Thresholding based on Grey Levels, Gradient Magnitude and Spatial Correlation

Image segmentation gained significant importance in recent years. The goal of segmentation is partitioning an image into distinct regions containing each pixel with similar attributes. Several Image segmentation techniques exist based on thresholding and clustering. Image segmentation based on thresholding is typically doesn’t find any objects and bounds (lines, curves, etc.) in image. To boost the segmentation performance based on thresholding strategies, a unique strategy that integrates the spacial information between pixel’s is designed. The proposed strategy utilizes pixel’s grey level Gradient magnitude and gray level spacial correlation at intervals a part to construct a unique two dimensional bar graph, known as GLGM & GLSC. This technique is valid through segmenting many real world pictures. Experimental results proved this method outperforms several existing Thresholding strategies.

Differential contrast imaging with differential hysteresis processing

The spacial information of images can be characterized with contrast patterns. We analyzed the spacial contrast distribution in micrograph data with differential hysteresis processing using a PiXISION-AP128 imaging workstation (JEOL USA Inc., Peabody, MA) and found that discrete spacial image components have discrete contrast characters which are defined by specific contrast levels and specific contrast ranges. Consecutive contrast segmentation at increasing contrast levels produced a limited number of contrast patterns which were discretely recognizable and distinguishable. These contrast patterns have an unique accumulative property, i.e., a digital image can be segmented into contrast patterns and reassembled from its contrast patterns. Contrast patterns can be mathematically defined by a differential hysteresis range (DHR). The DHR (DHR x-y) describes differential contrasts of a pattern with two intensity values as contrasts which are larger than the first value (x) and smaller than the second value (y). The contrast patterns can be visualized as images through linear scaling to the maximum visual perception range (8-bit). The DHR provides an universal imaging parameter since it applies to all images independent of source, size, bit depth, and spacial content.

Surface Brightness Profiles of 68 Globular Clusters Derived From Electronic Camera Recordings

Images of 68 globular clusters have been recorded in 125 exposures made with the electronic camera of the U.S. Naval Observatory on the 24-inch, 40-inch and 61-inch reflecting telescopes at the Flagstaff Station. The images were electronically malfocussed to allow the integration of light from the fainter cluster stars without saturation of the central portions of the brighter star images. Spacial information thus lost was partly regained by subsequent linear deconvolution of the cluster profiles by means of a star profile used as the point spread function.