Search

Background clutter characterization in infrared imagery has become an actively researched field, and several clutter models have been reported. These models attempt to evaluate the target detection and recognition probabilities that are characteristic of a certain scene when specific target and human visual perception features are known. The prior knowledge assumed and required by these models is a severe limitation. Furthermore, the attempt to model subjective and intricate mechanisms such as human perception with general mathematical formulas is controversial. In this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised learning mechanism based on genetic programming foundations. A set of characteristic scene and target features with a demonstrated influence on the human visual perception mechanism is first extracted from the original images. Then, the correlations between these features and detection performance results obtained by visual observer tests on the same set of images are captured into models by a learning algorithm. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper. © 2000 Society of Photo-Optical Instrumentation Engineers.

Histogram equalization and specification have been widely used to enhance information in a gray scale image, with histogram specification having the advantage of allowing the output histogram to be specified as compared to histogram equalization, which attempts to produce an output histogram that is uniform. Unfortunately, expanding histogram techniques to color images is not very straightforward. Since humans are sensitive to chromatic changes, care must be taken to ensure that incorrect colors are not produced. Additionally, expanding the one-dimensional histogram used in gray level histogram techniques to a joint histogram (usually of three variables representing the primary colors of red, green, and blue) can yield specified histograms which have no physical meaning, hence making it difficult to determine the set of histograms required for a desired enhancement. In this paper, we describe a method of extending gray level histogram specification to color images by performing histogram specification on the luminance, saturation, and hue components in the color difference C-Ycolor space. These methods take into account the relationship between the luminance and saturation components while yielding specified histograms that produce natural-looking results. © 1999 SPIE and IS&T.

Describes the image-processing requirements of different kinds of applications, and lists free sources to use while learning about your particular image processing needs. Types of processing including, computer graphics and computer vision; Requirements for working with multi-sensor fusion of multiple-wavelength spatial data. INSETS: Do on-line services support electro-optics..., by Terry Palmer.;Downloading image processing programs on the Internet..

The visually handicapped computer user or programmer is faced with a very difficult task in interfacing with computing hardware. The types of equipment currently available for this purpose fall into one of several general categories: expensive, nonportable, and domain-specific. In the current paper, we describe a new terminal device designed specifically for the visually handicapped that answers all of the problems mentioned above. The work is already under way, with a prototype model is currently in use by a visually handicapped programmer on the development team. In the remainder of this paper we will present a report on the current status of our project as well as plans and milestones for future work.

The problem of reconstructing the support of an imaged object from the support of its autocorrelation is addressed within the framework of genetic algorithms. First, we propose a method of coding binary sets into chromosomes that is both efficient and general, producing reasonably short chromosomes and being able to represent convex objects, as well as some non-convex and even clustered ones. Furthermore, in order to compensate for the computational costs normally incurred when genetic algorithms are applied, a novel multiresolution version of the algorithm was introduced and tested. The multiresolution genetic algorithm consists of a superposition of multiple algorithms evolving at different resolutions, sequentially. Upon occurrence of some convergence criteria at the current scale, the genetic population was mapped at a superior scale by a coarse-to-fine mapping that preserved the progress registered previously. This mapping is implemented in a genetic algorithm framework by a new genetic operator called cloning. A number of experiments of object support reconstruction were performed and the best results from different genetic generations were depicted in chronological sequence. While both versions of genetic algorithms achieved good results, the multiresolution approach was also able to substantially improve the convergence speed of the process. The effectiveness of the method can be extended even further if a parallel implementation of the genetic algorithm is employed. Finally, alternate coding methods could be readily used in both the standard and the multiresolution approaches, with no need for further adaptations of the basic structure of the genetic algorithm. © 1997 Society of Photo-Optical Instrumentation Engineers.

Visual display systems have become so advanced with available options, it is difficult to decide which type of system is needed. Traditionally, the choice has been to select the system with the greatest level of functional and physical fidelity that can be procured. While most would assume that more realism results in better training and performance, research (Dwyer, 1972; Marsh, 1983; Richey, 1986) suggests otherwise. Realism in a display system may serve to be more distracting than helpful, depending upon the training objective. The initial intent of this project was to locate any and all information pertaining to visual display characteristics as they relate to human visual functions or to visual training requirements. A selected literature search was conducted to locate relevant information, data bases or taxonomies that relate the above information. No current taxonomy of this kind was located; therefore, the efforts of the project turned in the direction of formulating a taxonomic structure based on information derived from various disciplines such as instructional technology, human psychology, and computer engineering. General guidelines only, with no specific information, pertaining to the selection of visual media for given situations were found in the instructional technology literature. Based on those guidelines, four general types of presentation media were selected: alphanumerics, 2-dimensional graphics, 3-dimensional graphics, and scene quality. Next, display specifications for these presentations were identified through the aid of engineering and vendor-provided design parameters. Finally, human limitations were applied and psychophysical transformations allowed the determination of final display descriptions.

Abstract not available