Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

Estimating Epipolar Geometry With The Use of a Camera Mounted Orientation Sensor

Context: Image processing and computer vision are rapidly becoming more and more commonplace, and the amount of information about a scene, such as 3D geometry, that can be obtained from an image, or multiple images of the scene is steadily increasing due to increasing resolutions and availability of imaging sensors, and an active research community. In parallel, advances in hardware design and manufacturing are allowing for devices such as gyroscopes, accelerometers and magnetometers and GPS receivers to be included alongside imaging devices at a consumer level. Aims: This work aims to investigate the use of orientation sensors in the field of computer vision as sources of data to aid with image processing and the determination of a scene’s geometry, in particular, the epipolar geometry of a pair of images - and devises a hybrid methodology from two sets of previous works in order to exploit the information available from orientation sensors alongside data gathered from image processing techniques. Method: A readily available consumer-level orientation sensor was used alongside a digital camera to capture images of a set of scenes and record the orientation of the camera. The fundamental matrix of these pairs of images was calculated using a variety of techniques - both incorporating data from the orientation sensor and excluding its use Results: Some methodologies could not produce an acceptable result for the Fundamental Matrix on certain image pairs, however, a method described in the literature that used an orientation sensor always produced a result - however in cases where the hybrid or purely computer vision methods also produced a result - this was found to be the least accurate. Conclusion: Results from this work show that the use of an orientation sensor to capture information alongside an imaging device can be used to improve both the accuracy and reliability of calculations of the scene’s geometry - however noise from the orientation sensor can limit this accuracy and further research would be needed to determine the magnitude of this problem and methods of mitigation

The lunar surface: visualizing changes

This research project attempted to create a method of comparison between the imagery from the Lunar Orbiter program (from the mid 1960\u27s) with that of the Clementine mission (of the mid 1990\u27s). The premise behind this research is that if any new surface features developed over the course of the past thirty years, they could be found by doing such a digitial comparison. There are many implication that such research could have on the future. Being that the moon is currently the most thouroughly studied celestial body, the use of doing such a comparison between databases of imagery would prove to be useful on ly for the moon. But in the future, such techniques could be applied to a variety of imagery. In the specific case of the lunar surface, it is important to know of things that develop on the surface (either volcanically or due to an impact) because it is the closest indicator of what may be happening at the earth\u27s outermost layer of atmosphere. Previously, these large databases had been collected, but not much had been done with the imagery. This research has been able to create a procedure in which such imagery from the Clementine satellite could be compared to imagery from the Lunar Orbiter program. This procedure is a bit involved because of the way that both of these databases of imagery are being archived. The Orbiter images exist as photographic negatives and the Clementine images exist on CDs as written in the PDS (Planetary Data Systems) format. This procedure is thus easy for the Orbiter imagery, which only needs to be obtained and then scanned. The Clementine image needs to be obtained and put through four programs: NasaView, Adobe Photoshop, Erdas Imagine, and an IDL (Interactive Data Language) code. Using the region of the lunar surface around the crater Aristarchus, digital comparisons yielded that there was no evidence that the lunar surface had changed. It did however prove that the major differences that were seen were due to inherent differences in the images and due to the sun\u27s illumination angle on the crater. Therefore, it seems logical to conclude that in order to obtain better results (that may translate into actual changes in lunar surface) it may be better to try to minimize the differences in image structure and resolution along with trying to correct for different illumination angles

Earth Observation: Data, Processing and Applications. Volume 2B: Processing — Image Rectification

Eds. Harrison, B.A., Jupp, D.L.B., Lewis, M.M., Sparks, T., Mueller, N., Byrne, G

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

On-photo Restitution and Management of an Angular Size-Illusion\u27s Behaviour Experienced in Architectural/Urban Spaces

An angular size-illusion refers to a contradictory effect related to a seeming size-decrease of focused objects as the observer approaches them. This paper continues the research in this field, trying to establish fundamental principles of how to: (a) efficiently perform an on-photo restitution of an illusion\u27s behaviour (expressed by illusion descriptors) when it is experienced in architectural/urban spaces during movement under the influence of available triggers (represented by illusion determinants of physical nature), and (b) manage it computationally so as to be sustainable for contemporary professional practice. To explain that conceptually, one simple architectural/urban matrix is chosen and digitally photographed. Images are photogrammetrically processed and determinants-related data obtained. Subsequently, descriptors-related outputs are calculated by applying derived mathematical equations (expressed in function of those determinants-related data). Then, behaviour-charts are created and corresponding illusion-characteristics read-off. Finally, it is illustrated how to manage (intentionally modify) the restituted behaviour by varying values of acquired determinants-related data. Given results allow also to "design a new illusion" (to programme and control it) by simulating in digital VR/AR environments. Thus, any cause of unwanted/unpredictable visual impression degradations of important architectural/urban structures can be prevented or minimized (by planning and performing adequate spatial/physical interventions on existing/reconstructed/newly designed matrices)