Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.Comment: CVPR 201

A Latent Source Model for Patch-Based Image Segmentation

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.Comment: International Conference on Medical Image Computing and Computer Assisted Interventions 201

Making Sense of a New Transport System: An Ethnographic Study of the Cambridgeshire Guided Busway

An increase in public transport use has the potential to contribute to improving population health, and there is growing interest in innovative public transport systems. Yet how new public transport infrastructure is experienced and integrated (or not) into daily practice is little understood. We investigated how the Cambridgeshire Guided Busway, UK, was used and experienced in the weeks following its opening, using the method of participant observation (travelling on the busway and observing and talking to passengers) and drawing on Normalization Process Theory to interpret our data. Using excerpts of field notes to support our interpretations, we describe how the ease with which the new transport system could be integrated into existing daily routines was important in determining whether individuals would continue to use it. It emerged that there were two groups of passengers with different experiences and attitudes. Passengers who had previously travelled frequently on regular bus services did not perceive the new system to be an improvement; consequently, they were frustrated that it was differentiated from and not coherent with the regular system. In contrast, passengers who had previously travelled almost exclusively by car appraised the busway positively and perceived it to be a novel and superior form of travel. Our rich qualitative account highlights the varied and creative ways in which people learn to use new public transport and integrate it into their everyday lives. This has consequences for the introduction and promotion of future transport innovations. It is important to emphasise the novelty of new public transport, but also the ways in which its use can become ordinary and routine. Addressing these issues could help to promote uptake of other public transport interventions, which may contribute to increasing physical activity and improving population health. © 2013 Jones et al

Biases in inferring dark matter profiles from dynamical and lensing measurements

The degeneracy between disc and halo contributions in spiral galaxy rotation curves makes it difficult to obtain a full understanding of the distribution of baryons and dark matter in disc galaxies like our own Milky Way. Using mock data, we study how constraints on dark matter profiles obtained from kinematics, strong lensing, or a combination of the two are affected by assumptions about the halo model. We compare four different models: spherical isothermal and Navarro-Frenk-White halos, along with spherical and elliptical Burkert halos. For both kinematics and lensing we find examples where different models fit the data well but give enclosed masses that are inconsistent with the true (i.e., input) values. This is especially notable when the input and fit models differ in having cored or cuspy profiles (such as fitting an NFW model when the underlying dark matter distribution follows a different profile). We find that mass biases are more pronounced with lensing than with kinematics, and using both methods can help reduce the bias and provide stronger constraints on the dark matter distributions.Comment: 13 pages, 8 figures, submitted for MNRAS publication Nov 15th 201