40,792 research outputs found
Development of Computer Vision-Enhanced Smart Golf Ball Retriever
An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field
Applications of inertial navigation and modern control theory to the all weather landing problem
Inertial navigation and automatic landing control theory applied to instrument landing proble
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Are we using the right potential functions in the Conditional Random Field
models that are popular in the Vision community? Semantic segmentation and
other pixel-level labelling tasks have made significant progress recently due
to the deep learning paradigm. However, most state-of-the-art structured
prediction methods also include a random field model with a hand-crafted
Gaussian potential to model spatial priors, label consistencies and
feature-based image conditioning.
In this paper, we challenge this view by developing a new inference and
learning framework which can learn pairwise CRF potentials restricted only by
their dependence on the image pixel values and the size of the support. Both
standard spatial and high-dimensional bilateral kernels are considered. Our
framework is based on the observation that CRF inference can be achieved via
projected gradient descent and consequently, can easily be integrated in deep
neural networks to allow for end-to-end training. It is empirically
demonstrated that such learned potentials can improve segmentation accuracy and
that certain label class interactions are indeed better modelled by a
non-Gaussian potential. In addition, we compare our inference method to the
commonly used mean-field algorithm. Our framework is evaluated on several
public benchmarks for semantic segmentation with improved performance compared
to previous state-of-the-art CNN+CRF models.Comment: Presented at EMMCVPR 2017 conferenc
Approximate Methods for State-Space Models
State-space models provide an important body of techniques for analyzing
time-series, but their use requires estimating unobserved states. The optimal
estimate of the state is its conditional expectation given the observation
histories, and computing this expectation is hard when there are
nonlinearities. Existing filtering methods, including sequential Monte Carlo,
tend to be either inaccurate or slow. In this paper, we study a nonlinear
filter for nonlinear/non-Gaussian state-space models, which uses Laplace's
method, an asymptotic series expansion, to approximate the state's conditional
mean and variance, together with a Gaussian conditional distribution. This {\em
Laplace-Gaussian filter} (LGF) gives fast, recursive, deterministic state
estimates, with an error which is set by the stochastic characteristics of the
model and is, we show, stable over time. We illustrate the estimation ability
of the LGF by applying it to the problem of neural decoding and compare it to
sequential Monte Carlo both in simulations and with real data. We find that the
LGF can deliver superior results in a small fraction of the computing time.Comment: 31 pages, 4 figures. Different pagination from journal version due to
incompatible style files but same content; the supplemental file for the
journal appears here as appendices B--E
Investigation of new radar-data-reduction techniques used to determine drag characteristics of a free-flight vehicle
An investigation was conducted of new techniques used to determine the complete transonic drag characteristics of a series of free-flight drop-test models using principally radar tracking data. The full capabilities of the radar tracking and meteorological measurement systems were utilized. In addition, preflight trajectory design, exact kinematic equations, and visual-analytical filtering procedures were employed. The results of this study were compared with the results obtained from analysis of the onboard, accelerometer and pressure sensor data of the only drop-test model that was instrumented. The accelerometer-pressure drag curve was approximated by the radar-data drag curve. However, a small amplitude oscillation on the latter curve precluded a precise definition of its drag rise
Adaptive Embedded LES of the NASA Hump
A scheme for adaptive embedded LES is proposed which automatically determines boundaries for LES regions in a hybrid LES-RANS computation, with the goal of minimizing the LES part of the computation for maximum accuracy with minimum cost. The model-invariant hybrid formulation enables this scheme through greater flexibility in the placement of RANS-LES transitions. An adaptive embedded large-eddy simulation is carried out for the NASA hump test case and adaptive meshing is added to show how additional adaptive features may be controlled by the adaptive hybrid scheme
- …