Object classifier often operates by making decisions based on the values of several shape properties measured from an image of the object. The paper introduces a unique definition of measure for 2-D geometrical object shape detection. Using this definition different object shapes can be identified on the basis of their degree of fitness parameter. Basically, we have fitted a 2-D polygon/curve on the object as a best fitted polygon/curve and computed the parameter degree of fitness which is the ratio of the matching area and non-matching area due to the fitted polygon/curve and the object both. The results show the effectiveness of the proposed measure.
Passive millimeter wave (PMMW) imaging systems have attracted an increasing interest over the past years due to their superior poor-weather performance compared with visible and IR systems. In passive imaging, the spatial information acquired is strictly band-limited. A major drawback to PMMW images is their poor angular resolution. Also, another problem of singlechannel PMMW imaging systems is the slow response time due to the lack of thermal sensitivity. The imager could operate at TV (television) rates using a number of parallel channels which may reduce the extent of this problem. In multi-channel, differences between the responses of individual channels can introduce noise into the image, which can obscure details of interest. The proposed noise-removal technique is a two-pass combined method of two techniques. One technique is for removing the DC component in the frequency domain and the other one is statistical filtering based on homogeneous region in the image (spatial) domain, followed by high-boost filtering by 3×3 mask for enhancement the image. High quality images are presented to demonstrate the potential of this technique.
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