Computer assisted analysis of auroral images obtained from high altitude polar satellites

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images

Mediabeads: An architecture for Path-Enhanced Media applications

. Telephone: + Intl. 732-562-3966. Tagging digital media, such as photos and videos, with capture time and location information has previously been proposed to enhance its organization and presentation. We believe that the full path traveled during media capture, rather than just the media capture locations, provides a much richer context for understanding and "re-living" a trip experience, and offers many possibilities for novel applications. We introduce the concept of path-enhanced media, in which media is associated and stored together with a densely sampled path in time and space, and we present the MediaBeads architecture for capturing, representing, browsing, editing, presenting, and searching this data. The architecture includes, among other things, novel data representations, new algorithms for automatically building movie-like presentations of trips, and novel search applications

Geometrical Methods for Lightness Adjustment in YCC Color Spaces

image processing, geometrical methods, image enhancement, color adjustment Lightening or darkening an image is a fundamental adjustment used to improve aesthetics or correct exposure. This paper describes new geometrical algorithms for lightness adjustment, implementing fast traversal of colors along lightness-saturation curves, applicable when the data starts naturally in YCC space (JPEG images or MPEG videos). Here, YCC refers generically to color spaces with one luminance and two color difference channels, including linear YCC spaces and CIELAB. Our first solution uses a class of curves that allows closed-form computation. Further assuming that saturation is a separable function of luminance and curve parameter simplifies the computations. This approach reduces clipping and better adjusts lightness together with saturation. Preliminary evaluation with 96 images finds good subjective results, and clipping is reduced to about 5 % of a prior approach

Model-Driven Image Analysis to Augment Databases

In this paper we consider how information may be obtained from images. To search large image collections we need to search on secondary parameters. We may look for images containing certain types of objects, for images where the objects are of a certain size or shape, or for images having certain features. Since we now have techniques to rapidly acquire and store many images, we need techniques for automatic image analysis to generate such parameters. This paper describes a promising category of image analysis, namely model-driven methods. Two examples, operating in very different domains, are presented. 1. Introduction To be able to retrieve images stored in databases we must associate identifying parameters with each of the images. It is through these parameters that we can select stored images for display, comparison, and further analysis. Primary parameters are produced when the images are obtained, and describe the imaging event and its process. For satellite images of earth we ..

Honest image thumbnails: Algorithm and subjective evaluation

image thumbnails, image quality, image browsing, blur, noise Image thumbnails are commonly used for selecting images for display, sharing or printing. Current, standard thumbnails do not distinguish between high and low quality originals. Both sharp and blurry originals appear sharp in the thumbnails, and both clean and noisy originals appear clean in the thumbnails. This leads to errors and inefficiencies during image selection. In this paper, thumbnails generated using image analysis better represent the local blur and the noise of the originals. The new thumbnails provide a quick, natural way for users to identify images of good quality, while allowing the viewer’s knowledge to select desired subject matter. A subjective evaluation using twenty subjects shows the new thumbnails are more representative of their originals for blurry images. In addition, there are no significant differences between the results of the new thumbnails and the standard thumbnails for clean images. The noise component improves the results for noisy images, but degrades the results for textured images. The blur component of the new thumbnails may always be used. The decision to use the noise component of the new thumbnails should be based on testing with the particular image mix expected for the application