Showing papers on "Markov random field published in 2013"

Multi-source Multi-scale Counting in Extremely Dense Crowd Images

Abstract: We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.

SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion

Abstract: Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM based on finding a coarse initial solution using hybrid discrete-continuous optimization and then improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random field (MRF) formulation, coupled with a continuous Levenberg-Marquardt refinement. The formulation naturally incorporates various sources of information about both the cameras and points, including noisy geotags and vanishing point (VP) estimates. We test our method on several large-scale photo collections, including one with measured camera positions, and show that it produces models that are similar to or better than those produced by incremental bundle adjustment, but more robustly and in a fraction of the time.

Superparsing: Scalable Nonparametric Image Parsing with Superpixels

Abstract: This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art non-parametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem.

Spectral–Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

Abstract: In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.

MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

Abstract: Deformable image registration is an important tool in medical image analysis. In the case of lung computed tomography (CT) registration there are three major challenges: large motion of small features, sliding motions between organs, and changing image contrast due to compression. Recently, Markov random field (MRF)-based discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for registration, in particular its susceptibility to local minima. However, to date the simplifications made to obtain tractable computational complexity reduced the registration accuracy. We address these challenges and preserve the potentially higher quality of discrete approaches with three novel contributions. First, we use an image-derived minimum spanning tree as a simplified graph structure, which copes well with the complex sliding motion and allows us to find the global optimum very efficiently. Second, a stochastic sampling approach for the similarity cost between images is introduced within a symmetric, diffeomorphic B-spline transformation model with diffusion regularization. The complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces. In addition to the geometric transform labels, hyper-labels are introduced, which represent local intensity variations in this task, and allow for the direct estimation of lung ventilation. We validate the improvements in accuracy and performance on exhale-inhale CT volume pairs using a large number of expert landmarks.

Identification of power distribution network topology via voltage correlation analysis

Abstract: We consider the problem of reconstructing the topology of a portion of the power distribution network, given a dataset of voltage measurements. By using an approximate model for the grid voltage magnitudes, we show that these signals exhibit some specific correlation properties, that can be described via a sparse Markov random field. By specializing the tools available for the identification of graphical models, we propose an algorithm for the reconstruction of the grid topology. Via simulations, we show how the algorithm performs well also when an exact nonlinear model of the grid voltages is adopted, when realistic power demand profiles are considered, and when the voltage measurements are affected by measurement noise.

Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification

Abstract: In the framework of remote-sensing image classification, support vector machines (SVMs) have lately been receiving substantial attention due to their accurate results in many applications as well as their remarkable generalization capability even with high-dimensional input data. However, SVM classifiers are intrinsically noncontextual, which represents an important limitation in image classification. In this paper, a novel and rigorous framework, which integrates SVMs and Markov random field models in a unique formulation for spatial contextual classification, is proposed. The developed contextual generalization of SVMs, is obtained by analytically relating the Markovian minimum-energy criterion to the application of an SVM in a suitably transformed space. Furthermore, as a second contribution, a novel contextual classifier is developed in the proposed general framework. Two specific algorithms, based on the Ho–Kashyap and Powell numerical procedures, are combined with this classifier to automate the estimation of its parameters. Experiments are carried out with hyperspectral, multichannel synthetic aperture radar, and multispectral high-resolution images and the behavior of the method as a function of the training-set size is assessed.

Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras

Abstract: The recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. The quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. The uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. The key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. The depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.

Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation

Abstract: In this paper, a new mixture model for image segmentation is presented. We propose a new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF). In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. In mixture models based on MRF, the M-step of the expectation-maximization (EM) algorithm cannot be directly applied to the prior distribution ${\pi_{ij}}$ for maximization of the log-likelihood with respect to the corresponding parameters. Compared with these models, our proposed method directly applies the EM algorithm to optimize the parameters, which makes it much simpler. Experimental results obtained by employing the proposed method on many synthetic and real-world grayscale and colored images demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models.

Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm

Abstract: This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method, the estimation of β is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating β jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of β. On the other hand, choosing an incorrect value of β can degrade estimation performance significantly. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images.

Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach

Abstract: In standard within-subject analyses of event-related functional magnetic resonance imaging (fMRI) data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based joint detection-estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a variational expectation-maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.

Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields

Abstract: Despite the recent progress in both pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object's motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.

Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs

Abstract: We introduce a novel approach for segmenting articulated spine shape models from medical images. A nonlinear low-dimensional manifold is created from a training set of mesh models to establish the patterns of global shape variations. Local appearance is captured from neighborhoods in the manifold once the overall representation converges. Inference with respect to the manifold and shape parameters is performed using a higher-order Markov random field (HOMRF). Singleton and pairwise potentials measure the support from the global data and shape coherence in manifold space respectively, while higher-order cliques encode geometrical modes of variation to segment each localized vertebra models. Generic feature functions learned from ground-truth data assigns costs to the higher-order terms. Optimization of the model parameters is achieved using efficient linear programming and duality. The resulting model is geometrically intuitive, captures the statistical distribution of the underlying manifold and respects image support. Clinical experiments demonstrated promising results in terms of spine segmentation. Quantitative comparison to expert identification yields an accuracy of 1.6 ± 0.6 mm for CT imaging and of 2.0 ± 0.8 mm for MR imaging, based on the localization of anatomical landmarks.

Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis

Abstract: This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest. First of all, to detect multiple source locations, a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity. This method can also locate and separate different signals at the same time. Second, after the camera points in the direction of the estimated angle, the proposed state-transition support vector machine is used to provide favorable discriminability for human behavior identification. A new Markov random field (MRF) function based on the localized contour sequence (LCS) is also presented while the system computes transition probabilities between states. Such LCS-based MRF functions can effectively smooth transitions and enhance recognition. The experimental results show that the average error of DOA decreases to around 7°, which is better than those of the baselines. Also, our proposed behavior identification system can reach an 88.3% accuracy rate. The aforementioned results have therefore demonstrated the feasibility of the proposed method.

Characterness: An Indicator of Text in the Wild

Abstract: Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. Despite its value, however, identifying general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem which reflects the `characterness' of an image region. In this sense our approach mirrors the move from saliency detection methods to measures of `objectness'. In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field (MRF) model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multi-cue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection datasets. We also show that our measurement of `characterness' is superior than state-of-the-art saliency detection models when applied to the same task.

Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

Abstract: In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.

Markov Random Field Structures for Facial Action Unit Intensity Estimation

Abstract: We present a novel Markov Random Field (MRF) structure-based approach to the problem of facial action unit (AU) intensity estimation. AUs generally appear in common combinations, and exhibit strong relationships between the intensities of a number of AUs. The aim of this work is to harness these links in order to improve the estimation of the intensity values over that possible from regression of individual AUs. Our method exploits Support Vector Regression outputs to model appearance likelihoods of each individual AU, and integrates these with intensity combination priors in MRF structures to improve the overall intensity estimates. We demonstrate the benefits of our approach on the upper face AUs annotated in the DISFA database, with significant improvements seen in both correlation and error rates for the majority of AUs, and on average.

A Hybrid Multiview Stereo Algorithm for Modeling Urban Scenes

Abstract: We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial mesh-based surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.

Guided Depth Enhancement via Anisotropic Diffusion

Abstract: In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar's range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent.

Context-Based Hierarchical Unequal Merging for SAR Image Segmentation

Abstract: This paper presents an image segmentation method named Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequal merging algorithm is designed, which includes two stages: 1) coarse merging stage and 2) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy. Compared with two state-of-the-art Markov random field models, CHUMSIS can obtain good segmentation results and successfully reduce running time.

Modeling Natural Images Using Gated MRFs

Abstract: This paper describes a Markov Random Field for real-valued image modeling that has two sets of latent variables. One set is used to gate the interactions between all pairs of pixels, while the second set determines the mean intensities of each pixel. This is a powerful model with a conditional distribution over the input that is Gaussian, with both mean and covariance determined by the configuration of latent variables, which is unlike previous models that were restricted to using Gaussians with either a fixed mean or a diagonal covariance matrix. Thanks to the increased flexibility, this gated MRF can generate more realistic samples after training on an unconstrained distribution of high-resolution natural images. Furthermore, the latent variables of the model can be inferred efficiently and can be used as very effective descriptors in recognition tasks. Both generation and discrimination drastically improve as layers of binary latent variables are added to the model, yielding a hierarchical model called a Deep Belief Network.

Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Abstract: Subpixel mapping (SPM) is a promising technique to increase the spatial resolution of land cover maps. Markov random field (MRF)-based SPM has the advantages of considering spatial and spectral constraints simultaneously. In the conventional MRF, only the spectral information of one observed coarse spatial resolution image is utilized, which limits the SPM accuracy. In this letter, supplementary information from subpixel shifted remote sensing images (SSRSI) is used with MRF to produce more accurate SPM results. That is, spectral information from SSRSI is incorporated into the likelihood energy function of MRF to provide multiple spectral constraints. Simulated and real images were tested with the subpixel/pixel spatial attraction model, Hopfield neural networks (HNNs), HNN with SSRSI, image interpolation then hard classification, conventional MRF, and proposed MRF with SSRSI based SPM methods. Results showed that the proposed method can generate the most accurate SPM results among these methods.

A Graph-Based Classification Method for Hyperspectral Images

Abstract: The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method.

A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk

Abstract: We present a methodological framework for multichannel Markov random fields (MRFs). We show that conditional independence allows loopy belief propagation to solve a multichannel MRF as a single channel MRF. We use conditional mutual information to search for features that satisfy conditional independence assumptions. Using this framework we incorporate kinetic feature maps derived from breast dynamic contrast enhanced magnetic resonance imaging as observation channels in MRF for tumor segmentation. Our algorithm based on multichannel MRF achieves an receiver operating characteristic area under curve (AUC) of 0.97 for tumor segmentation when using a radiologist's manual delineation as ground truth. Single channel MRF based on the best feature chosen from the same pool of features as used by the multichannel MRF achieved a lower AUC of 0.89. We also present a comparison against the well established normalized cuts segmentation algorithm along with commonly used approaches for breast tumor segmentation including fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES). These previous methods give a lower AUC of 0.92, 0.88, and 0.60, respectively. Finally, we also investigate the role of superior segmentation in feature extraction and tumor characterization. Specifically, we examine the effect of improved segmentation on predicting the probability of breast cancer recurrence as determined by a validated tumor gene expression assay. We demonstrate that an support vector machine classifier trained on kinetic statistics extracted from tumors as segmented by our algorithm gives a significant improvement in distinguishing between women with high and low recurrence risk, giving an AUC of 0.88 as compared to 0.79, 0.76, 0.75, and 0.66 when using normalized cuts, single channel MRF, FCM, and FCM-VES, respectively, for segmentation.

Fast and robust optical flow for time-lapse microscopy using super-voxels

Abstract: Motivation: Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. Results: We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10 × faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50% in regions with complex dynamic processes, such as cell divisions. Availability: Source code freely available in the Software section at http://janelia.org/lab/keller-lab. Contact:amatf@janelia.hhmi.org or kellerp@janelia.hhmi.org Supplementary information:Supplementary data are available at Bioinformatics online.