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Cortical thickness is a more reliable measure of atrophy than volume due to the low variability in the cytoarchitectural structure of the grey matter. However, this more desirable measure of disease-related alterations is not fully evaluated in early dementia. The study presented here is the first to report the spatial patterns of cortical thickness in the pre-clinical stages of Alzheimer's disease, namely mild cognitive impairment (MCI). Cortical thickness measurements for 34 healthy elderly, 62 MCI and 42 Alzheimer's disease subjects were made using fully automated magnetic resonance imaging-based analysis techniques in order to determine the pattern of cortical thinning as a function of disease progression. The thickness of the cortex decreased significantly when the healthy elderly brains were compared to those with MCI, mainly in the medial temporal lobe region and in some regions of the frontal and the parietal cortices. With the progression of disease from MCI to Alzheimer's disease, a general thinning of the entire cortex with significant extension into the lateral temporal lobe was found. In all cases, the results were more pronounced in the left hemisphere. In conclusion, we have shown that there is a specific pattern in the thinning of the cortical ribbon which is in agreement with the previous histological reports. These novel findings support the notion of increased isocortical involvement with the progression of disease.

Person re-identification has recently attracted a lot of attention in the computer vision community. This is in part due to the challenging nature of matching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an approach that goes beyond appearance by integrating a semantic aspect into the model. We jointly learn a discriminative projection to a joint appearance-attribute subspace, effectively leveraging the interaction between attributes and appearance for matching. Our experimental results support our model and demonstrate the performance gain yielded by coupling both tasks. Our results outperform several stateof-the-art methods on VIPeR, a standard re-identification dataset. Finally, we report similar results on a new large-scale dataset we collected and labeled for our task.

Computed tomography (CT) is used widely to image patients for medical diagnosis and to scan baggage for threatening materials. Automated reading of these images can be used to reduce the costs of a human operator, extract quantitative information from the images or support the judgements of a human operator. Object quantification requires an image segmentation to make measurements about object size, material composition and morphology. Medical applications mostly require the segmentation of prespecified objects, such as specific organs or lesions, which allows the use of customized algorithms that take advantage of training data to provide orientation and anatomical context of the segmentation targets. In contrast, baggage screening requires the segmentation algorithm to provide segmentation of an unspecified number of objects with enormous variability in size, shape, appearance and spatial context. Furthermore, security systems demand 3D segmentation algorithms that can quickly and reliably detect threats. To address this problem, we present a segmentation algorithm for 3D CT images that makes no assumptions on the number of objects in the image or on the composition of these objects. The algorithm features a new Automatic QUality Measure (AQUA) model that measures the segmentation confidence for any single object (from any segmentation method) and uses this confidence measure to both control splitting and to optimize the segmentation parameters at runtime for each dataset. The algorithm is tested on 27 bags that were packed with a large variety of different objects.

Abstract. We address the problem of estimating human pose in a single image using a part based approach. Pose accuracy is directly affected by the accuracy of the part detectors but more accurate detectors are likely to be also more computationally expensive. We propose to use multiple, heterogeneous part detectors with varying accuracy and computation requirements, ordered in a hierarchy, to achieve more accurate and efficient pose estimation. For inference, we propose an algorithm to localize articulated objects by exploiting an ordered hierarchy of detectors with increasing accuracy. The inference uses branch and bound method to search for each part and use kinematics from neighboring parts to guide the branching behavior and compute bounds on the best part estimate. We demonstrate our approach on a publicly available People dataset and outperform the state-of-art methods. Our inference is 3 times faster than one based on using a single, highly accurate detector.

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We present an approach to recognizing single actor human

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