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

Sparse and low rank approximations for action recognition

Action recognition is crucial area of research in computer vision with wide range of applications in surveillance, patient-monitoring systems, video indexing, Human- Computer Interaction and many more. These applications require automated action recognition. Robust classification methods are sought-after despite influential research in this field over past decade. The data resources have grown tremendously owing to the advances in the digital revolution which cannot be compared to the meagre resources in the past. The main limitation on a system when dealing with video data is the computational burden due to large dimensions and data redundancy. Sparse and low rank approximation methods have evolved recently which aim at concise and meaningful representation of data. This thesis explores the application of sparse and low rank approximation methods in the context of video data classification with the following contributions. 1. An approach for solving the problem of action and gesture classification is proposed within the sparse representation domain, effectively dealing with large feature dimensions, 2. Low rank matrix completion approach is proposed to jointly classify more than one action 3. Deep features are proposed for robust classification of multiple actions within matrix completion framework which can handle data deficiencies. This thesis starts with the applicability of sparse representations based classifi- cation methods to the problem of action and gesture recognition. Random projection is used to reduce the dimensionality of the features. These are referred to as compressed features in this thesis. The dictionary formed with compressed features has proved to be efficient for the classification task achieving comparable results to the state of the art. Next, this thesis addresses the more promising problem of simultaneous classifi- cation of multiple actions. This is treated as matrix completion problem under transduction setting. Matrix completion methods are considered as the generic extension to the sparse representation methods from compressed sensing point of view. The features and corresponding labels of the training and test data are concatenated and placed as columns of a matrix. The unknown test labels would be the missing entries in that matrix. This is solved using rank minimization techniques based on the assumption that the underlying complete matrix would be a low rank one. This approach has achieved results better than the state of the art on datasets with varying complexities. This thesis then extends the matrix completion framework for joint classification of actions to handle the missing features besides missing test labels. In this context, deep features from a convolutional neural network are proposed. A convolutional neural network is trained on the training data and features are extracted from train and test data from the trained network. The performance of the deep features has proved to be promising when compared to the state of the art hand-crafted features

From Maxout to Channel-Out: Encoding Information on Sparse Pathways

Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.Comment: 10 pages including the appendix, 9 figure

ANFIS Definition of Focal Length for Zoom Lens via Fuzzy Logic Functions

The digital cameras have been effected from systematical errors which decreased metric quality of image. The digital cameras have been effected from systematical errors that decreases metric quality of image. The aim of this chapter is to explore usability of fuzzy logic on calibration of digital cameras. Therefore, a 145‐pointed planar test field has been prepared in the laboratories of Department of Geodesy and Photogrammetric Engineering at the Gebze Technical University. The test field has been imaged from five points of view with the digital camera Nikon Coolpix‐E8700 within maximum (71.2 mm) and minimum (8.9 mm) focal length. The input-output data have been determined from 10 calibration images obtained for fuzzy logic process. These data have also been used and formed for the space resection process. Adaptive neuro‐fuzzy inference system (ANFIS) functions have been used for fuzzy process at MATLAB 7.0, and the results of these two distinct methods have been compared. Finally, the most convenient (least squares average error) or the most useful ANFIS “Trimf, trapmf, gbellmf, gaussmf, gauss2mf, pimf, dsigmf and psigmf” functions are determined and compared for space resection method for the conventional bundle adjustment process

Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems

Given an increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g. perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human-AI teaming case where a managing agent is tasked with identifying when to perform a delegation assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, the sensing deficiencies. These contexts provide cases where the manager must learn to attribute capabilities to suitability for decision-making. As such, we demonstrate how a Reinforcement Learning (RL) manager can correct the context-delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation

Opportunity Knocks: An Economic Analysis of Television Advertisements

Certain aspects of advertising–especially on television–are not easily explained with conventional economic models. In particular, much of the imagery and repetitive thematic content seen in advertisements suggests it is "psychological" in nature, as opposed to "informative". To understand the economic rationale for incorporating such material, we develop a theory of preferences in which information about threshold payoffs induces sudden shifts in demand. These threshold payoffs are best understood in the context of human evolutionary history. Furthermore, the presence of threshold payoffs in consumer preferences gives firms incentive for providing threshold-type information. To examine the use of threshold-related content in television advertisements, we look for this con- tent in a sample of 370 television advertisements. We find considerable evidence that advertisers make strategic use of threshold-type content in television advertisements. Specifically, threshold-related content occurred in 83% of food and beverage advertisements for children and in 71% of advertisements for general audiences. Furthermore, the threshold-related content in children’s food and beverage advertisements occurred with statistically greater frequency than factual content, which isn’t true for food and beverage advertisements for general audiences

New constraints on data-closeness and needle map consistency for shape-from-shading

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Second, we exploit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first set of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constraints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a variety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We provide an experimental assessment of the new shape-from-shading framework on both real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-closeness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map

Sparse Representation for Paddy Plants Nutrient Deficiency Tracking System

Moving object detection and tracking from consecutive frames of sensing devices (Unmanned Aerial Vehicles-UAV) needs efficient sampling from mass data with sufficient memory saving. Objects with super pixels are tracked by Compressive Sensing (CS) and the generative structural part model is designed to be adaptive to variation of deformable objects. CS can precisely reconstruct sparse signal with a small amount of sampling data. This system creates the sparse representation (SR) dictionary representing the nutrient deficiency tracking system for paddy plants to support the healthily growth of the whole field. This system uses compressed domain features that can be exploited to map the semantic features of consecutive frames. As the CS is a developing signal processing technique, a sparse signal is reconstructed with efficient sampling rate and creates the sparse dictionary. The SR for paddy plant health system can build rich information about paddy plants from signaling devices and can alert the deficiency conditions accurately in real time