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We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to(More)
Re-identification of pedestrians in video-surveillance settings can be effectively approached by treating each human figure as an articulated body, whose pose is estimated through the framework of Pictorial Structures (PS). In this way, we can focus selectively on similarities between the appearance of body parts to recognize a previously seen individual.(More)
Is it possible to identify human schizophrenic patients just by analyzing their brain images? This is the fundamental question of magnetic resonance imaging (MRI) based studies of human brains for people affected by schizophrenia and other mental illnesses traditionally diagnosed by self-reports and behavioral observations. The appeal of this approach is at(More)
Aromatase inhibitors (AI) are being evaluated as long-term adjuvant therapies and chemopreventives in breast cancer. However, there are concerns about bone mineral density loss in an estrogen-free environment. Unlike nonsteroidal AIs, the steroidal AI exemestane may exert beneficial effects on bone through its primary metabolite 17-hydroexemestane. We(More)
This paper proposes a new approach to model-based clustering under prior knowledge. The proposed formulation can be interpreted from two different angles: as penalized logistic regression, where the class labels are only indirectly observed (via the probability density of each class); as finite mixture learning under a grouping prior. To estimate the(More)
Schizophrenia research based on magnetic resonance imaging (MRI) traditionally relies on the volumetric analysis of brain matter, either characterizing the whole intracranial volume or studying the attributes of small regions of interest (ROI), corresponding to well-known functional parts in the brain. In this work, we addressed the second scenario,(More)
We present a generative model to perform cosegmentation on an arbitrary number of images, where cosegmentation has been defined as the task of segmenting simultaneously the common parts between a pair of images. We build upon a previous work that introduced a new approach to model-based clustering under prior knowledge, and exploit its simplicity and(More)
A video surveillance sequence generally contains a lot of scattered information regarding several objects in cluttered scenes. Especially in case of use of digital hand-held cameras, the overall quality is very low due to the unstable motion and the low resolution, even if multiple shots of the desired target are available. To overcome these limitations,(More)