By Topic

Image Processing, IEEE Transactions on

Issue 11 • Date Nov. 2013

Filter Results

Displaying Results 1 - 25 of 48
  • [Front cover]

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (117 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (129 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 4153 - B4155
    Save to Project icon | Request Permissions | PDF file iconPDF (447 KB)  
    Freely Available from IEEE
  • [Blank page - back cover]

    Page(s): B4156
    Save to Project icon | Request Permissions | PDF file iconPDF (5 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 4157 - B4160
    Save to Project icon | Request Permissions | PDF file iconPDF (450 KB)  
    Freely Available from IEEE
  • Image Noise Reduction via Geometric Multiscale Ridgelet Support Vector Transform and Dictionary Learning

    Page(s): 4161 - 4169
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4159 KB) |  | HTML iconHTML  

    Advances in machine learning technology have made efficient image denoising possible. In this paper, we propose a new ridgelet support vector machine (RSVM) for image noise reduction. Multiscale ridgelet support vector filter (MRSVF) is first deduced from RSVM, to produce a multiscale, multidirection, undecimated, dyadic, aliasing, and shift-invariant geometric multiscale ridgelet support vector transform (GMRSVT). Then, multiscale dictionaries are learned from examples to reduce noises existed in GMRSVT coefficients. Compared with the available approaches, the proposed method has the following characteristics. The proposed MRSVF can extract the salient features associated with the linear singularities of images. Consequently, GMRSVT can well approximate edges, contours and textures in images, and avoid ringing effects suffered from sampling in the multiscale decomposition of images. Sparse coding is explored for noise reduction via the learned multiscale and overcomplete dictionaries. Some experiments are taken on natural images, and the results show the efficiency of the proposed method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Establishing Point Correspondence of 3D Faces Via Sparse Facial Deformable Model

    Page(s): 4170 - 4181
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3811 KB) |  | HTML iconHTML  

    Establishing a dense vertex-to-vertex anthropometric correspondence between 3D faces is an important and fundamental problem in 3D face research, which can contribute to most applications of 3D faces. This paper proposes a sparse facial deformable model to automatically achieve this task. For an input 3D face, the basic idea is to generate a new 3D face that has the same mesh topology as a reference face and the highly similar shape to the input face, and whose vertices correspond to those of the reference face in an anthropometric sense. Two constraints: 1) the shape constraint and 2) correspondence constraint are modeled in our method to satisfy the three requirements. The shape constraint is solved by a novel face deformation approach in which a normal-ray scheme is integrated to the closest-vertex scheme to keep high-curvature shapes in deformation. The correspondence constraint is based on an assumption that if the vertices on 3D faces are corresponded, their shape signals lie on a manifold and each face signal can be represented sparsely by a few typical items in a dictionary. The dictionary can be well learnt and contains the distribution information of the corresponded vertices. The correspondence information can be conveyed to the sparse representation of the generated 3D face. Thus, a patch-based sparse representation is proposed as the correspondence constraint. By solving the correspondence constraint iteratively, the vertices of the generated face can be adjusted to correspondence positions gradually. At the early iteration steps, smaller sparsity thresholds are set that yield larger representation errors but better globally corresponded vertices. At the later steps, relatively larger sparsity thresholds are used to encode local shapes. By this method, the vertices in the new face approach the right positions progressively until the final global correspondence is reached. Our method is automatic, and the manual work is needed only in training procedure- The experimental results on a large-scale publicly available 3D face data set, BU-3DFE, demonstrate that our method achieves better performance than existing methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generalizing the Majority Voting Scheme to Spatially Constrained Voting

    Page(s): 4182 - 4194
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1108 KB) |  | HTML iconHTML  

    Generating ensembles from multiple individual classifiers is a popular approach to raise the accuracy of the decision. As a rule for decision making, majority voting is a usually applied model. In this paper, we generalize classical majority voting by incorporating probability terms pn,k to constrain the basic framework. These terms control whether a correct or false decision is made if k correct votes are present among the total number of n. This generalization is motivated by object detection problems, where the members of the ensemble are image processing algorithms giving their votes as pixels in the image domain. In this scenario, the terms pn,k can be specialized by a geometric constraint. Namely, the votes should fall inside a region matching the size and shape of the object to vote together. We give several theoretical results in this new model for both dependent and independent classifiers, whose individual accuracies may also differ. As a real world example, we present our ensemble-based system developed for the detection of the optic disc in retinal images. For this problem, experimental results are shown to demonstrate the characterization capability of this system. We also investigate how the generalized model can help us to improve an ensemble with extending it by adding a new algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

    Page(s): 4195 - 4210
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3321 KB) |  | HTML iconHTML  

    This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Variational Stereo Imaging of Oceanic Waves With Statistical Constraints

    Page(s): 4211 - 4223
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6574 KB) |  | HTML iconHTML  

    An image processing observational technique for the stereoscopic reconstruction of the waveform of oceanic sea states is developed. The technique incorporates the enforcement of any given statistical wave law modeling the quasi-Gaussianity of oceanic waves observed in nature. The problem is posed in a variational optimization framework, where the desired waveform is obtained as the minimizer of a cost functional that combines image observations, smoothness priors and a weak statistical constraint. The minimizer is obtained by combining gradient descent and multigrid methods on the necessary optimality equations of the cost functional. Robust photometric error criteria and a spatial intensity compensation model are also developed to improve the performance of the presented image matching strategy. The weak statistical constraint is thoroughly evaluated in combination with other elements presented to reconstruct and enforce constraints on experimental stereo data, demonstrating the improvement in the estimation of the observed ocean surface. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

    Page(s): 4224 - 4236
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3427 KB) |  | HTML iconHTML  

    We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Extracting Dominant Textures in Real Time With Multi-Scale Hue-Saturation-Intensity Histograms

    Page(s): 4237 - 4248
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2762 KB) |  | HTML iconHTML  

    It is very important to extract high quality texture features from images. This is, however, often laborious, because the randomness in the color distribution patterns for texture elements makes texture measurement very difficult, despite these elements having a very similar visual appearance. In this paper, we propose the use of multi-scale color histograms to measure the effect of color distribution patterns efficiently and without having to compute the actual patterns, which saves considerable effort. Meanwhile, the hue-saturation-intensity color model is mainly adopted to take the advantage of human visual experiences in texture recognition. We discuss and validate the effectiveness and efficiency of our method by applying to various benchmarks. The results show that we can extract quality dominant textures automatically in real time, and faster by several orders of magnitude than existing methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Face Illumination Manipulation Using a Single Reference Image by Adaptive Layer Decomposition

    Page(s): 4249 - 4259
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5998 KB) |  | HTML iconHTML  

    This paper proposes a novel image-based framework to manipulate the illumination of human face through adaptive layer decomposition. According to our framework, only a single reference image, without any knowledge of the 3D geometry or material information of the input face, is needed. To transfer the illumination effects of a reference face image to a normal lighting face, we first decompose the lightness layers of the reference and the input images into large-scale and detail layers through weighted least squares (WLS) filter with adaptive smoothing parameters according to the gradient values of the face images. The large-scale layer of the reference image is filtered with the guidance of the input image by guided filter with adaptive smoothing parameters according to the face structures. The relit result is obtained by replacing the largescale layer of the input image with that of the reference image. To normalize the illumination effects of a non-normal lighting face (i.e., face delighting), we introduce similar reflectance prior to the layer decomposition stage by WLS filter, which make the normalized result less affected by the high contrast light and shadow effects of the input face. Through these two procedures, we can change the illumination effects of a non-normal lighting face by first normalizing the illumination and then transferring the illumination of another reference face to it. We acquire convincing relit results of both face relighting and delighting on numerous input and reference face images with various illumination effects and genders. Comparisons with previous papers show that our framework is less affected by geometry differences and can preserve better the identification structure and skin color of the input face. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Weighted Color and Texture Sample Selection for Image Matting

    Page(s): 4260 - 4270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3827 KB) |  | HTML iconHTML  

    Color sampling based matting methods find the best known samples for foreground and background colors of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Furthermore, current sampling based matting methods choose samples that are located around the boundaries of foreground and background regions. In this paper, we overcome these two problems. First, we propose texture as a feature that can complement color to improve matting by discriminating between known regions with similar colors. The contribution of texture and color is automatically estimated by analyzing the content of the image. Second, we combine local sampling with a global sampling scheme that prevents true foreground or background samples to be missed during the sample collection stage. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results shows that the proposed method is ranked first among all other image matting methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Contrast-Guided Image Interpolation

    Page(s): 4271 - 4285
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3338 KB) |  | HTML iconHTML  

    In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45° and 135° CDMs for interpolating the diagonal pixels and 2) the 0° and 90° CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Gaussian Process Guided Particle Filter for Tracking 3D Human Pose in Video

    Page(s): 4286 - 4300
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2695 KB) |  | HTML iconHTML  

    In this paper, we propose a hybrid method that combines Gaussian process learning, a particle filter, and annealing to track the 3D pose of a human subject in video sequences. Our approach, which we refer to as annealed Gaussian process guided particle filter, comprises two steps. In the training step, we use a supervised learning method to train a Gaussian process regressor that takes the silhouette descriptor as an input and produces multiple output poses modeled by a mixture of Gaussian distributions. In the tracking step, the output pose distributions from the Gaussian process regression are combined with the annealed particle filter to track the 3D pose in each frame of the video sequence. Our experiments show that the proposed method does not require initialization and does not lose tracking of the pose. We compare our approach with a standard annealed particle filter using the HumanEva-I dataset and with other state of the art approaches using the HumanEva-II dataset. The evaluation results show that our approach can successfully track the 3D human pose over long video sequences and give more accurate pose tracking results than the annealed particle filter. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Water Reflection Recognition Based on Motion Blur Invariant Moments in Curvelet Space

    Page(s): 4301 - 4313
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2654 KB) |  | HTML iconHTML  

    Water reflection, a typical imperfect reflection symmetry problem, plays an important role in image content analysis. Existing techniques of symmetry recognition, however, cannot recognize water reflection images correctly because of the complex and various distortions caused by the water wave. Hence, we propose a novel water reflection recognition technique to solve the problem. First, we construct a novel feature space composed of motion blur invariant moments in low-frequency curvelet space and of curvelet coefficients in high-frequency curvelet space. Second, we propose an efficient algorithm including two sub-algorithms: low-frequency reflection cost minimization and high-frequency curvelet coefficients discrimination to classify water reflection images and to determine the reflection axis. Through experimenting on authentic images in a series of tasks, the proposed techniques prove effective and reliable in classifying water reflection images and detecting the reflection axis, as well as in retrieving images with water reflection. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

    Page(s): 4314 - 4327
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3910 KB) |  | HTML iconHTML  

    Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an lp norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-Class Constrained Normalized Cut With Hard, Soft, Unary and Pairwise Priors and its Applications to Object Segmentation

    Page(s): 4328 - 4340
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4389 KB) |  | HTML iconHTML  

    Normalized cut is a powerful method for image segmentation as well as data clustering. However, it does not perform well in challenging segmentation problems, such as segmenting objects in a complex background. Researchers have attempted to incorporate priors or constraints to handle such cases. Available priors in image segmentation problems may be hard or soft, unary or pairwise, but only hard must-link constraints and two-class settings are well studied. The main difficulties may lie in the following aspects: 1) the nontransitive nature of cannot-link constraints makes it hard to use such constraints in multi-class settings and 2) in multi-class or pairwise settings, the output labels have inconsistent representations with given priors, making soft priors difficult to use. In this paper, we propose novel algorithms, which can handle both hard and soft, both unary and pairwise priors in multi-class settings and provide closed form and efficient solutions. We also apply the proposed algorithms to the problem of object segmentation, producing good results by further introducing a spatial regularity term. Experiments show that the proposed algorithms outperform the state-of-the-art algorithms significantly in clustering accuracy. Other merits of the proposed algorithms are also demonstrated. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image Classification via Object-Aware Holistic Superpixel Selection

    Page(s): 4341 - 4352
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1792 KB) |  | HTML iconHTML  

    In this paper, we propose an object-aware holistic superpixel selection (HPS) method to automatically select the discriminative superpixels of an image for image classification purpose. Through only considering the selected superpixels, the interference of cluttered background on the object can be alleviated effectively and thus the classification performance is significantly enhanced. In particular, for an image, HPS first selects the discriminative superpixels for the characteristics of certain class, which can together match the object template of this class well. In addition, these superpixels compose a class-specific matching region. Through performing such superpixel selection for several most probable classes, respectively, HPS generates multiple class-specific matching regions for a single image. Then, HPS merges these matching regions into an integral object region through exploiting their pixel-level intersection information. Finally, such object region instead of the original image is used for image classification. An appealing advantage of HPS is the ability to alleviate the interference of cluttered background yet not require the object to be segmented out accurately. We evaluate the proposed HPS on four challenging image classification benchmark datasets: Oxford-IIIT PET 37, Caltech-UCSD Birds 200, Caltech 101, and PASCAL VOC 2011. The experimental results consistently show that the proposed HPS can remarkably improve the classification performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Self-Similar Anisotropic Texture Analysis: The Hyperbolic Wavelet Transform Contribution

    Page(s): 4353 - 4363
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1387 KB) |  | HTML iconHTML  

    Textures in images can often be well modeled using self-similar processes while they may simultaneously display anisotropy. The present contribution thus aims at studying jointly selfsimilarity and anisotropy by focusing on a specific classical class of Gaussian anisotropic selfsimilar processes. It will be first shown that accurate joint estimates of the anisotropy and selfsimilarity parameters are performed by replacing the standard 2D-discrete wavelet transform with the hyperbolic wavelet transform, which permits the use of different dilation factors along the horizontal and vertical axes. Defining anisotropy requires a reference direction that needs not a priori match the horizontal and vertical axes according to which the images are digitized; this discrepancy defines a rotation angle. Second, we show that this rotation angle can be jointly estimated. Third, a nonparametric bootstrap based procedure is described, which provides confidence intervals in addition to the estimates themselves and enables us to construct an isotropy test procedure, which can be applied to a single texture image. Fourth, the robustness and versatility of the proposed analysis are illustrated by being applied to a large variety of different isotropic and anisotropic self-similar fields. As an illustration, we show that a true anisotropy built-in self-similarity can be disentangled from an isotropic self-similarity to which an anisotropic trend has been superimposed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • PET Protection Optimization for Streaming Scalable Videos With Multiple Transmissions

    Page(s): 4364 - 4379
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1641 KB) |  | HTML iconHTML  

    This paper investigates priority encoding transmission (PET) protection for streaming scalably compressed video streams over erasure channels, for the scenarios where a small number of retransmissions are allowed. In principle, the optimal protection depends not only on the importance of each stream element, but also on the expected channel behavior. By formulating a collection of hypotheses concerning its own behavior in future transmissions, limited-retransmission PET (LR-PET) effectively constructs channel codes spanning multiple transmission slots and thus offers better protection efficiency than the original PET. As the number of transmission opportunities increases, the optimization for LR-PET becomes very challenging because the number of hypothetical retransmission paths increases exponentially. As a key contribution, this paper develops a method to derive the effective recovery-probability versus redundancy-rate characteristic for the LR-PET procedure with any number of transmission opportunities. This significantly accelerates the protection assignment procedure in the original LR-PET with only two transmissions, and also makes a quick and optimal protection assignment feasible for scenarios where more transmissions are possible. This paper also gives a concrete proof to the redundancy embedding property of the channel codes formed by LR-PET, which allows for a decoupled optimization for sequentially dependent source elements with convex utility-length characteristic. This essentially justifies the source-independent construction of the protection convex hull for LR-PET. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • General Subspace Learning With Corrupted Training Data Via Graph Embedding

    Page(s): 4380 - 4393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3715 KB) |  | HTML iconHTML  

    We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fits the undesired properties of the data, and an error container that models the gross corruptions possibly existing in the data. Given a set of data points, these three components can be learned by solving a nuclear norm regularized optimization problem, which is convex and can be efficiently solved in polynomial time. Using the method as a tool, we propose a new discriminant analysis (i.e., supervised subspace learning) algorithm called Corruptions Tolerant Discriminant Analysis (CTDA), in which the intrinsic subspace is used to capture the features with high within-class similarity, the penalty subspace takes the role of modeling the undesired features with high between-class similarity, and the error container takes charge of fitting the possible corruptions in the data. We show that CTDA can well handle the gross corruptions possibly existing in the training data, whereas previous linear discriminant analysis algorithms arguably fail in such a setting. Extensive experiments conducted on two benchmark human face data sets and one object recognition data set show that CTDA outperforms the related algorithms. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Augmented Active Surface Model for the Recovery of Small Structures in CT

    Page(s): 4394 - 4406
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3734 KB) |  | HTML iconHTML  

    This paper devises an augmented active surface model for the recovery of small structures in a low resolution and high noise setting, where the role of regularization is especially important. The emphasis here is on evaluating performance using real clinical computed tomography (CT) data with comparisons made to an objective ground truth acquired using micro-CT. In this paper, we show that the application of conventional active contour methods to small objects leads to non-optimal results because of the inherent properties of the energy terms and their interactions with one another. We show that the blind use of a gradient magnitude based energy performs poorly at these object scales and that the point spread function (PSF) is a critical factor that needs to be accounted for. We propose a new model that augments the external energy with prior knowledge by incorporating the PSF and the assumption of reasonably constant underlying CT numbers. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient Reconstruction of All-in-Focus Images Through Shifted Pinholes From Multi-Focus Images for Dense Light Field Synthesis and Rendering

    Page(s): 4407 - 4421
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (3384 KB) |  | HTML iconHTML  

    Scene refocusing beyond extended depth of field for users to observe objects effectively is aimed by researchers in computational photography, microscopic imaging, and so on. Ordinary all-in-focus image reconstruction from a sequence of multi-focus images achieves extended depth of field, where reconstructed images would be captured through a pinhole in the center on the lens. In this paper, we propose a novel method for reconstructing all-in-focus images through shifted pinholes on the lens based on 3D frequency analysis of multi-focus images. Such shifted pinhole images are obtained by a linear combination of multi-focus images with scene-independent 2D filters in the frequency domain. The proposed method enables us to efficiently synthesize dense 4D light field on the lens plane for image-based rendering, especially, robust scene refocusing with arbitrary bokeh. Our novel method using simple linear filters achieves not only reconstruction of all-in-focus images even for shifted pinholes more robustly than the conventional methods depending on scene/focus estimation, but also scene refocusing without suffering from limitation of resolution in comparison with recent approaches using special devices such as lens arrays in computational photography. View full abstract»

    Open Access

Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003