Analysis of the symbiotic star AG Pegasi

High and low dispersion IUE data are analyzed in conjunction with coincident ground based spectrophotometric scans and supplementary infrared photometry of the symbiotic object AG Pegasi. The IUE observations yield an improved value of E(B-V) = 0.12. The two stellar components are easily recognized in the spectra. The cool component may be an M1.7 III star and the hot component appears to have T (sub eff) of approximately 30000 K. The emission lines observed in the ultraviolet indicate two or three distince emitting regions. Nebular component ultraviolet intercombination lines suggest an electron density of several times 10 billion/cu cm

Analysis of high excitation planetary nebulae

Combination of extensive ground-based spectroscopic observation of high excitation planetary with IUE data permit determination not only of improved diagnostics but also better abundances for elements such as C and N that are well represented in the ultraviolet spectra and also C, Ar and metals Na, Ca and K whose lines appear in the wavelength 3200-8100 A region

Deflative scheme of the geographic cycle in an arid climate

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The shape on the map

Abstract included in text

Using moment invariants for classifying shapes on large scale maps

Automated feature extraction and object recognition are large research areas in the field of image processing and computer vision. Recognition is largely based on the matching of descriptions of shapes. Numerous shapes description techniques have been developed, such as scalar features (dimension, area, number of corners etc.), Fourier descriptors and moment invariants. These techniques numerically describe shapes independent of translation, scale and rotation and can be easily applied to topographical data. The applicability of the moment invariants technique to classify objects on large-scale maps is described. From the test data used, moments are fairly reliable at distinguishing certain classes of topographic object. However, their effectiveness will increase when fused with the results of other techniques

Topographic Object Recognition Through Shape

Automatic structuring (feature coding and object recognition) of topographic data, such as that derived from air survey or raster scanning large-scale paper maps, requires the classification of objects such as buildings, roads, rivers, fields and railways. The recognition of objects in computer vision is largely based on the matching of descriptions of shapes. Fourier descriptors, moment invariants, boundary chain coding and scalar descriptors are methods that have been widely used and have been developed to describe shape irrespective of position, orientation and scale. The applicability of the above four methods to topographic shapes is described and their usefulness evaluated. All methods derive descriptors consisting of a small number of real values from the object's polygonal boundary. Two large corpora representing data sets from Ordnance Survey maps of Purbeck and Plymouth were available. The effectiveness of each description technique was evaluated by using one corpus as a training-set to derive distributions for the values for supervised learning. This was then used to reclassify the objects in both data sets using each individual descriptor to evaluate their effectiveness. No individual descriptor or method produced consistent correct classification. Various models for the fusion of the classification results from individual descriptors were implemented. These were used to experiment with different combinations of descriptors in order to improve results. Overall results show that Moment Invariants fused with the minfusion rule gave the best performance with the two data sets. Much further work remains to be done as enumerated in the concluding section

Data Fusion for Topographic Object Classification

This paper presents research conducted into the automatic recognition of features and objects on topographic maps (for example, buildings, roads, land parcels etc.) using a selection of shape description methods developed mostly in the field of computer vision. In particular the work here focuses on the proposal and evaluation of fusion techniques (at the decision level of representation) for the classification of topographic data. A set of Ordnance Survey large-scale digital data (1:1250 and 1:2500) was used to evaluate the classification performance of the shape recognition methods used. Each technique proved partially successful in distinguishing classes of objects, however, no one technique provided a general solution to the problem. Further outlined experiments combine these techniques, using a data fusion methodology, on the real-world problem of checking and assigning feature codes in large-scale Ordnance Survey digital data