Norman R. Guivens

Genetic algorithms for unconventional imaging

Unconventional imaging techniques obtain high resolution images of objects at very long ranges without the use of large diameter primary optical elements. Cost and weight constraints lead us to consider methods for using sparse arrays of subapertures. In this paper, we present a genetic algorithm method for designing sparse arrays of subapertures for an unconventional imaging technique known as correlography. We have compared the solutions found using genetic algorithms to other techniques for generating arrays with filled autocorrelations. The results of this comparison are presented in this paper.

Adaptive ray casting (ARC) algorithm for simulation of machine vision systems

Optical sensor simulations based on high fidelity models of illumination, scenes, cameras, and signal processors can accurately predict the performance of machine vision systems. These simulations typically render images of the scene from solid models that define each object in the sensors field of view by a ray casting algorithm, then pass the image through models of the camera (receiver) and the signal processor. Conventional ray casting algorithms cast a uniformly spaced grid of rays toward the scene from the camera and add the returns computed for each ray to the appropriate pixel of the image. This paper describes an adaptive ray casting (ARC) algorithm that dynamically adjusts the resolution of the ray grid, within bounds set by the user, to match the level of detail present in each part of the image. The ARC Algorithm generates a resolution map for the scene specifying the resolution required in each pixel, then it dynamically adjusts the spacing of the ray grid to match the required resolution during the rendering process. The resolution map is stored in the same array as the image, allowing the algorithm to run efficiently on systems with limited memory. This ARC Algorithm renders images of very high fidelity without extreme execution times.

Prototype model of optical detection systems

The authors have developed a prototype model of optical detection systems based on a set of primitive mathematical operations that are characteristic of elements in a detection system. The model can cascade these operations arbitrarily to simulate very complex detection systems without requiring cumbersome amounts of input for simple detection systems. Each execution of the model cascades an independent single instance of the noise associated with each operation drawn from the mathematically correct distributions in the same manner as an actual detection system. Thus, ensembles of images from the simulation exhibit the same statistical properties in each pixel as an ensemble of images obtained from a corresponding optical sensor. The resulting images are suitable for development and evaluation of image processors and machine vision systems.

Simulation of illumination for machine vision and inspection

Illumination subsystems are critical elements of most machine vision and inspection systems. The linear response of most optical detectors generally requires much more uniform illumination than the logarithmic response of the human eye to achieve a similar level of performance. Excessive illumination can saturate optical detector elements, while insufficient illumination causes excessive shot noise that can cripple system performance. Speckle from coherent or partially coherent illumination can also affect system performance. Computer simulations of machine vision and inspection systems, like SPARTAs SENSORSIM, permit accurate analysis of various illumination designs to determine their suitability for a particular application. These simulations permit rapid analysis of system configurations and variation of system parameters to identify optimal designs on a price/performance curve. SENSORSIM can also provide images or other signatures at intermediate stages of the generation process for isolation and analysis sources of degradation in a sensor system. This sort of analysis often is not possible in laboratory experiments. Although models of more limited scope may be useful for some analyses, such models cannot support analysis of phenomena that depend upon interaction among several components or subsystems. Thus, simulations like SENSORSIM provide an invaluable capability for optimizing the cost and performance of optical sensor systems.

A Performance Metric for Coherent Active Imaging Systems

This paper describes a performance metric for the evaluation of active coherent imaging systems. This metric can be determined for any system using analytical considerations or measured using standard targets. It has implications for comparison of different imaging systems, optimization of imaging systems, and identification of areas in which significant improvements in particular systems can be realized. The imaging system performance metric described here is suitable for analysis of unconventional imaging systems in which the image is sensed by an array of discrete detectors or in which the image is produced by manipulation of arrays of data. The implications of this formula for determination of photon-efficient and optimized systems are discussed. An important result of this paper will be to show that the efficient use of photons is only part of the story. An efficient system must still be optimized to make best use of the imaging hardware.

Reduced computation algorithm for phase retrieval

Abstract not available.

Active/Passive Multiwavelength Sensor Simulation

The combination of contractive solid geometry (CSG) and ray casting provides a number of advantages for modeling target signatures obtained using active laser radar systems. These include ease of modeling complex targets and a methodology well-suited for the incorporation of most important laser radar phenomenology. For applications requiring image understanding or discrimination, the use of multiple types of sensors may be required. Fusion of information from several types of sensor may provide discrimination or identification capability not achievable using a single sensor. Sensor fusion studies require a consistent set of signatures for a given target, and such sets are not currently available from measurements. The use of separately simulated signatures generated using different target models may introduce artificial signature differences due to differences in the target model which would not be present in measurements made with real systems. Our approach has been to retain the advantages of the ray casting and CSG approach, which is well-suited to active systems, and to make use of mapping techniques to include the effects of surface temperature and emissivity variations, permitting the calculation of infrared signatures. This paper discusses high-resolution signature generation for both active and passive scenes. Phenomenology addressed includes the illumination beam profile, material bidirectional reflectance effects, glint insertion, and bistatic illumination for active images, and incorporation of temperature and emissivity information for passive scenes. Simula-tion of receiver optics and detector effects are discussed for both types of sensors. In the final section, examples of multimode imagery will be presented.