Application of Skylab EREP photographs to study of the modern episode of accelerated erosion in southern Arizona

The author has identified the following significant results. Indexing and analysis of the SL 2, SL 3, and SL 4 photos of the project area has shown that S-190A coverage with less than 30% clouds totals about 123,000 sq km. The 70-mm unenlarged color, color-infrared, B/W red, and B/W green bands from S-190A are of good to excellent quality; the B/W IR bands from SL 2 are excessively grainy and have very low resolution; those from SL 3 are better but nevertheless have low resolution. The 5-inch unenlarged color transparencies from S-190B are generally of excellent photographic quality. However, where cloud cover is extensive, commonly the S-190A and S-190B color and color-IR photos are correctly exposed for the clouds but considerably underexposed for the ground. The 4X enlargements of all bands of S-190A photos taken by SL 2 are much fuzzier than they should be; evidently the enlarger was not focused properly. The 4X enlargements from SL 3 are much superior

A color indexing system based on perception (CISBOP)

Color is an important feature for searching large databases of images, since it is invariant with respect to camera position, object orientation and size, and partial occlusion. There are currently many color-based image indexing systems (e.g. Flickner et al., 1995), which all basically work by building color histograms in RGB space. Our goal was to construct a color-indexing system based on the known properties of the human color vision system. Our images were chosen from a large commercially available (COREL) image database consisting of 60.000 digitized photographs. For each image, we buildt a color histogram by converting the RGB triplets for each pixel into color-opponent coordinates. These luminance, red-green, and yellow-blue coordinates correspond to the color directions found in human color vision (Krauskopf, Williams Heeley, 1982). Luminance was averaged for each color value, and the resulting color circle was split into 127 segments. The categories were constructed so that the number of hue categories increased with increasing saturation. Six different rings were used for saturation, with the radius doubling as saturation increases. Thus, there is little discrimination of hues for unsaturated colors, whereas there are 64 different hues at the most saturated level. Two different histograms were built, one using the frequencies with which the different colors occurred, and another one that used the average luminance level of each color segment. We used a query-by-example stragey for searching. Several different distance measures were evaluated by asking human observers to make similarity judgments. For most images, this search, based on color only, results in images that are perceptually and often semantically similar to the target image

Probabilistic Color Image Classifier Based on Volumetric Robust Features

Need of more sophisticated methods to handle color images becomes higher due to the usage size and volume of images To retrieve and index the color images there must be a proper and efficient indexing and classification method to reduce the processing time false indexing and increase the efficiency of classification and grouping We propose a new probabilistic model for the classification of color images using volumetric robust features which represents the color and intensity values of an region The image has been split into number of images using box methods to generate integral image The generated integral image is used to compute the interest point and the interest point represent the volumetric feature of an integral image With the set of interest points computed for a source image we compute the probability value of other set of interest points trained for each class to come up with the higher probability to identify the class of the input image The proposed method has higher efficiency and evaluated with 2000 images as data set where 70 has been used for training and 30 as test se