With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.

Myocardial deformation imaging can provide valuable insights in myocardial mechanics and help in the diagnosis, prognosis and follow-up of cardiac diseases. However, extracting these indices in 3D is challenging due to the limitations in spatial and temporal resolution of the current volumetric ultrasound systems. For this purpose, we developed an anatomical free-form deformation image registration framework which is locally adapted to the anatomy of the heart. In this work we explored whether incorporating a myocardial volume conservation regularizer would improve strain estimates. We evaluated our technique on in silico echo sequences featuring realistic speckle textures and showed the volume conservation regularizer to be beneficial in reducing strain errors further when used in combination with a smoothness penalty. This combination led to more physiological boundary conditions. It also made distinguishing ischemic from normal segments easier in clinical images.

Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.

Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial soft tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes.

In this paper the problem of pairwise model-to-scene point set registration is considered. Three contributions are made. Firstly, the relations between correspondence-based and some information-theoretic point cloud registration algorithms are formalized. Starting from the observation that the outlier handling of existing methods relies on heuristically determined models, a second contribution is made exploiting aforementioned relations to derive a new robust point set registration algorithm. Representing model and scene point clouds by mixtures of Gaus-sians, the method minimizes their Kullback-Leibler divergence both w.r.t. the registration transformation parameters and w.r.t. the scene's mixture coefficients. This results in an Expectation-Maximization Iterative Closest Point (EM-ICP) approach with a parameter-free outlier model that is optimal in information-theoretical sense. While the current (CUDA) implementation is limited to the rigid registration case, the underlying theory applies to both rigid and non-rigid point set registration. As a by-product of the registration algorithm's theory, a third contribution is made by suggesting a new point cloud Kernel Density Estimation approach which relies on maximizing the resulting distribution's entropy w.r.t. the kernel weights. The rigid registration algorithm is applied to align different patches of the publicly available Stanford Dragon and Stanford Happy Budha range data. The results show good performance regarding accuracy, robustness and convergence range.

Since most 3D cameras cannot capture the complete 3D face, an important challenge in 3D face recognition is the comparison of two 3D facial surfaces with little or no over lap. In this paper, a local feature method is presented to tackle this challenge exploiting the symmetry of the human face. Features are located and described using an extension of SIFT for meshes (meshSIFT). As such, features are localized as extrema in the curvature scale space of the in put mesh, and are described by concatenating histograms of shape indices and slant angles of the neighborhood. For 3D face scans with sufficient overlap, the number of matching meshSIFT features is a reliable measure for face recognition purposes. However, as the feature descriptor is not symmetrical, features on one face are not matched with their symmetrical counterpart on another face impeding their feasibility for comparison efface scans with limited or no (left-right) overlap. In order to alleviate this problem, facial symmetry could be used to increase the overlap between two face scans by mirroring one of both faces w.r.t. an arbitrary plane. As this would increase the computational demand, this paper proposes an efficient approach to de scribe the features of a mirrored face by mirroring the mesh SIFT descriptors of the input face. The presented method is validated on the data of the "SHREC '11: Face Scans" contest, containing many partial scans. This resulted in a recognition rate of 98.6% and a mean average precision of 93.3%, clearly outperforming all other participants in the challenge.

The recognition of faces under varying expressions is one of the current challenges in the face recognition community. In this paper, we propose a method fusing different complementary approaches each dealing with expression variations. The first approach uses an isometric deformation model and is based on the largest singular values of the geodesic distance matrix as an expression-invariant shape descriptor. The second approach performs recognition on the more rigid parts of the face that are less affected by expression variations. Several fusion techniques are examined for combining the approaches. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 5.85% for the verification scenario and a rank 1 recognition rate of 94.48% for the identification scenario using the sum rule as fusion technique. This result outperforms other 3D expression-invariant face recognition methods on the same database.

Magnetic resonance (MR) cine images are often used to clinically assess left ventricular cardiac function. In a typical study, multiple 2-D long axis (LA) and short axis (SA) cine images are acquired, each in a different breath-hold. Differences in lung volume during breath-hold and overall patient motion distort spatial alignment of the images thus complicating spatial integration of all image data in three dimensions. We present a fully automatic postprocessing approach to correct these slice misalignments. The approach is based on the constrained optimization of the intensity similarity of intersecting image lines after the automatic definition of a region of interest. It uses all views and all time frames simultaneously. Our method models both in-plane and out-of-plane translations and full 3-D rotations, can be applied retrospectively and does not require a cardiac wall segmentation. The method was validated on both healthy volunteer and patient data with simulated misalignments, as well as on clinical multibreath-hold patient data. For the simulated data, subpixel accuracy could be obtained using translational correction. The possibilities and limitations of rotational correction were investigated and discussed. For the clinical multibreath-hold patient data sets, the median discrepancy between manual SA and LA contours was reduced from 2.83 to 1.33 mm using the proposed correction method. We have also shown the usefulness of the correction method for functional analysis on clinical image data. The same clinical multibreath-hold data sets were resegmented after positional correction, taking newly available complementary information of intersecting slices into account, further reducing the median discrepancy to 0.43 mm. This is due to the integration of the 2-D slice information into 3-D space.

During the registration of 3D CAD models of metallic knee implant components to single-plane sagittal fluoroscopy images, the 3D pose of each implant component is estimated by maximizing the similarity between its 2D image appearance and the observed fluoroscopy image. Because knee implant components are highly symmetrical with respect to the sagittal plane, two significantly different model poses result in 2D image projections very similar to an observed sagittal fluoroscopy image. Traditional 2D/3D registration algorithms tend to converge to one of these symmetrical poses discarding the other one. This paper presents a method which simultaneously estimates both symmetrical solutions. In order not to limit the proposed method to 3D models with an exact plane of symmetry, a completely data-driven symmetry constraint is used which is imposed to the estimated pose parameters. The algorithm is embedded in a statistical framework which is optimized using a deterministic annealing expectation-maximization approach. The validity of the method is demonstrated by registration of the tibial knee implant component to real and simulated fluoroscopy images.