133,963 research outputs found
Adaptive guidance and control for future remote sensing systems
A unique approach to onboard processing was developed that is capable of acquiring high quality image data for users in near real time. The approach is divided into two steps: the development of an onboard cloud detection system; and the development of a landmark tracker. The results of these two developments are outlined and the requirements of an operational guidance and control system capable of providing continuous estimation of the sensor boresight position are summarized
Plug-and-play distributed state estimation for linear systems
This paper proposes a state estimator for large-scale linear systems
described by the interaction of state-coupled subsystems affected by bounded
disturbances. We equip each subsystem with a Local State Estimator (LSE) for
the reconstruction of the subsystem states using pieces of information from
parent subsystems only. Moreover we provide conditions guaranteeing that the
estimation errors are confined into prescribed polyhedral sets and converge to
zero in absence of disturbances. Quite remarkably, the design of an LSE is
recast into an optimization problem that requires data from the corresponding
subsystem and its parents only. This allows one to synthesize LSEs in a
Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of
the whole estimator requires at most the design of an LSE for the subsystem and
its parents. Theoretical results are backed up by numerical experiments on a
mechanical system
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
This paper investigates, using prior shape models and the concept of ball
scale (b-scale), ways of automatically recognizing objects in 3D images without
performing elaborate searches or optimization. That is, the goal is to place
the model in a single shot close to the right pose (position, orientation, and
scale) in a given image so that the model boundaries fall in the close vicinity
of object boundaries in the image. This is achieved via the following set of
key ideas: (a) A semi-automatic way of constructing a multi-object shape model
assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship
between objects in the training images and their intensity patterns captured in
b-scale images. (c) A hierarchical mechanism of positioning the model, in a
one-shot way, in a given image from a knowledge of the learnt pose relationship
and the b-scale image of the given image to be segmented. The evaluation
results on a set of 20 routine clinical abdominal female and male CT data sets
indicate the following: (1) Incorporating a large number of objects improves
the recognition accuracy dramatically. (2) The recognition algorithm can be
thought as a hierarchical framework such that quick replacement of the model
assembly is defined as coarse recognition and delineation itself is known as
finest recognition. (3) Scale yields useful information about the relationship
between the model assembly and any given image such that the recognition
results in a placement of the model close to the actual pose without doing any
elaborate searches or optimization. (4) Effective object recognition can make
delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201
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