KEYWORDS: Visualization, Visual process modeling, Image retrieval, Computing systems, Visual compression, Information technology, Control systems, Databases, Systems modeling, Image compression
When fabricating a large area holographic diffuser by cascading hologram of diffusion patterns with specified area, it was found that a fluctuation of diffraction intensity occurred at the boundaries of the two adjacent exposed areas. It was affirmed experimentally that this problem could be alleviated by controlling the intensity distribution of reference laser beam around the boundary of each exposure area. Using this method, the fluctuation was reduced considerably. For better compensation of the non-uniformity a theoretical analysis for the diffusion process of photopolymer is discussed and compared with the experimental results.
In this paper, we describe a new approach to managing large image databases, which we call active browsing. Active browsing integrates relevance feedback into the browsing environment, so that users can modify the database's organization to suit the desired task. Our method is based on a similarity pyramid data structure, which hierarchically organizes the database, so that it can be efficiently browsed. At coarse levels, the similarity pyramid allows users to view the database as large clusters of similar images. Alternatively, users can 'zoom into' finer levels to view individual images. We discuss relevance feedback for the browsing process, and argue that it is fundamentally different from relevance feedback for more traditional search-by-query tasks. We propose two fundamental operations for active browsing: pruning and reorganization. Both of these operations depend on a user-defined relevance set, which represents the image or set of images desired by the user. We present statistical methods for accurately pruning the database, and we propose a new 'worm hole' distance metric for reorganizing the database, so that members of the relevance set are grouped together.
The advent of large image databases (> 10,000) has created a need for tools which can search and organize image automatically by their content. This paper presents a method for designing a hierarchical browsing environment which we call a similarity pyramid. The similarity pyramid groups similar images together while allowing users to view the database at varying levels of resolution. We show that the similarity pyramid is best constructed using agglomerative (bottom-up) clustering methods, and present a fast-sparse clustering method which dramatically reduces both memory and computation over conventional methods. We then present an objective measure of pyramid organization called dispersion, and we use it to show that our fast-sparse clustering method produces better similarity pyramids than top down approaches.
KEYWORDS: Visual process modeling, Visualization, Image retrieval, Systems modeling, Object recognition, Databases, Information technology, Image processing, Visual system, Data modeling
Next generation content-based retrieval systems for image and multimedia databases will benefit from utilizing higher level models of human visual processing. This includes incorporating models of early vision as well as more specialized areas like the IT cortex, which is thought to be important in object recognition. Artistic representation is typically based on abstraction of visual content in images. Analogies of various modes of artistic representation can be see in scientific investigations of the visual system. These two observations suggest that an examination of traditional artistic representation may aid in constructing robust feature spaces for content abstraction in image retrieval. In addition, artistic renderings can be used to test the performance of models of image similarity in existing content-retrieval systems.
Stochastic halftone screens can be used in ordered dither halftoning algorithms to generate binary image textures that have shaped power spectra. This paper will examine some techniques for modeling perception of binary texture. Alternative methods for constructing grayscale dot profiles will be discussed, with the aim of improving the visual optimality of the entire grayscale dot profile.
Recent developments in adaptive learning systems allow quantifying of a user's qualitative aesthetics and provide an alternative to more traditional approaches to image manipulation. Image enhancement or other desired manipulations can be thought of as nonlinear transformations from an input space of arbitrary images into an output space of desired aesthetic images. Derivation of imaging manipulations of this type can be cast as supervised learning problems. Approaches to reduce the dimensionality of the transformations described above are highly desirable. One approach is to define transformations through more structured descriptors than raw image pixels. Transformations are then learned between sets of image metrics as opposed to sets of image pixels. Adaptive neural networks can be used to learn arbitrary imaging transformations from example images. An alternative approach that is functionally equivalent is to use an adaptive fuzzy logic controller. Fuzzy logic can be thought of as a linguistically understandable meta-representation of an underlying functional transformation. Fuzzy logic also provides a possible link between semantic labeling of qualitative image characteristics and the underlying raw image data.
KEYWORDS: RGB color model, Visualization, Image processing, Visual process modeling, Image enhancement, 3D image processing, Color image processing, Solid modeling, Systems modeling, CRTs
This paper explores the use of a perceptually-based color space in the context of color image enhancement. Both algorithmic processing of images as well as visualization of the effects of processing algorithms are examined using CIELUV as a uniform perceptual model. Both the methodology and observations regarding the limited nature of real world device color gamuts are not specifically tied to the LUV color space, and should apply to other
uniform perceptual color models as well.
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