Posterior shape models

We present a method to compute the conditional distribution of a statistical shape model given partial data. The result is a "posterior shape model", which is again a statistical shape model of the same form as the original model. This allows its direct use in the variety of algorithms that include prior knowledge about the variability of a class of shapes with a statistical shape model. Posterior shape models then provide a statistically sound yet easy method to integrate partial data into these algorithms. Usually, shape models represent a complete organ, for instance in our experiments the femur bone, modeled by a multivariate normal distribution. But because in many application certain parts of the shape are known a priori, it is of great interest to model the posterior distribution of the whole shape given the known parts. These could be isolated landmark points or larger portions of the shape, like the healthy part of a pathological or damaged organ. However, because for most shape models the dimensionality of the data is much higher than the number of examples, the normal distribution is singular, and the conditional distribution not readily available. In this paper, we present two main contributions: First, we show how the posterior model can be efficiently computed as a statistical shape model in standard form and used in any shape model algorithm. We complement this paper with a freely available implementation of our algorithms. Second, we show that most common approaches put forth in the literature to overcome this are equivalent to probabilistic principal component analysis (PPCA), and Gaussian Process regression. To illustrate the use of posterior shape models, we apply them on two problems from medical image analysis: model-based image segmentation incorporating prior knowledge from landmarks, and the prediction of anatomically correct knee shapes for trochlear dysplasia patients, which constitutes a novel medical application. Our experiments confirm that the use of conditional shape models for image segmentation improves the overall segmentation accuracy and robustness

Finite element surface registration incorporating curvature, volume preservation, and statistical model information

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models

A versatile maskless microscope projection photolithography system and its application in light-directed fabrication of DNA microarrays

We present a maskless microscope projection lithography system (MPLS), in which photomasks have been replaced by a Digital Micromirror Device type spatial light modulator (DMD, Texas Instruments). Employing video projector technology high resolution patterns, designed as bitmap images on the computer, are displayed using a micromirror array consisting of about 786000 tiny individually addressable tilting mirrors. The DMD, which is located in the image plane of an infinity corrected microscope, is projected onto a substrate placed in the focal plane of the microscope objective. With a 5x(0.25 NA) Fluar microscope objective, a fivefold reduction of the image to a total size of 9 mm2 and a minimum feature size of 3.5 microns is achieved. Our system can be used in the visible range as well as in the near UV (with a light intensity of up to 76 mW/cm2 around the 365 nm Hg-line). We developed an inexpensive and simple method to enable exact focusing and controlling of the image quality of the projected patterns. Our MPLS has originally been designed for the light-directed in situ synthesis of DNA microarrays. One requirement is a high UV intensity to keep the fabrication process reasonably short. Another demand is a sufficient contrast ratio over small distances (of about 5 microns). This is necessary to achieve a high density of features (i.e. separated sites on the substrate at which different DNA sequences are synthesized in parallel fashion) while at the same time the number of stray light induced DNA sequence errors is kept reasonably small. We demonstrate the performance of the apparatus in light-directed DNA chip synthesis and discuss its advantages and limitations.Comment: 12 pages, 9 figures, journal articl

A vision system for mobile maritime surveillance platforms

Mobile surveillance systems play an important role to minimise security and safety threats in high-risk or hazardous environments. Providing a mobile marine surveillance platform with situational awareness of its environment is important for mission success. An essential part of situational awareness is the ability to detect and subsequently track potential target objects.Typically, the exact type of target objects is unknown, hence detection is addressed as a problem of finding parts of an image that stand out in relation to their surrounding regions or are atypical to the domain. Contrary to existing saliency methods, this thesis proposes the use of a domain specific visual attention approach for detecting potential regions of interest in maritime imagery. For this, low-level features that are indicative of maritime targets are identified. These features are then evaluated with respect to their local, regional, and global significance. Together with a domain specific background segmentation technique, the features are combined in a Bayesian classifier to direct visual attention to potential target objects.The maritime environment introduces challenges to the camera system: gusts, wind, swell, or waves can cause the platform to move drastically and unpredictably. Pan-tilt-zoom cameras that are often utilised for surveillance tasks can adjusting their orientation to provide a stable view onto the target. However, in rough maritime environments this requires high-speed and precise inputs. In contrast, omnidirectional cameras provide a full spherical view, which allows the acquisition and tracking of multiple targets at the same time. However, the target itself only occupies a small fraction of the overall view. This thesis proposes a novel, target-centric approach for image stabilisation. A virtual camera is extracted from the omnidirectional view for each target and is adjusted based on the measurements of an inertial measurement unit and an image feature tracker. The combination of these two techniques in a probabilistic framework allows for stabilisation of rotational and translational ego-motion. Furthermore, it has the specific advantage of being robust to loosely calibrated and synchronised hardware since the fusion of tracking and stabilisation means that tracking uncertainty can be used to compensate for errors in calibration and synchronisation. This then completely eliminates the need for tedious calibration phases and the adverse effects of assembly slippage over time.Finally, this thesis combines the visual attention and omnidirectional stabilisation frameworks and proposes a multi view tracking system that is capable of detecting potential target objects in the maritime domain. Although the visual attention framework performed well on the benchmark datasets, the evaluation on real-world maritime imagery produced a high number of false positives. An investigation reveals that the problem is that benchmark data sets are unconsciously being influenced by human shot selection, which greatly simplifies the problem of visual attention. Despite the number of false positives, the tracking approach itself is robust even if a high number of false positives are tracked

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Smart Kitchens for People with Cognitive Impairments: A Qualitative Study of Design Requirements

Individuals with cognitive impairments currently leverage extensive human resources during their transitions from assisted living to independent living. In Western Europe, many government-supported volunteer organizations provide sheltered living facilities; supervised environments in which people with cognitive impairments collaboratively learn daily living skills. In this paper, we describe communal cooking practices in sheltered living facilities and identify opportunities for supporting these with interactive technology to reduce volunteer workload. We conducted two contextual observations of twelve people with cognitive impairments cooking in sheltered living facilities and supplemented this data through interviews with four employees and volunteers who supervise them. Through thematic analysis, we identified four themes to inform design requirements for communal cooking activities: Work organization, community, supervision, and practicalities. Based on these, we present five design implications for assistive systems in kitchens for people with cognitive deficiencies