therefore, more suited for automatic vessel centerline extraction.

The Kalman filter is widely acknowledged as a linear optimal estimator. However, it is usually considered to have taxing computational and storage requirements in the field of image superresolution. This work presents an innovative Kalman filter–based method to solve the inherent problems of the Kalman filter and make it feasible for superresolution applications. Unlike other Kalman filter–based methods, we did not assume the pixels of high-resolution (HR) image are uncorrelated and the covariance matrix is diagonal for the calculation simplification. Instead, we take the spatial correlation of the pixels within a local block of HR image into consideration and propose a block diagonal assumption for the prediction covariance after a new construction way of the column vector which represents the HR image is presented. This significantly reduces the computational complexity during the Kalman filtering. Then, we develop a point-wise Kalman filter method to reduce the computational complexity and decrease the storage requirement. Furthermore, an iterative scheme is proposed to overcome the inaccurate initial estimation problem of superresolution reconstruction. The experiments with synthetic and real data show that the proposed method is effective and outperforms other superresolution methods.

We propose a unified approach to propagate knowledge into a high-dimensional space from a small informative set, in this case, scale invariant feature transform (SIFT) features. Our contribution lies in three aspects. First, we propose a spectral graph embedding of the SIFT points for dimensionality reduction, which provides efficient keypoints transcription into a Euclidean manifold. We use iterative deflation to speed up the eigendecomposition of the underlying Laplacian matrix of the embedded graph. Then, we describe a variational framework for manifold denoising based on p -Laplacian to enhance keypoints classification, thereby lessening the negative impact of outliers onto our variational shape framework and achieving higher classification accuracy through agglomerative categorization. Finally, we describe our algorithm for multilabel diffusion on graph. Theoretical analysis of the algorithm is developed along with the corresponding connections with other methods. Tests have been conducted on a collection of images from the Berkeley database. Performance evaluation results show that our framework allows us to efficiently propagate the prior knowledge.

We present a stochastically seeded, clustered halftoning method with parametric control of dot shapes as well as seed placement adaption to local image structure. While the literature on other randomly structured screens (blue noise, green noise, and stochastic halftoning) is quite extensive, there is very little discussion on optimization of dot shape for robustness or preferred tile appearance. The parametric control presented here can enable an imaging engineer to optimize robustness for a given printing system. The halftoning method that we introduce defines dot centers as seeds that are placed typically in a random, high spatial frequency configuration. Spot functions are defined about these randomly placed seeds, where the spot function allows control of dot cluster growth, touch points, cluster angle, and eccentricity. The spot function can also be applied to regular and irregular polygonal halftone tiling. The adaptability of the seed placement provided by the halftoning method allows for better edge rendition than conventional isotropic methods and enables the implementation of data-embedding techniques. The resulting monochromatic screens can be seamlessly integrated with novel successive filling techniques in order to provide dot-off-dot vector halftoning capabilities.

An efficient descriptor and a complete scheme for removing incorrect correspondences are two essentials for accurate feature points matching in image processing and pattern recognition. This paper proposes a new rigid feature points matching algorithm. First, a new feature descriptor based on the local sharpness distribution is proposed to extract features, which leads to a feature set of extremely low dimensionality. With these features, the normalized cross-correlation coefficient and a bidirectional matching strategy are employed to generate the initial feature point correspondences. Then a complete scheme with two rules is proposed to remove the probable incorrect correspondences. Owing to these two rules based on the voting strategy and the ratio of the distances, respectively, the accuracy of feature points matching is remarkably improved. Experimental results show that the proposed algorithm is efficient for feature matching and comparisons with other algorithms show its better comprehensive performance.

In order to reduce the computational complexity for the high-efficiency video coding standard, an algorithm is proposed. First, we create a histogram based on the distribution of gray values in each coding tree unit. By using a self-correlation function of the gray histogram, we can skip some of the depth levels that are unnecessary. Second, the relationship between the sum of absolute transformed difference (SATD) and coding unit (CU) is used to decide an early termination for the split of the current CU, if the minimum SATD of this CU is smaller than a threshold. Experimental results show that the proposed algorithm can save an average of 38.39% of the encoding time with negligible degradation of coding efficiency compared with the reference model HM10.1.

We investigate the use of different trabecular bone descriptors and advanced machine learning techniques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R ² . The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869±0.121 , R ² : 0.68±0.079 ), which was significantly better than DXA BMD alone (RMSE: 0.948±0.119 , R ² : 0.61±0.101 ) (p<10 ⁻⁴ ). For multivariate feature sets, SVR outperformed multiregression (p<0.05 ). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

An intrafield deinterlacing algorithm based on gradient-regularized modular neural networks is proposed. The proposed method defines six gradient regularization terms for every missing pixel. Different modular neural networks are selectively used according to the gradient of the pixel to be interpolated. With the statistics of the six gradient regularization terms, a more robust output is generated by modular neural networks. When compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise-ratio while achieving better subjective quality.

To solve the image deblurring problem, most of the recently proposed nonblind deblurring approaches focus on regularizing the solutions by adding image priors, such as sparse prior, piece-wise smooth prior (total-variation prior), and so on. This paper, however, presents a new term, which can be viewed as a supplement to the data fidelity term, to improve the nonblind deblurring result. The new term is based on the relative error in frequency domain (REFD) and can suppress the ringing artifacts and preserve the image details. We analyze the properties of REFD for image deconvolution; then, a nonblind image deconvolution algorithm is proposed via introducing the REFD term into the image deconvolution model. The experimental results demonstrate the performance and efficacy of our proposed algorithm when compared to some other state-of-the-art algorithms.

Algorithms for video quality assessment (VQA) aim to estimate the qualities of videos in a manner that agrees with human judgments of quality. Modern VQA algorithms often estimate video quality by comparing localized space-time regions or groups of frames from the reference and distorted videos, using comparisons based on visual features, statistics, and/or perceptual models. We present a VQA algorithm that estimates quality via separate estimates of perceived degradation due to (1) spatial distortion and (2) joint spatial and temporal distortion. The first stage of the algorithm estimates perceived quality degradation due to spatial distortion; this stage operates by adaptively applying to groups of spatial video frames the two strategies from the most apparent distortion algorithm with an extension to account for temporal masking. The second stage of the algorithm estimates perceived quality degradation due to joint spatial and temporal distortion; this stage operates by measuring the dissimilarity between the reference and distorted videos represented in terms of two-dimensional spatiotemporal slices. Finally, the estimates obtained from the two stages are combined to yield an overall estimate of perceived quality degradation. Testing on various video-quality databases demonstrates that our algorithm performs well in predicting video quality and is competitive with current state-of-the-art VQA algorithms.

Digital tomosynthesis is an innovative imaging technology for early breast cancer detection by providing three-dimensional anatomical information with fast image acquisition and low-dose radiation. Most of current breast tomosynthesis systems utilize a design where a single x-ray tube moves along an arc above objects over a certain angular range. The mechanical movement and patient motion during the scan may degrade image quality. With a carbon nanotube–based multibeam x-ray source, a new breast tomosynthesis modality is innovated, which will potentially produce better image quality with stationary beam sources and faster scan and it enables a variety of beam distributions. In this study, several beam distributions, such as beam sources spanning along a one-dimensional (1-D) parallel configuration and sources over a two-dimensional (2-D) rectangle shape are investigated based on computer simulations. Preliminary results show that 2-D rectangle shapes outperform 1-D parallel shapes by providing better Z-resolution, enhanced image contrast, reduced out-of-plane blur and artifacts and lower reconstruction noise. These benefits may expand tomosynthesis applications to diagnostic and interventional procedures.

Transform coding using the discrete cosine transform is one of the most popular techniques for image and video compression. However, at low bit rates, the coded images suffer from severe visual distortions. An innovative approach is proposed that deals with artifacts in JPEG compressed images. Our algorithm addresses all three types of artifacts which are prevalent in JPEG images: blocking, and for edges blurring, and aliasing. We enhance the quality of the image via two stages. First, we remove blocking artifacts via boundary smoothing and guided filtering. Then, we reduce blurring and aliasing around the edges via a local edge-regeneration stage. We compared the proposed algorithm with other modern JPEG artifact-removal algorithms. The results demonstrate that the proposed approach is competitive, and can in many cases outperform, competing algorithms.

We present an innovative method based on the linear advection equation, an important partial differential equation, to perform the fusion of images. The basic idea of this method is to insert the relevant information from other source images into the current source image through an advection process. Furthermore, we present the discrete scheme of this model and compare it with classical fusion approaches, the diffusion equation-based method, and some state-of-the-art fusion approaches on three groups of fusion images that are often used in the image fusion research. The results of experiments show that the fusion method based on the advection equation is comparable with the best of the classical, diffusion-based, and state-of-the-art methods. The high “weighted performance metric” Q ^AB/F of fused images certifies that the relevant information is well injected from the input to the output images. Moreover, this method has fewer adjustable parameters with settings that affect the metric Q ^AB/F less than other methods, and the evolution from input to output is also faster than the diffusion-based method. In addition, this model allows us to cope with noisy source image fusion by adding a diffusion term in the equation, thereby combining the denoising process with the fusion process.

Superresolution (SR) algorithms have recently become a hot research topic. The main purpose of image upscaling is to obtain high-resolution images from low-resolution ones, and these upscaled images should look like they had been taken with a camera having a resolution the same as the upscaled images, and at least present natural textures. In general, some SR algorithms preserve clear edges but blur the textures, while others preserve detailed textures but cause some obvious artifacts along edges. The proposed SR algorithm presents the detailed textures and, meanwhile, refines the strong edges and avoids causing obvious artifacts. The goal is achieved by using orthogonal fractal as the preliminary upscaling method in conjunction with the proper postprocessing where directional enhancement is adopted. In fact, the postprocessing part in the proposed SR algorithm can effectively reduce most jagged artifacts caused by SR algorithms. In the simulation results, it is shown that the proposed SR algorithm performs well in both objective and subjective measurements. Moreover, most detailed textures are properly enhanced and most jagged artifacts caused by SR algorithms can also be effectively reduced.

We describe a photon statistics-based theoretical model of the response to incident light of an image sensor and show that conditional reset and multibit temporal oversampling increase the dynamic range significantly. This photon-based modeling approach describes the full image sensor design space of temporal and spatial oversampling either with a binary comparison or with a multibit read of each sample. We find excellent quantitative agreement between measurements on custom hardware and our theoretical predictions. We then use this model to show what improvements in dynamic range and low-light response can be achieved by oversampling and what the limits of improvement caused by pixel size and lens parameters are.

We propose an innovative method that reduces the size of three-dimensional (3-D) volume data while preserving important features in the data. Our method quantifies the importance of features in the 3-D data based on gray level co-occurrence matrix texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact compared with the previous methods. Although an additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.

We propose a local region statistics-based weighted interpolation filter for intrafield deinterlacing. The proposed algorithm consists of three steps: first, we preinterpolate the missing line with an efficient interpolation filter in the working window. Then, we calculate the coefficients of the center-weighted interpolation filter by exploiting local statistics, namely the center-weighted mean, the center-weighted variance, and the closeness of the neighboring pixels. In the last step, we interpolate the missing line using the proposed filter. Experimental results show that the proposed algorithm provides superior performances in terms of both objective and subjective image qualities than the existing algorithm.

We propose a color image segmentation approach based on rough set theory elements. Main contributions of the proposed approach are twofold. First, by using an adaptive threshold selection, the approach is automatically adjustable according to the image content. Second, a region-merging process, which takes into account both features and spatial relations of the resulting segments, lets us minimize over-segmentation issues. These two proposals allow our method to overcome some performance issues shown by previous rough set theory-based approaches. In addition, a study to determine the best suited color representation for our segmentation approach is carried out, determining that the best results are obtained using a perceptually uniform color space. A set of qualitative and quantitative tests over a comprehensive image database shows that the proposed method produces high-quality segmentation outcomes, better than those obtained using the previous rough set theory-based and standard segmentation approaches.

Least significant bit matching revisited (LSBMR) steganography uses two adjacent pixels as an embedding unit to conceal secret messages. In each unit, the modification probability of the first pixel is twice the modification probability of the second pixel, thereby causing an asymmetric effect on the statistical distribution of pixel difference in LSBMR for consecutive pixels (LSBMRCP). On the basis of the analysis of this asymmetric effect, a fast steganalytic feature is deduced and an embedding rate estimating method that employs an iteration strategy is proposed. Experimental results show that the steganalytic feature can effectively detect LSBMRCP steganography and that the detection performance is superior to the methods proposed by Tan and Xiong et al. Moreover, the embedding rate estimating method is so accurate that the order of magnitude of prediction error is maintained at 10 ⁻² , measured by the mean absolute error, median absolute difference, and interquartile range, thus indicating that the proposed method significantly outperforms the method proposed by Tan.

We propose a new set of bivariate discrete orthogonal polynomials, which are the product of Charlier’s discrete orthogonal polynomials with one variable by Tchebichef, Krawtchouk, and Hahn discrete orthogonal polynomials with one variable. This set of bivariate discrete orthogonal polynomials is used to define several new types of discrete orthogonal moments such as Charlier-Tchebichef moments (CTM), Charlier-Krawtchouk moments (CKM), and Charlier-Hahn moments (CHM). We also present an approach for fast computation of CTM, CKM, and CHM discrete orthogonal moments using the image block representation for binary images and image slice representation for grayscale images. A set of Charlier-Tchebichef invariant moments, Charlier-Krawtchouk invariant moments, and Charlier-Hahn invariant moments is also presented. These invariant moments are derived algebraically from the geometric invariant moments, and their computation is accelerated using an image representation scheme. The presented algorithms are tested in several well-known computer vision datasets including image reconstruction, computational time, moment’s invariability, and classification of objects. The performance of these invariant moments used as pattern features for a pattern classification is compared with Hu, Legendre, Tchebichef-Krawtchouk, Tchebichef-Hahn, and Krawtchouk-Hahn invariant moments.

Multiview video coding (MVC) is a recent extension of H.264/AVC, and it consumes huge encoding time to select the optimal macroblock (MB) mode, among different size candidate modes. As compared with the small-size mode (Inter16×8 , Inter8×16 , Inter8×8 , Intra8×8 , and Intra4×4 ), the large-size mode (Skip/Direct, Inter16×16 , and Intra16×16 ) occupies most of the MB mode proportion with much less computational complexity. Thus, if the large-size mode could be early decided as the optimal MB mode, the complexity of mode decision could be effectively reduced. In this work, an early large-size mode decision algorithm is proposed based on the global correlation of rate-distortion (RD) costs between neighbor views and the local correlation of RD costs among candidate modes. Average RD costs of large-size and small-size MB modes in the neighbor view are employed as a global reference for the threshold of early decision. And RD costs of estimated modes are used to calculate the local adjustment for the threshold. Experimental results demonstrate that the proposed algorithm can significantly reduce the whole encoding time while maintaining an RD performance similar to that of the original MVC encoder.

We present an innovative calibration method for line-scan cameras to estimate the intrinsic parameters. The calibration involves using a stationary planar pattern that consists of repeated vertical and slanted lines, and constructing a two-dimensional (2-D) calibration framework with one-dimensional (1-D) data. A feature point reconstruction method is applied to transform the 1-D camera calibration problem into the 2-D scope. Camera parameters are then solved by using a 2-D camera model with constraints unique to 1-D geometry. In our tests over 12 calibrations with images of 2048×2048  pixels , the average of the reprojection errors is 0.46 pixels. As opposed to other line-scan camera calibration techniques, this method does not require the camera to progressively scan a pattern, thus eliminating the need for additional mechanical devices to assist the calibration. This method does not need a three-dimensional pattern as a calibration target, either. The stationary planar target makes the calibration more suitable for an application that has to be done in a nonlaboratory setting, such as highway pavement inspection.

Real-time camera tracking is steadily gaining importance due to the drive from various applications, such as augmented reality, three-dimensional structure estimation/modeling, and mobile computing environment. However, tracking a monocular camera in an unknown environment is not a trivial work. We describe a real-time camera tracking framework designed to track a monocular camera in a workspace. In particular, we focus on integration of a bundle of nonlinear filters to achieve robust camera tracking and scalable feature mapping, which can extend to larger environment. The basic idea of the proposed framework is that a particle filter–based camera tracking is connected to independent feature tracking filters, which have fixed-state dimension. In addition, every estimate required for template prediction is obtained from the independent feature estimators so that the template prediction can be maintained without additional framework for the template state estimation. We split the camera tracking and feature mapping into two separate tasks, and they are handled in two parallel processes. We demonstrate the effectiveness of the proposed approach within a desktop environment in real time.

Algorithms for image quality assessment (IQA) aim to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving predictive performance; very few studies have focused on analyzing and improving the runtime performance of IQA algorithms. This paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms (most apparent distortion, multiscale structural similarity, visual information fidelity, and visual signal-to-noise ratio) and two no-reference algorithms (blind image integrity notator using DCT statistics and blind/referenceless image spatial quality evaluator) in C++ based on the code provided by their respective authors. We then conducted a hotspot analysis to identify sections of code that were performance bottlenecks and performed microarchitectural analysis to identify the underlying causes for these bottlenecks. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. Based on these results, we propose microarchitectural-conscious coding techniques and custom hardware recommendations for performance improvement.

The representation of images is an active and very important area in image processing and pattern recognition. Therefore, in the literature, different contour codes for binary images have been proposed, such as F4,F8,VCC,3OT , and AF8 . These codes have been used in many papers since the first chain code, F8 , was introduced by Freeman in 1961. All the codes have been tried here as vector representations, including vertex chain code (VCC ). To know their properties, this paper provides an analysis of comparisons of each code, and as an important contribution, we investigated the relationship between them and found a series of transformations that allow simple and efficient sets to go from one code to another. We found the equivalences between F4,VCC,3OT,F8 , and AF8 . As an important consequence of the transition matrix concept, we proposed a new code for eight connectivity by observing a missing code in the state of the art and in the inspiration of the 3OT code.