The Advancing State of the Art in 2nd Generation Star Trackers
Until recently, only first-generation charge coupled device (CCD) spacecraft star trackers have been available.
memory capacity source code↗
SEARCH · Engineering Papers
Results for “Pattern recognition”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Probabilistic Independence Networks for Hidden Markov Probability Models
In this paper we explore hidden Markov models(HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general enference algorithms for arbitrary PINs.
random variables pattern recognition signal proces↗
Cascade Error Projection: An Efficient Hardware Learning Algorithm
A new learning algorithm termed cascade error projection (CEP) is presented. CEP is an adaption of a constructive architecture from cascade correlation and the dynamical stepsize of A/D conversion from the cascade back propagation algorithm.
learning algorithm pattern recognition cascade err↗
Image Retrieval by Content: A Machine Learning Approach
There has been an explosive growth in the amount of image data.
machine learning pattern recognition automated dat↗
Image Database Exploration: Progress and Challenges
In this paper we discuss the general problem of automated image database exploration, the particular aspects of image databases which distinguish them from other databases, and how this impacts the application of off-the-shelf learning algorithms to problems of this nature. Current progress will be illustrated using two large-scale image exploration projects at JPL. The paper concludes with a discussion of current and future challenges.
image database exploration pattern recognition tec↗
Optical Implementation of Matching Pursuit for Image Representation
We have developed a technique for image analysis, representation, and decomposition. This technique was motivated by Stephane Mallat's matching-pursuit algorithm. We've altered and simplified the mechanics of his algorithm to enable an extremely fast implementation via optical processing. Initial computer simulations show that our algorithm is capable of decomposing and representing a 2-D image as a linear combination of basis images with both high speed and high fidelity.
matching pursuit adaptive wavelet transform image ↗
Neural network post-processing of grayscale optical correlator
In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.
grayscale optical correlator↗
Hidden Markov Models for detecting aseismic events in Southern California
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pattern recognition↗
Smart Cameras for Remote Science Survey
Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels - distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
robotics↗
Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis
Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.
Image Processing; Pattern Recognition↗
Introduction to Remote Sensing Image Registration
For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications
Pattern Recognition↗
Registration of Textured Remote Sensing Images Using Directional Gabor Frames
In this paper we propose to utilize a new concept of discrete directional Gabor frames for automatic image registration. The directional Gabor representations have been shown to provide more accurate feature extraction than directional wavelet transforms for images where texture is the dominant feature. Initial experimental results are presented here which indicate that discrete directional Gabor frames exhibit strong correlations, which indicates that they are likely to improve the existing image registration toolbox.
Image Processing; Pattern Recognition↗
Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis
Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.
Image Processing:Pattern Recognition↗
Introduction to Remote Sensing Image Registration
For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a priori information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications.
Pattern Recognition↗
Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)
Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.
Pattern Recognition↗
Registration of Textured Remote Sensing Images Using Directional Gabor Frames
In this paper we propose to utilize a new concept of discrete directional Gabor frames for automatic image registration. The directional Gabor representations have been shown to provide more accurate feature extraction than directional wavelet transforms for images where texture is the dominant feature. Initial experimental results are presented here which indicate that discrete directional Gabor frames exhibit strong correlations, which indicates that they are likely to improve the existing image registration toolbox.
Pattern Recognition↗
Trade-Space Analysis Tool for Designing Constellations (TAT-C)
While there is growing interest in implementing future NASA Earth Science missions as Distributed Spacecraft Missions (DSMs), there are currently no tool to help in the design of DSMs. The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. Our Trade-space Analysis Tool for Constellations (TAT-C) allows to investigate questions such as: "Which type of constellations should be chosen? How many spacecraft should be included in the constellation? Which design has the best cost/risk value?" This paper provides a description of the TAT-C tool and its components.
Image Processing↗
Planetary Crater Detection and Registration Using Marked Point Processes, Graph Cut Algorithms, and Wavelet Transforms
This paper addresses the problem of semi-automatic image registration on planetary images. A joint feature-based and area-based approach is proposed. Firstly, the most relevant craters are extracted from the two images to register, and then, registration is performed in two steps. The first step matches the craters extracted from the images based on a generalized Hausdorff distance. In the second step, the mutual information between the two images is maximized to achieve high registration accuracy. Craters are detected by a stochastic-geometry approach based on a marked point process model and of a multiple-birth-and-cut energy minimization algorithm. The experimental validation is carried out with 13 images for the crater extraction stage, and with 20 semi-synthetic pairs of images with ground truth and several images extracted from actual multi-temporal lunar scenes for the registration phase.
Image Processing; Pattern Recognition↗