645 research outputs found
At Face Value: Visual Antecedents of Impression Formation in Servicescapes
Consumers may base employee impressions on physical appearance\ud
and displayed personal objects. In a scenario experiment,\ud
using photos of a physician and a 360-degree panorama of his\ud
consultation room, we examined the effects of appearance and\ud
tangibles on impression formation. Study 1 shows that observers\ud
employ various strategies of combining information from different\ud
sources when forming an impression of the employee’s friendliness\ud
and competence. Whereas previous research has shown that impression\ud
formation based on personal appearances proceeds in an\ud
automatic fashion, the findings of study 2 indicate that impression\ud
formation grounded in the perception of tangibles requires more\ud
elaborate processin
WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning
To date, the widely-adopted way to perform fixation collection in panoptic
video is based on a head-mounted display (HMD), where participants' fixations
are collected while wearing an HMD to explore the given panoptic scene freely.
However, this widely-used data collection method is insufficient for training
deep models to accurately predict which regions in a given panoptic are most
important when it contains intermittent salient events. The main reason is that
there always exist "blind zooms" when using HMD to collect fixations since the
participants cannot keep spinning their heads to explore the entire panoptic
scene all the time. Consequently, the collected fixations tend to be trapped in
some local views, leaving the remaining areas to be the "blind zooms".
Therefore, fixation data collected using HMD-based methods that accumulate
local views cannot accurately represent the overall global importance of
complex panoramic scenes. This paper introduces the auxiliary Window with a
Dynamic Blurring (WinDB) fixation collection approach for panoptic video, which
doesn't need HMD and is blind-zoom-free. Thus, the collected fixations can well
reflect the regional-wise importance degree. Using our WinDB approach, we have
released a new PanopticVideo-300 dataset, containing 300 panoptic clips
covering over 225 categories. Besides, we have presented a simple baseline
design to take full advantage of PanopticVideo-300 to handle the
blind-zoom-free attribute-induced fixation shifting problem
Towards a Strawberry Harvest Prediction System Using Computer Vision and Pattern Recognition
Farmers require advance notice when a harvest is approaching, so they can allocate resources and hire workers as efficiently as possible. Existing methods are subjective and labor intensive, and require the expertise of a professional forecaster. Cal Poly’s EE department has been collaborating with the Cal Poly Strawberry Center to investigate the potential in using digital imaging processing to predict harvests more reliably. This paper shows the progress of that ongoing project, as well as what aspects could still be improved. Three main blocks comprise this system: data acquisition, which obtains and catalogues images of the strawberry plants; computer vision, which extracts information from the images and constructs a time-series model of the field as a whole; and prediction, which uses the field’s history to guess when the most likely harvest window will be. The best method of data acquisition is determined through a decision matrix to be a small autonomous rover. Several challenges specific to images captured via drone, such as fisheye distortion and dirt masking, are examined and mitigated. Using thresholding, the nRGB color space is shown to be the most promising for image segmentation of red strawberries. Data from field 25 at the Cal Poly Strawberry Center is tabulated, analyzed, and compared against industry trends across California. Ultimately, this work serves as a strong benchmark towards a full strawberry yield prediction system
Visualizations of Downtown San Bernardino and a Proposed Development Using CityEngine
Cities are experiencing increasing growth in population and business infrastructure. These changes have profound impacts on urban planners and stakeholders alike, in how they view and conceptualize potential new developments. In the past, the downtown area of the City of San Bernardino would take on new projects only having a rendering of the proposed building(s), making it time consuming and difficult to understand the wider impact on the surrounding areas. Without view analyses these developments could potentially result in termination due to deadlines or loss of interest from stakeholder. This project addressed this issue by creating 3D renderings of the area using CityEngine and preforming various visual analyses for new development(s). Having CityEngine will deduct meeting time and effectively answer visual questions their various stakeholders have in regard to the developments or cityscape of downtown San Bernardino area. These conclusions of these findings were significant to the downtown City of San Bernardino, and the project was able to be created with the data provided. The data also allowed the project to and create the cityscape of the downtown area and to preform various visual analyses to solidify the project’s fruition
A metabolomic data fusion approach to support gliomas grading
Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T2-filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors
Deep Reinforcement Learning for Active Human Pose Estimation
Most 3d human pose estimation methods assume that input -- be it images of a
scene collected from one or several viewpoints, or from a video -- is given.
Consequently, they focus on estimates leveraging prior knowledge and
measurement by fusing information spatially and/or temporally, whenever
available. In this paper we address the problem of an active observer with
freedom to move and explore the scene spatially -- in `time-freeze' mode --
and/or temporally, by selecting informative viewpoints that improve its
estimation accuracy. Towards this end, we introduce Pose-DRL, a fully trainable
deep reinforcement learning-based active pose estimation architecture which
learns to select appropriate views, in space and time, to feed an underlying
monocular pose estimator. We evaluate our model using single- and multi-target
estimators with strong result in both settings. Our system further learns
automatic stopping conditions in time and transition functions to the next
temporal processing step in videos. In extensive experiments with the Panoptic
multi-view setup, and for complex scenes containing multiple people, we show
that our model learns to select viewpoints that yield significantly more
accurate pose estimates compared to strong multi-view baselines.Comment: Accepted to The Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20). Submission updated to include supplementary materia
Aeronautical Engineering: A special bibliography with indexes, supplement 64, December 1975
This bibliography lists 288 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1975
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