5,778 research outputs found
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
On Using Physical Analogies for Feature and Shape Extraction in Computer Vision
There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision
Maximum likelihood estimation of circle parameters via convolution
Copyright © 2006 IEEEThe accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne–Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a “phase-coded kernel” (PCK) and the MLE. This is related to the “phase-coded annulus” which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér–Rao Lower Bound in ideal images and in both real and synthetic digital images.Emanuel E. Zelniker, Student Member, IEEE, and I. Vaughan L. Clarkso
First Observational Tests of Eternal Inflation: Analysis Methods and WMAP 7-Year Results
In the picture of eternal inflation, our observable universe resides inside a
single bubble nucleated from an inflating false vacuum. Many of the theories
giving rise to eternal inflation predict that we have causal access to
collisions with other bubble universes, providing an opportunity to confront
these theories with observation. We present the results from the first
observational search for the effects of bubble collisions, using cosmic
microwave background data from the WMAP satellite. Our search targets a generic
set of properties associated with a bubble collision spacetime, which we
describe in detail. We use a modular algorithm that is designed to avoid a
posteriori selection effects, automatically picking out the most promising
signals, performing a search for causal boundaries, and conducting a full
Bayesian parameter estimation and model selection analysis. We outline each
component of this algorithm, describing its response to simulated CMB skies
with and without bubble collisions. Comparing the results for simulated bubble
collisions to the results from an analysis of the WMAP 7-year data, we rule out
bubble collisions over a range of parameter space. Our model selection results
based on WMAP 7-year data do not warrant augmenting LCDM with bubble
collisions. Data from the Planck satellite can be used to more definitively
test the bubble collision hypothesis.Comment: Companion to arXiv:1012.1995. 41 pages, 23 figures. v2: replaced with
version accepted by PRD. Significant extensions to the Bayesian pipeline to
do the full-sky non-Gaussian source detection problem (previously restricted
to patches). Note that this has changed the normalization of evidence values
reported previously, as full-sky priors are now employed, but the conclusions
remain unchange
Project OASIS: The Design of a Signal Detector for the Search for Extraterrestrial Intelligence
An 8 million channel spectrum analyzer (MCSA) was designed the meet to meet the needs of a SETI program. The MCSA puts out a very large data base at very high rates. The development of a device which follows the MCSA, is presented
- …