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A systematic introduction to the concepts and techniques of computer image processing and recognition is presented. Consideration is given to such topics as image formation and perception; computer representation of images; image enhancement and restoration; reconstruction from projections; digital television, encoding, and data compression; scene understanding; scene matching and recognition; and processing techniques for linear systems.
Discussion of a statistical procedure for treatment of noise-affected images to recover unaffected images by recursive processing with noise background elimination. The feasibility of the application of a recursive linear Kalman filtering technique to image processing is demonstrated. The procedure is applicable to images which are characterized statistically by mean and correlation functions. A time invariant dynamic model is proposed to provide stationary statistics for the scanner output.
This paper describes the Multimission VICAR Planner [Chien 1994] (MVP) system, which use Artificial Intelligence (AI) Planning techniques [Iwasaki & Friedland, 1985, Pemberthy & Weld, 1992, Stefik, 1981] to automatically construct executable complex image processing procedures.
Virtual Diagnostics Interface technology, or ViDI, is a suite of techniques utilizing image processing, data handling and three-dimensional computer graphics. These techniques aid in the design, implementation, and analysis of complex aerospace experiments. LiveView3D is a software application component of ViDI used to display experimental wind tunnel data in real-time within an interactive, three-dimensional virtual environment. The LiveView3D software application was under development at NASA Langley Research Center (LaRC) for nearly three years. LiveView3D recently was upgraded to perform real-time (as well as post-test) comparisons of experimental data with pre-computed Computational Fluid Dynamics (CFD) predictions. This capability was utilized to compare experimental measurements with CFD predictions of the surface pressure distribution of the NASA Ares I Crew Launch Vehicle (CLV) - like vehicle when tested in the NASA LaRC Unitary Plan Wind Tunnel (UPWT) in December 2006 - January 2007 timeframe. The wind tunnel tests were conducted to develop a database of experimentally-measured aerodynamic performance of the CLV-like configuration for validation of CFD predictive codes.
Virtual Diagnostics Interface technology, or ViDI, is a suite of techniques utilizing image processing, data handling and three-dimensional computer graphics. These techniques aid in the design, implementation, and analysis of complex aerospace experiments. LiveView3D is a software application component of ViDI used to display experimental wind tunnel data in real-time within an interactive, three-dimensional virtual environment. The LiveView3D software application was under development at NASA Langley Research Center (LaRC) for nearly three years. LiveView3D recently was upgraded to perform real-time (as well as post-test) comparisons of experimental data with pre-computed Computational Fluid Dynamics (CFD) predictions. This capability was utilized to compare experimental measurements with CFD predictions of the surface pressure distribution of the NASA Ares I Crew Launch Vehicle (CLV) - like vehicle when tested in the NASA LaRC Unitary Plan Wind Tunnel (UPWT) in December 2006 - January 2007 timeframe. The wind tunnel tests were conducted to develop a database of experimentally-measured aerodynamic performance of the CLV-like configuration for validation of CFD predictive codes.
Additive manufacturing is a rapid prototyping technology that allows parts to be built in a series of thin layers from plastic, ceramics, and metallics. Metallic additive manufacturing is an emerging form of rapid prototyping that allows complex structures to be built using various metallic powders. Significant time and cost savings have also been observed using the metallic additive manufacturing compared with traditional techniques. Development of the metallic additive manufacturing technology has advanced significantly over the last decade, although many of the techniques to inspect parts made from these processes have not advanced significantly or have limitations. Several external geometry inspection techniques exist such as Coordinate Measurement Machines (CMM), Laser Scanners, Structured Light Scanning Systems, or even traditional calipers and gages. All of the aforementioned techniques are limited to external geometry and contours or must use a contact probe to inspect limited internal dimensions. This presentation will document the development of a process for real-time dimensional inspection technique and digital quality record of the additive manufacturing process using Infrared camera imaging and processing techniques.
The Additive Manufacturing Infrared Inspection Task started the development of a real-time dimensional inspection technique and digital quality record for the additive manufacturing process using infrared camera imaging and processing techniques. This project will benefit additive manufacturing by providing real-time inspection of internal geometry that is not currently possible and reduce the time and cost of additive manufactured parts with automated real-time dimensional inspections which deletes post-production inspections.
Currently there is considerable interest in active microwave sensors for earth resources applications, such as the SEASAT-A radar. However, to obtain spatial resolutions comparable to optical sensors at radar frequencies, sophisticated image formation processing techniques must be applied to the raw data. Processing requirements for non-coherent optical and coherent radar imaging systems are compared. The image formation processing requirements for synthetic aperture radar (SAR) systems are discussed. Both optical and digital techniques are addressed, and examples of hardware and imagery for each processing technique are presented.
Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.
A 30 x 30 arcsec field at the Galactic Center (1.5 x 1.5 parsec) was mapped at 8.3 microns and 12.41 microns with high spatial resolution and accurate relative astrometry, using the 16 x 16 Si:Bi accumulation mode charge injection device Goddard infrared array camera. The design and performance of the array camera detector electronics system and image data processing techniques are discussed. Color temperature and dust opacity distributions derived from the spatially accurate images indicate that the compact infrared sources and the large scale ridge structure are bounded by warmer, more diffuse material. None of the objects appear to be heated appreciably by internal luminosity sources. These results are consistent with the model proposing that the complex is heated externally by a strong luminosity source at the Galactic Center, which dominates the energetics of the inner few parsecs of the galaxy.
A 30 x 30 arcsec field at the Galactic Center (1.5 x 1.5 parsec) was mapped at 8.3 microns and 12.41 microns with high spatial resolution and accurate relative astrometry, using the 16 x 16 Si:Bi accumulation mode charge injection device Goddard infrared array camera. The design and performance of the array camera detector electronics system and image data processing techniques are discussed. Color temperature and dust opacity distributions derived from the spatially accurate images indicate that the compact infrared sources and the large scale ridge structure are bounded by warmer, more diffuse material. None of the objects appear to be heated appreciably by internal luminosity sources. These results are consistent with the model proposing that the complex is heated externally by a strong luminosity source at the Galactic Center, which dominates the energetics of the inner few parsecs of the galaxy.
Currently there is considerable interest in active microwave sensors for earth resources applications. A particular example is the Seasat-A radar. However, to obtain spatial resolutions comparable to optical sensors at radar frequencies, sophisticated image formation processing techniques must be applied to the raw data. This paper briefly compares processing requirements for non-coherent optical and coherent radar imaging systems, and then discusses the image formation processing requirements for synthetic aperture radar (SAR) systems. Both optical and digital techniques are addressed, and examples of hardware and imagery for each processing technique are presented.
Digital imaging techniques used in sinusoidal frequency response modification with or without computer control and display on operator console
Video digitization techniques have been developed to analyze the exhaust plume of the Space Shuttle Main Engine. Temporal averaging and a frame-by-frame analysis provide data used to evaluate the capabilities of image processing techniques for use as measurement tools. Capabilities include the determination of the necessary time requirement for the Mach disk to obtain a fully-developed state. Other results show the Mach disk tracks the nozzle for short time intervals, and that dominate frequencies exist for the nozzle and Mach disk movement.
Image orthicon TV system and application to auroral observations, noting data acquisition mechanism
New computer techniques for analyzing and processing photographic images of galaxies are presented, with interesting scientific findings gleaned from the processed photographic data. Discovery and enhancement of very faint and low-contrast nebulous features, improved resolution of near-limit detail in nebulous and stellar images, and relative colors of a group of nebulosities in the field are attained by the methods. Digital algorithms, nonlinear pattern-recognition filters, linear convolution filters, plate averaging and contrast enhancement techniques, and an atmospheric deconvolution technique are described. New detail is revealed in images of NGC 7331, Stephan's Quintet, Seyfert's Sextet, and the jet in M87, via processes of addition of plates, star removal, contrast enhancement, standard deviation filtering, and computer ratioing to bring out qualitative color differences.
We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.
We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.