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Healthcare Event and Activity Logging.
The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
Visualizing the semantic content of large text databases using text maps
A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content
CRF Learning with CNN Features for Image Segmentation
Conditional Random Rields (CRF) have been widely applied in image
segmentations. While most studies rely on hand-crafted features, we here
propose to exploit a pre-trained large convolutional neural network (CNN) to
generate deep features for CRF learning. The deep CNN is trained on the
ImageNet dataset and transferred to image segmentations here for constructing
potentials of superpixels. Then the CRF parameters are learnt using a
structured support vector machine (SSVM). To fully exploit context information
in inference, we construct spatially related co-occurrence pairwise potentials
and incorporate them into the energy function. This prefers labelling of object
pairs that frequently co-occur in a certain spatial layout and at the same time
avoids implausible labellings during the inference. Extensive experiments on
binary and multi-class segmentation benchmarks demonstrate the promise of the
proposed method. We thus provide new baselines for the segmentation performance
on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC
2011 datasets
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