In the field of image processing, identifying object is based on appropriately chosen descriptors. The proper choice of descriptors in pattern recognition is the most sensitive criteria as small misjudgments may lead to wrong identification. There have been several algorithms proposed and worked in this field. Here, the idea is to identify the objects in an uncomplicated method while being computationally efficient. This paper is based on identifying the patterns of objects / polygons based on the recording area of the objects per line scan. The descriptors here are invariant to translation and become invariant to scaling after normalization. Here the objects considered are regular polygons in various background conditions. In order to reduce the noise, after segmentation by thresholding along with labeling and area filtering is done. Along with polygon identification, descriptors for all the numbers are also shown. In order to identify the objects, the average magnitude difference function (AMDF) is applied to each characteristic curve. This paper also shows that though AMDF is a dissimilarity measure, it works better here than auto correlation function (ACF), which is a similarity measure.
A track-before-detect (TBD) algorithm based on elliptical Hough transform (EHT) is presented for jointly detecting and tracking weak ballistic targets during its exoatmospheric flight. The new method makes use of the fact that the ballistic target follows the elliptical orbit when restricted to two-body problem. The relationship between raw radar measurements in the data space and the elliptical parameters in the parameter space is established with coordinate transformations. The EHT detection algorithm is designed and the orbit planarity is also considered to reduce the noise accumulation effect. The detection performances related to primary and second thresholds and signal-to-noise ratio (SNR) are analyzed and verified through simulations. The advantage of the new method lies in it can not only detect and track weak ballistic targets but also can predict the impact point by using available parameters.
Groundwater which occurs beneath the earth’s surface is an important natural resource. The study area, part of Yadadri Bhuvanagiri district, Telangana, which is a part of Musi river basin, is a hard rock terrain covering an area of 1107 sq km, part of the eastern Dharwar craton (EDC) of the southern India peninsular shield. Exploration of groundwater in this terrain is difficult because groundwater is confined to weathered and fractured layers. The present study aims to delineate groundwater potential zones using the multi-criteria decision analysis (MCDA) technique. An integrated application of remote sensing and GIS is very effective in mapping features that provide information about groundwater potential zones using several thematic layers such as geology, soil, geomorphology, slope, lineament density, drainage density, rainfall, and land use/land cover maps. The groundwater potential zones of the study area are classified into five categories, namely very high, high, medium, low, and very low and they occupied an area of 116.8, 381.29, 345.15, 149.70, and 115.42 km2 respectively. The results were validated using discharge data from existing wells in the study area, and the majority of the high-productivity wells are located in very high and high groundwater potential zones. The groundwater potential zone mapping using this model will be a very useful improving the groundwater resources of the study area.
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