289 research outputs found
Automatic Detection of Circular Objects by Ellipse Growing
We present a new method for automatically detecting circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space is restricted to one and two dimensions for efficiency, and special weighting schemes are
introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3-D object shape reconstruction
Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a
challenging problem in statistics and AI. Classical approaches for {exploratory
data analysis} are usually not flexible enough to deal with the uncertainty
inherent to real-world data: they are often restricted to fixed latent
interaction models and homogeneous likelihoods; they are sensitive to missing,
corrupt and anomalous data; moreover, their expressiveness generally comes at
the price of intractable inference. As a result, supervision from statisticians
is usually needed to find the right model for the data. However, since domain
experts are not necessarily also experts in statistics, we propose Automatic
Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible
at large. Specifically, ABDA allows for automatic and efficient missing value
estimation, statistical data type and likelihood discovery, anomaly detection
and dependency structure mining, on top of providing accurate density
estimation. Extensive empirical evidence shows that ABDA is a suitable tool for
automatic exploratory analysis of mixed continuous and discrete tabular data.Comment: In proceedings of the Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
Proposed Experiment in Two-Qubit Linear Optical Photonic Gates for Maximal Success Rates
Here we propose an experiment in Linear Optical Quantum Computing (LOQC)
using the framework first developed by Knill, Laflamme, and Milburn. This
experiment will test the ideas of the authors' previous work on imperfect LOQC
gates using number-resolving photon detectors. We suggest a relatively simple
physical apparatus capable of producing CZ gates with controllable fidelity
less than 1 and success rates higher than the current theoretical maximum
(S=2/27) for perfect fidelity. These experimental setups are within the reach
of many experimental groups and would provide an interesting experiment in
photonic quantum computing.Comment: 9 pages, 3 figure
Quantization-free parameter space reduction in ellipse detection
Ellipse modeling and detection is an important task in many computer vision and pattern recognition applications. In this thesis, four Hough-based transform algorithms have been carefully selected, studied and analyzed. These techniques include the Standard Hough Transform, Probabilistic Hough Transform, Randomized Hough Transform and Directional Information for Parameter Space Decomposition. The four algorithms are analyzed and compared against each other in this study using synthetic ellipses. Objects such as noise have been introduced to distract ellipse detection in some of the synthetic ellipse images. To complete the analysis, real world images were used to test each algorithm resulting in the proposal of a new algorithm. The proposed algorithm uses the strengths from each of the analyzed algorithms. This new algorithm uses the same approach as the Directional Information for Parameter Space Decomposition to determine the ellipse center. However, in the process of collecting votes for the ellipse center, pairs of unique edge points voted for the center are also kept in an array. A minimum of two pairs of edge points are required to determine the ellipse. This significantly reduces the usual five dimensional array requirement needed in the Standard Hough Transform. We present results of the experiments with synthetic images demonstrating that the proposed method is more effective and robust to noise. Real world applications on complex real world images are also performed successfully in the experiment
NETRA - A Parallel Architecture for Integrated Vision Systems II: Algorithms and Performance Evaluation
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Aeronautics and Space Administration / NASA NAG-1-61
Fuzzy Set Methods for Object Recognition in Space Applications
Progress on the following four tasks is described: (1) fuzzy set based decision methodologies; (2) membership calculation; (3) clustering methods (including derivation of pose estimation parameters), and (4) acquisition of images and testing of algorithms
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