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3D human pose estimation is a very difficult task. In this paper we propose that this problem can be more easily solved by first finding the solutions to a set of easier sub-problems. These are to locally estimate pose conditioned on a fixed root node state, which defines the global position and orientation of the person. The global solution can then be found using information extracted during this procedure. This approach has two key benefits: The first is that each local solution can be found by modeling the articulated object as a kinematic chain, which has far less degrees of freedom than alternative models. The second is that by using this approach we can represent, or support, a much larger area of the posterior than is currently possible. This allows far more robust algorithms to be implemented since there is far less pressure to prune the search space to free up computational resources. We apply this approach to two problems: The first is single frame monocular 3D pose estimation, where we propose a method to directly extract 3D pose without first extracting any intermediate 2D representation or being dependent on strong spatial prior models. The second is multi-view 3D tracking where we show that using the above technique results in an approach that is far more robust than current approaches, without relying on strong temporal prior models. In both domains we demonstrate the strength and versatility of the proposed method. A new approach to searching global solution by first finding a set of local solutionsApplied to both monocular 3D pose detection and multiview 3D trackingAchieve good accuracy using weak likelihood model and no assumed motion modelSource code is publically available to ensure reproducible results.

The geometric potential force (GPF) used in segmentation of medical images is in general a robust method. However, calculation of the GPF is often time consuming and slow. In the present work, we propose several methods for improving the GPF calculation and evaluate their efficiency against the original method. Among different methods investigated, the procedure that combines Riesz transform and integration by part provides the fastest solution. Both static and dynamic images have been employed to demonstrate the efficacy of the proposed methods.

Summary Image-based non-invasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFRro) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRro was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR3D). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R = 0.885, P < 0.01), when using cFFR3D as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFRro and cFFR3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses. This article is protected by copyright. All rights reserved.

A novel fully automated graph cut method to segment IVUS and OCT images.Shape priors to impose global and local shape constraints.Adaptive cost function based on data driven artifact classification.Tracking integrated into graph segmentation to efficiently impose temporal consistency. We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels. The proposed approach is applied to IVUS and OCT images. Both qualitative and quantitative results show superior performance of the proposed method compared to a number of alternative segmentation techniques.

International audience; We address the problem of object modeling from 3D and 3D+T data made up of images which contain different parts of an object of interest, are separated by large spaces, and are misaligned with respect to each other. These images have only a limited number of intersections, hence making their registration particularly challenging. Furthermore, such data may result from various medical imaging modalities and can therefore present very diverse spatial configurations. Previous methods perform registration and object modeling (segmentation and interpolation) sequentially. However, sequential registration is ill-suited for the case of images with few intersections. We propose a new methodology which, regardless of the spatial configuration of the data, performs the three stages of registration, segmentation, and shape interpolation from spaced and misaligned images simultaneously. We integrate these three processes in a level set framework, in order to benefit from their synergistic interactions. We also propose a new registration method that exploits segmentation information rather than pixel intensities, and that accounts for the global shape of the object of interest, for increased robustness and accuracy. The accuracy of registration is compared against traditional mutual information based methods, and the total modeling framework is assessed against traditional sequential processing and validated on artificial, CT, and MRI data.

We present a study in level set representation and evolution using radial basis functions (RBFs) for active contour and active surface models. It builds on recent works by others who introduced RBFs into level sets for structural topology optimisation. Here, we introduce the concept into deformable models and present a new level set formulation able to handle more complex topological changes, in particular perturbation away from the evolving front. In the conventional level set technique, the initial active contour/surface is implicitly represented by a signed distance function and periodically re-initialised to maintain numerical stability. We interpolate the initial distance function using RBFs on a much coarser grid, which provides great potential in modelling in high dimensional space. Its deformation is considered as an updating of the RBF interpolants, an ordinary differential equation (ODE) problem, instead of a partial differential equation (PDE) problem, and hence it becomes much easier to solve. Re-initialisation is found no longer necessary, in contrast to conventional finite difference method (FDM) based level set approaches. The proposed level set updating scheme is efficient and does not suffer from self-flattening while evolving, hence it avoids large numerical errors. Further, more complex topological changes are readily achievable and the initial contour or surface can be placed arbitrarily in the image. These properties are extensively demonstrated on both synthetic and real 2D and 3D data. We also present a novel active contour model, implemented with this level set scheme, based on multiscale learning and fusion of image primitives from vector-valued data, e.g. colour images, without channel separation or decomposition.

A statistical shape model that accurately generalizes a family of 3D shapes requires establishing correspondences across the set of shapes. However in 3D anatomical meshes, finding a sufficient number of landmarks to accurately describe the shape can be a challenge, and often only a few points are easily identifiable due to the smooth nature of the object surface. Using a sparse set of landmarks, this paper finds a dense set of vertex correspondences across a set of 3D aortic root meshes. This is achieved by non-rigidly transforming a source mesh to a target mesh. Then, for every vertex on the target, a corresponding vertex on the deformed source is found, resulting in complete correspondence. A more accurate transformation results in better correspondence establishment, and our mesh registration experiments show an average Hausdorff distance of 3.65mm, and an average point-to-mesh distance of 0.41mm, i. e. within one voxel.

We present a novel HMM based approach to simultaneous segmentation of vessel walls in Lymphatic confocal images. The vessel borders are parameterized using RBFs to minimize the number of tracking points. The proposed method tracks the hidden states that indicate border locations for both the inner and outer walls. The observation for both borders is obtained using edge-based features from steerable filters. Two separate Gaussian probability distributions for the vessel borders and background are used to infer the emission probability, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into a minimization of an s-excess graph cost, with each node in the graph corresponding to a hidden state and the weight for each node being defined by its emission probability. We define the inter-relations between neighboring nodes based on the transmission probability. We present both qualitative and quantitative analysis in comparison to the popular Viterbi algorithm.

In this paper, we propose a unified approach to deformable model-based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes the full use of directional information of the image gradient vectors and their interactions across image domain. However, instead of directly using such a vector field for deformable segmentation as in the conventional approaches, we derive a more salient representation for contour evolution, and very importantly, we demonstrate that this representation of image force field not only leads to global minimum through convex relaxation but also can achieve the same result using the conventional gradient descent with an intrinsic regularization. Thus, the proposed method can handle arbitrary initializations. The proposed external force field for deformable segmentation has both edge-based properties in that the GCF is computed from image gradients, and the region-based attributes since its divergence can be treated as a region indication function. Moreover, nonlinear diffusion can be conveniently applied to GCF to improve its performance in dealing with noise interference. We also show the extension of GCF from 2D to 3D. In comparison to the state-of-the-art deformable segmentation techniques, the proposed method shows greater flexibility in model initialization and optimization realization, as well as better performance toward noise interference and appearance variation.

We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.

In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth.

This paper proposes an adaptive denoising method that can significantly reduce Rician noise in magnetic resonance imag- ing (MRI). We use a Rayleigh kernel in the denoising pro- cessing of self-snakes instead of the Gaussian kernel that is usually used. The Rayleigh kernel is adaptively constructed according to the estimated standard deviation of Rician noise in images. The numerical implementation is carried out by applying the level-set techniques with a semi-implicit scheme. Experimental results in both synthetic and real im- ages demonstrate the effectiveness and advantages of the proposed method in comparison with the traditional methods.

In this paper, we present a graph based segmentation method that only requires a single point from user initialization. We incorporate a new image feature into the segmentation scheme. It is derived from a vector field that takes into account gradient vector interactions across the image domain, and has the simplicity of edge based features but also proves to be a useful region indication in two-level segmentation. Effective vector field diffusion is proposed to deal with excessive image noise. Based on a single user point we unravel the image and transfer the object segmentation into a height field segmentation in polar coordinates, which in effect imposes a star shape prior. The search of a minimum closed set on a node weighted, directed graph produces the segmentation result. Comparative analysis on real world images demonstrates promising performances of the proposed method in segmentation accuracy and its simplicity in user interaction.

Facial fiducial point localization is a crucial step for most facial analysis applications, e.g., face recognition, expression recognition and facial aging simulation. Although state-of-art methods have the ability to provide good salient point location on frontal faces, finding a global solution under large variations caused by off-plane rotations and exaggerated expression changes is still a challenge. In this paper, we present a system with a two-level shape model to facilitate accurate facial fiducial point localization. In the first level, two local component models interact with each other in order to offer novel shape constraints. At the same time, the clamped local shape model provides constrained non-linear shape initialization for better convergence performance of the shape model as a whole. The experimental results confirm that the proposed method is capable of dealing with the face alignment under large shape variations.

We present a shape prior based graph cut method which does not require user initialisation. The shape prior is generalised from multiple training shapes, rather than using singular templates as priors. Weighted directed graph construction is used to impose geometrical and smooth constraints learned from priors. The proposed cost function is built upon combining selective feature extractors. A SVM classifier is used to determine an optimal combination of features in presence of calcification, fibrotic tissues, soft plaques, and metallic stent, each of which has its own characteristics in ultrasound images. Comparative analysis on manually labelled ground-truth shows superior performance of the proposed method compared to conventional graph cut methods.

The authors propose a novel spatiotemporal constraint based on shape and appearance and combine it with a level-set deformable model for left ventricle (LV) segmentation in four-dimensional gated cardiac SPECT, particularly in the presence of perfusion defects. The model incorporates appearance and shape information into a ‘soft-to-hard’ probabilistic constraint, and utilises spatiotemporal regularisation via a maximum a posteriori framework. This constraint force allows more flexibility than the rigid forces of shape constraint-only schemes, as well as other state of the art joint shape and appearance constraints. The combined model can hypothesise defective LV borders based on prior knowledge. The authors present comparative results to illustrate the improvement gain. A brief defect detection example is finally presented as an application of the proposed method.

International audience; We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans, and is compared against the traditional sequential approach which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach, but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

Representing articulated objects as a graphical model has gained much popularity in recent years, often the root node of the graph describes the global position and orientation of the object. In this work a method is presented to robustly track 3D human pose by permitting greater uncertainty to be modeled over the root node than existing techniques allow. Significantly, this is achieved without increasing the uncertainty of remaining parts of the model. The benefit is that a greater volume of the posterior can be supported making the approach less vulnerable to tracking failure. Given a hypothesis of the root node state a novel method is presented to estimate the posterior over the remaining parts of the body conditioned on this value. All probability distributions are approximated using a single Gaussian allowing inference to be carried out in closed form. A set of deterministically selected sample points are used that allow the posterior to be updated for each part requiring just seven image likelihood evaluations making it extremely efficient. Multiple root node states are supported and propagated using standard sampling techniques. We believe this to be the first work devoted to efficient tracking of human pose whilst modeling large uncertainty in the root node and demonstrate the presented method to be more robust to tracking failures than existing approaches.

We present a 3D deformable model that is based on a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based on hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so as to facilitate topological changes. The proposed external force field can attract the deformable model to object boundaries with arbitrary initialization, and allows the deformable model to reach highly concave regions which are generally difficult for other methods. We provide a comparative study on the segmentation of various geometries in real 3D images, and show that the proposed method achieves significant improvements against existing image gradient techniques. Index Terms—Deformable model, 3D image segmentation, biomedical image processing, level set, energy minimization. I. INTRODUCTION Segmenting volumetric biomedic data is an intricate pro- cess, due to the complexity and variability of image data and shapes. There have been applications of simple techniques such as thresholding and region growing in the extraction of 3D objects from volumetric images. However, these techniques are very sensitive to noise and intensity inhomogeneities which exist in real images, and often produce leakages and regions which are not contiguous. Atlas based approaches perform segmentation based on image registration techniques, whereby an image can be segmented by finding a transformation that maps a template image to the target image. It is however generally difficult to accurately extract complex geometries due to the variability of anatomical structures. Statistical ap- proaches (1) are also used to identify different tissue structures from medical images. It usually involves manual interaction to segment images in order to obtain a sufficiently large set of training samples. Such strategies are often restricted to problems where there is sufficient prior knowledge about the shape or appearance variations of the relevant structures. Deformable models can be an effective alternative approach. They are usually based on a variational framework to minimize an energy functional defined on a continuous contour or surface. They have the ability to adapt to complex shape variations and to incorporate priors to regularize segmentation. Curves or surfaces evolve under the influence of both internal and external forces to extract the image object boundaries. The design of deformable models often varies in the representation of the object boundary and external force field used. These

3D Human pose estimation from a single monocular image is an extremely difficult problem. Currently there are two main approaches to solving this problem, the first is to learn a direct mapping from image features to 3D pose [1], the second is to first extract 2D pose as an intermediate stage and then ‘lift’ this to a 3D pose [2]. The limitation with both of these approaches is that they are only applicable to poses that are similar to those represented in the original training set, e.g. walking. It is unlikely they will scale to extract arbitrary 3D poses. Contrary to this, in the domain of 2D pose estimation current state-of-the-art methods have been shown capable of detecting poses that are much more varied [3]. This has been achieved using generative models built around the Pictorial Structures representation that decomposes pose estimation into a search across individual parts [4]. In this paper we present a generative method to extract 3D pose from single images using a part based representation. The method is stochastic, though in contrast to methods used for 3D tracking (e.g. the particle filter), where the search space in each frame is tightly constrained by previous observations, in single image pose estimation the search space is much larger. To permit a search over this space a generative prior model is learnt from motion capture data. Stochastic samples are used to approximate this prior and to facilitate its update. In effect, the initial prior is iteratively deformed to the posterior distribution. The body is represented by a set of ten parts, each part has a fixed length and connected parts are forced to join at fixed locations. The conditional distribution between two connected parts is modeled by first learning a joint distribution using a GMM p(xi,x j∣θi j), where xi and x j is the state of the ith and jth part respectively and θi j is the set of model parameters. As each model is represented using a GMM the model parameters are defined as θi j = {λ k i j,μ i j,Σi j}k=1, where K is the number of components in the model and λ k i j,μ k i j,Σ k i j represent the kth component’s weight, mean and covariance respectively. For efficiency all covariances used to represent limb conditionals are diagonal and can be partitioned such that Σi j = diag(Λ k ii,Λ k j j) and likewise μ k i j = ( μk i ,μ k j ) . Given a value for x j (e.g. a sample) the conditional distribution p(xi∣x j,θ k i j) is first calculated from the joint distribution p(xi,x j∣θi j), following which a sample xi can be drawn from it. The conditional distribution, p(xi∣x j,θ k i j), is also a GMM with parameters {λ k i ,μk i ,Λii}k=1. The component weights are proportional to the marginal distribution λ k i ∝ p(x j∣θ k i j), which is calculated from the normal distribution p(x j∣θ k i j) = λ k i jN (x j; μj ,Λj j). Note this conditional model is different to typical approximations used, when the conditional model is approximated by p(xi j∣θi j), where xi j is the value of xi in the local frame of reference of x j [3]. A benefit of learning a full conditional model between neighboring parts is that different GMM components learnt in quaternion space correspond to different spatial locations in R3. This is illustrated in Fig. 1 where it can clearly be seen that this representation can clearly capture multiple modes.

We propose a new level set segmentation method with statistical shape prior using a variational approach. The image energy is derived from a robust image gradient feature. This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the model to handle relatively large shape variations. Comparative examples using both synthetic and real images show the robustness and efficiency of the proposed method.

We present a region based active contour model which does not require any initialisation and is capable of modelling multi-modal image regions. Its external force is based on statistically learning and grouping of image primitives in multiscale, and its numerical solution is carried out using radial basis function interpolation and time dependent expansion coefficient updating. The initialisation-free property makes it attractive to applications such as detecting unkown number of objects with unkown topologies.

We propose a level set based framework to segment textured images. The snake deforms in the image domain in searching for object boundaries by minimizing an energy functional, which is defined based on dynamically selected local distribution of orientation invariant features. We also explore the user initialization to simplify the segmentation and improve accuracy. Experimental results on both synthetic and real data show significant improvements compared to direct modeling of filtering responses or piecewise constant modeling.

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

In this paper, we propose a novel level set based active contour model to segment textured images. The proposed method is based on the assumption that local histograms of filtering responses between foreground and background regions are statistically separable. In order to be able to handle texture non-uniformities, which often occur in real world images, we use rotation invariant filtering features and local spectral histograms as image feature to drive the snake segmentation. Automatic histogram bin size selection is carried out so that its underlying distribution can be best represented. Experimental results on both synthetic and real data show promising results and significant improvements compared to direct modeling based on filtering responses.

In this paper an approach is described to estimate 3D pose using a part based stochastic method. A proposed representation of the human body is explored defined over joints that employs full conditional models learnt between connected joints. This representation is compared against a popular alternative defined over parts using approximated limb conditionals. It is shown that using full limb conditionals results in a model that is far more representative of the original training data. Furthermore, it is demonstrated that Expectation Maximization is suitable for estimating 3D pose and better convergence is achieved when using full limb conditionals. To demonstrate the efficacy of the proposed method it is applied to the domain of 3D pose estimation using a single monocular image. Quantitative results are provided using the HumanEva dataset which confirm that the proposed method outperforms that of the competing part based model. In this work just a single model is learnt to represent all actions contained in the dataset which is applied to all subjects viewed from differing angles.

This paper presents an extension of our recently introduced MAC model to deal with the initialization dependency problem that commonly appears in edge-based approaches. Its dynamic force field, unique bidirectionality, and constrained diffusion-based level set evolution provide great freedom in contour initialization and show significant improvements in initialization independency compared to other edge-based techniques. It can handle more sophisticated topological changes than splitting and merging. It provides new potentials for edge-based active contour methods, particularly when detecting and localizing objects with unknown location, geometry, and topology.

We propose a new external force field for deformable models which can be conveniently generalized to high dimensions. The external force field is based on hypothesized interactions between the relative geometries of the deformable model and image gradients. The evolution of the deformable model is solved using the level set method. The dynamic interaction forces between the geometries can greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and in dealing with weak image edges. The new deformable model can handle arbitrary cross-boundary initializations. Here, we show that the proposed method achieve significant improvements when compared against existing state-of-the-art techniques.

We present segmentation and tracking of deformable objects using non-linear on-line learning of high-level shape information in the form of a level set function. The emphasis is for successful tracking of objects that undergo smooth arbitrary deformations, but without the a priori learning of shape constraints. The high-level shape information is learnt on-line by defining a memory of object samples in a high-dimensional shape space. These shape samples are then used as weights via a locally defined shape space kernel function to define a template against which potential future shapes of the tracked object can be compared. Results for the successful tracking of a range of deformable motions are presented.

We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges, and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of gradient vector flow (GVF) snake, the combined geodesic and GVF snake, and the charged particle model.

An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with dynamic photometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the object in each new frame, incorporating Procrustes shape alignment. Promising results under complex deformations of shape, varied levels of noise, and close-to-complete occlusion in complex textured backgrounds are presented.

We propose a novel active contour model by incorporating particle based electrostatic interactions into the geometric active contour framework. The proposed active contour, embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Unlike other contour models, the proposed vector field dynamically adapts by updating itself when a contour reaches a boundary. The model is then more invariant to initialisation and possesses better convergence abilities. Analytical and comparative results are presented on synthetic and real images.

A new external velocity field for active contours is proposed. The velocity field is based on magnetostatics and hypothesised magnetic interactions between the active contour and image gradients. In this paper, we introduce the method and study its convergence capability for the recovery of shapes with complex topology and geometry, including deep, narrow concavities. The proposed active contour can be arbitrarily initialised. Level sets are used to achieve topological freedom. The proposed method is compared against shape recovery methods based on distance vector flow, constant flow, generalised version of GVF, geodesic GGVF, and curvature vector flow.

An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the Region-aided Geometric Snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images.

A novel method is presented to detect defects in random colour textures which requires only a very few normal samples for unsupervised training. We decorrelate the colour image by generating three eigenchannels in each of which the surface texture image is divided into overlapping patches of various sizes. Then, a mixture model and EM is applied to reduce groupings of patches to a small number of textural exemplars, or texems. Localised defect detection is achieved by comparing the learned texems to patches in the unseen image eigenchannels.

We present a multidimensional histogram method to inspect tonality on colour textured surfaces, e.g. ceramic tiles. Comparison in the noise dominated chromatic channels is error prone. We perform vector- ordered colour smoothing and generate a PCA-based reconstruction of a query tile based on a reference tile eigenspace. Histograms of local feature vectors are then compared for tonality defect detection. The proposed method is compared and evaluated on a data set with groundtruth.

Segment-wise matching is an important research problem that supports higher-level understanding of\ud shapes in geometry processing. Many existing segment-wise matching techniques assume perfect input segmentation,\ud and would suer from imperfect or over-segmented input. To handle this shortcoming, we propose\ud multi-layer graphs (MLGs) to represent possible arrangements of partially merged segments of input shapes.\ud We then adapt the diusion pruning technique on the MLGs to nd consistent segment-wise matching. To\ud obtain high quality matching, we develop a voting step to nd hierarchically consistent correspondences as\ud nal output. We evaluate our technique with both qualitative and quantitative experiments on both manmade\ud and deformable shapes. Experimental results demonstrate the eectiveness of our technique when\ud compared to two state-of-the-art methods.

The standardgeometric orgeodesic active contourisa powerful segmentation method, yet it is susceptible to weak edges and image noise. We propose a new region-aided, geometric, colour active contour that integrates gradient flow forces with region constraints. These constraints are composed of image region vector flow forces obtained through the diffusion of the region segmentation map. The extra region force gives the snake a global view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using the level set approach.

A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the distribution of currently estimated foreground pixels; (b) the ratio of the background distribution to the model distribution for all estimated background pixels. This approach provides robust discrimination to identify the division between foreground and background pixels, which is useful for applications such as object tracking.