Showing papers on "Aerial image published in 1987"

Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing

Abstract: This paper discusses roles of artificial intelligence in the automatic interpretation of remotely sensed imagery. We first discuss several image understanding systems for analyzing complex aerial photographs. The discussion is mainly concerned with knowledge representation and control structure in the aerial image understanding systems: a blackboard model for integrating diverse object detection modules, a symbolic model representation for three-dimensional object recognition, and integration of bottom-up and top-down analyses. Then, a model of expert systems for image processing is introduced that discusses which and what combinations of image processing operators are effective to analyze an image. Various information about image processing techniques is used to find efficient and reliable image analysis processes. In general, two kinds of knowledge, that is, knowledge about objects and about analysis tools (i. e., image processing techniques) are required to realize versatile photointerpretation systems.

Automating Knowledge Acquisition For Aerial Image Interpretation

Abstract: The interpretation of aerial photographs requires a lot of knowledge about the scene under consideration. Knowledge about the type of scene: airport, suburban housing development, urban city, aids in low-level and intermediate level image analysis, and will drive high-level interpretation by constraining search for plausible consistent scene models. Collecting and representing large knowledge bases requires specialized tools. In this paper we describe the organization of a set of tools for interactive knowledge acquisition of scene primitives and spatial constraints for interpretation of aerial imagery. These tools include a user interface for interactive knowledge acquisition, the automated compilation of that knowledge from a schema-based representation into productions that are directly executable by our interpretation system, and a performance analysis tool that generates a critique of the final interpretation. Finally, the generality of these tools is demonstrated by the generation of rules for a new task, suburban house scenes, and the analysis of a set of imagery by our interpretation system.

Annular Field Systems And The Future Of Optical Microlithography

Abstract: Annular field projection optical systems have been used in production for the manufacture of circuits with feature sizes about 75% of the size of those produced by stepper lenses with the same diffraction limits. One factor contributing to this is the uniformity of imagery over the field inherent in these systems, both in and out of focus. The limits of optical microlithography will be determined by the smallest feature sizes that can be recorded with adequate focal range. For partially coherent illumination, the intensity/defocus (l-D) diagram of an optical system for a particular pattern can be used to give both the depth of focus for a given recording window and the recordability of a pattern as measured by the slope of the intensity distribution of the aerial image at the recording point. The inherent focal range advantage of annular field optical systems, when combined with their other advantages, makes them prime candidates for extending the range of optical lithography to feature sizes below 0.5 µm.

Focus : The Critical Parameter For Submicron Optical Lithography :Part 2

Abstract: Depth of focus requirements and contributions to the focal error budget for submicron optical lithography are reviewed Models are presented which estimate depth of focus in both thin and thick layers of photoresist The effects of resist refraction on usable depth of focus are considered Measurements of image plane tilt, curvature, and astigmatism in 5X reduction lenses collected using an automated, in situ, aerial image monitor are analyzed

Practical Characterization Of 0.5 μm Optical Lithography

Abstract: This paper establishes the characterization techniques that are needed to repeatedly fabricate submicron structures which are below the resolution specifications of an optical system. A two-parameter system is defined, consisting of a material or photosensitive component and an optical component, which represents the major contributors to process variability. Statistical inference and correlation analysis are used to estimate the effects and extent of the individual components on the total lithographic system. The photosensitive component, consisting of the photoresist, films and related chemistry, is shown to substantially represent the variability in the final linewidth of 0.5 μm structures. Positive and negative photoresist systems are compared and analyzed for run-to-run and within-run variability utilizing photoresist speed-point analysis. The optical component is explained by using a two-dimensional aerial image model which takes as input the numerical aperture, wavelength, coherence, aberrations and experimentally measured effects. A method for quantitatively measuring flare or background exposure is introduced.

Organizing The Landscape For Image Understanding Purposes

Abstract: Automated information extraction from aerial imagery has proven to be a very difficult problem. Two decades of statistical pattern recognition research have indicated the need for more robust approaches. Image understanding, which integrates low level pattern recognition symbolizers and knowledge based artificial intelligence techniques, is the favored research approach for the future. Presently, however, the bulk of the image understanding efforts have not exploited the knowledge based aspects of information extraction from aerial imagery, but rather have been stymied in efforts devoted to pixel to symbol transformations that treat most landscape patterns as background noise. This paper outlines the physical structuring of landscapes which is believed to be a necessary ingredient for knowledge based aerial image understanding. A successful approach for manual interpretation of terrain information from aerial imagery is outlined. Two examples of computer terrain analysis that utilize terrain structural information are briefly introduced and sources of information on the physical structure of landscapes and digital terrain data are presented.

Aerial Image Systems

Abstract: Aerial images produce the best stereoscopic images of the viewed world.Despite the fact that every optic in existence produces an aerial image, fewpersons are aware of their existence and possible uses. Constant reference tothe eye and other optical systems have produced a psychosis of design that onlyconsiders "focal planes" in the design and analysis of optical systems. Allobjects in the field of view of the optical device are imaged by the device asan aerial image. Use of aerial images in vision and visual display systems canprovide a true stereoscopic representation of the viewed world.This paper discusses aerial image systems - their applications and designsand presents designs and design concepts that utilize aerial images to obtainsuperior visual displays, particularly with application to visual simulation.Introduction"Seeing" is a learned ability, learned at such an early age that most of theprocesses proceed at a subconscious level. (One of the purposes of any trainingmethodology

Image Steadiness: A Critical Factor In System Performance

Abstract: One of the important factors that affect the performance of aerial image detecting systems is the presence of sensor angular velocities during image recording. The primary contributors to these unwanted angular velocities are: - Velocity of the aircraft relative to the earth. - Low frequency aircraft angular motions. - Vibration induced angular velocities.

Optical projection system

Abstract: An optical projection system comprises a, preferably plano-convex, lens (40) to produce an aerial image of a photograph for recordal, eg by a camera (30) The system may be used to project a photographic negative image aerially and to record that image using a video camera