Target Detection Using Fractal Geometry

The concepts and theory of fractal geometry were applied to the problem of segmenting a 256 x 256 pixel image so that manmade objects could be extracted from natural backgrounds. The two most important measurements necessary to extract these manmade objects were fractal dimension and lacunarity. Provision was made to pass the manmade portion to a lookup table for subsequent identification. A computer program was written to construct cloud backgrounds of fractal dimensions which were allowed to vary between 2.2 and 2.8. Images of three model space targets were combined with these backgrounds to provide a data set for testing the validity of the approach. Once the data set was constructed, computer programs were written to extract estimates of the fractal dimension and lacunarity on 4 x 4 pixel subsets of the image. It was shown that for clouds of fractal dimension 2.7 or less, appropriate thresholding on fractal dimension and lacunarity yielded a 64 x 64 edge-detected image with all or most of the cloud background removed. These images were enhanced by an erosion and dilation to provide the final image passed to the lookup table. While the ultimate goal was to pass the final image to a neural network for identification, this work shows the applicability of fractal geometry to the problems of image segmentation, edge detection and separating a target of interest from a natural background

On Multifractal Structure in Non-Representational Art

Multifractal analysis techniques are applied to patterns in several abstract expressionist artworks, paintined by various artists. The analysis is carried out on two distinct types of structures: the physical patterns formed by a specific color (``blobs''), as well as patterns formed by the luminance gradient between adjacent colors (``edges''). It is found that the analysis method applied to ``blobs'' cannot distinguish between artists of the same movement, yielding a multifractal spectrum of dimensions between about 1.5-1.8. The method can distinguish between different types of images, however, as demonstrated by studying a radically different type of art. The data suggests that the ``edge'' method can distinguish between artists in the same movement, and is proposed to represent a toy model of visual discrimination. A ``fractal reconstruction'' analysis technique is also applied to the images, in order to determine whether or not a specific signature can be extracted which might serve as a type of fingerprint for the movement. However, these results are vague and no direct conclusions may be drawn.Comment: 53 pp LaTeX, 10 figures (ps/eps

KLASIFIKASI PENYAKIT PARU BERDASARKAN CITRA X-RAY THORAX MENGGUNAKAN METODE FRAKTAL BOX COUNTING

The lungs are important and vital organs that are easily infected. Lung examination can be done through examination of x-ray images, but the clinical diagnosis of the results of x-ray images is difficult. In making it easier to analyze the diagnosis of the results of the x-ray image, this research carried out the process of classifying x-ray images based on the type of disease.. The image that was processed was 120 (one hundred and twenty) chest x-ray images by segmenting the lung area. From this lung area, then canny edge detection is done to take spots from lung disease. From smart fractal edge detection values are obtained, which will be calculated using the box calculation method, so classification can be done. The results of the experiment using the nearest k-neighbor method (K-NN), where the results with the greatest accuracy are shown by the value K = 5 which is 79.65% and the lowest accuracy at the value K = 7 is 71.28%