Christopher L. Howell

Image fusion algorithm assessment using measures of complementary and redundant information

Often various amounts of complementary information exist when imagery of the same scene is captured in different spectral bands. Image fusion should merge the available information within the source images into a single fused image that contains more relevant information compared to any single source image. The benefits of image fusion are more readily seen when the source images contain complementary information. Intuitively complementary information allows for measurable improvements in human task performance. However, quantifying the effect complementary information has on fusion algorithms remains open research. The goal of this study is to quantify the effect of complementary information on image fusion algorithm performance. Algorithm performance is assessed using a new performance metric, based on mutual information. Human perception experiments are conducted using controlled amounts of complementary information as input to a simple fusion process. This establishes the relationship between complementary information and task performance. The results of this study suggest a correlation exists between the proposed metric and identification task performance.

Neural net algorithm for target ID trained on simulated data

Simulation-based training for target acquisition algorithms is an important goal for reducing the cost and risk associated with live data collections. To this end, the US Army Night Vision and Electronic Sensors Directorate (NVESD) has developed high-fidelity virtual scenes of terrains and targets using the DIRSIG in pursuit of a virtual DRI (detect, recognize, identify) capability. In this study, the NVESD has developed a neural network (NN) algorithm that can be trained on simulated data to classify targets of interest when presented with real data. This paper discusses the classification performance of a NN algorithm and the potential impact training with simulated data has on algorithm performance.

Wavefront-Guided Versus Wavefront-Optimized Photorefractive Keratectomy: Visual and Military Task Performance

PURPOSE To compare visual performance, marksmanship performance, and threshold target identification following wavefront-guided (WFG) versus wavefront-optimized (WFO) photorefractive keratectomy (PRK). METHODS In this prospective, randomized clinical trial, active duty U.S. military Soldiers, age 21 or over, electing to undergo PRK were randomized to undergo WFG (n = 27) or WFO (n = 27) PRK for myopia or myopic astigmatism. Binocular visual performance was assessed preoperatively and 1, 3, and 6 months postoperatively: Super Vision Test high contrast, Super Vision Test contrast sensitivity (CS), and 25% contrast acuity with night vision goggle filter. CS function was generated testing at five spatial frequencies. Marksmanship performance in low light conditions was evaluated in a firing tunnel. Target detection and identification performance was tested for probability of identification of varying target sets and probability of detection of humans in cluttered environments. RESULTS Visual performance, CS function, marksmanship, and threshold target identification demonstrated no statistically significant differences over time between the two treatments. Exploratory regression analysis of firing range tasks at 6 months showed no significant differences or correlations between procedures. Regression analysis of vehicle and handheld probability of identification showed a significant association with pretreatment performance. CONCLUSIONS Both WFG and WFO PRK results translate to excellent and comparable visual and military performance.

Investigating binocular summation in human vision using complementary fused external noise

The impact noise has on the processing of visual information at various stages within the human visual system (HVS) is still an open research area. To gain additional insight, twelve experiments were administered to human observers using sine wave targets to determine their contrast thresholds. A single frame of additive white Gaussian noise (AWGN) and its complement were used to investigate the effect of noise on the summation of visual information within the HVS. A standard contrast threshold experiment served as the baseline for comparisons. In the standard experiment, a range of sine wave targets are shown to the observers and their ability to detect the targets at varying contrast levels were recorded. The remaining experiments added some form of noise (noise image or its complement) and/or an additional sine wave target separated between one to three octaves to the test target. All of these experiments were tested using either a single monitor for viewing the targets or with a dual monitor presentation method for comparison. In the dual monitor experiments, a ninety degree mirror was used to direct each target to a different eye, allowing for the information to be fused binocularly. The experiments in this study present different approaches for delivering external noise to the HVS, and should allow for an improved understanding regarding how noise enters the HVS and what impact noise has on the processing of visual information.

HIL range performance of notional hyperspectral imaging sensors

In the use of conventional broadband imaging systems, whether reflective or emissive, scene image contrasts are often so low that target discrimination is difficult or uncertain, and it is contrast that drives human-in-the-loop (HIL) sensor range performance. This situation can occur even when the spectral shapes of the target and background signatures (radiances) across the sensor waveband differ significantly from each other. The fundamental components of broadband image contrast are the spectral integrals of the target and background signatures, and this spectral integration can average away the spectral differences between scene objects. In many low broadband image contrast situations, hyperspectral imaging (HSI) can preserve a greater degree of the intrinsic scene spectral contrast for the display, and more display contrast means greater range performance by a trained observer. This paper documents a study using spectral radiometric signature modeling and the U.S. Army’s Night Vision Integrated Performance Model (NV-IPM) to show how waveband selection by a notional HSI sensor using spectral contrast optimization can significantly increase HIL sensor range performance over conventional broadband sensors.

Face acquisition camera design using the NV-IPM image generation tool

In this paper, we demonstrate the utility of the Night Vision Integrated Performance Model (NV-IPM) image generation tool by using it to create a database of face images with controlled degradations. Available face recognition algorithms can then be used to directly evaluate camera designs using these degraded images. By controlling camera effects such as blur, noise, and sampling, we can analyze algorithm performance and establish a more complete performance standard for face acquisition cameras. The ability to accurately simulate imagery and directly test with algorithms not only improves the system design process but greatly reduces development cost.

Military target task performance after wavefront-guided (WFG) and wavefront-optimized (WFO) photorefractive keratectomy (PRK)

Major decisions regarding life and death are routinely made on the modern battlefield, where visual function of the individual soldier can be of critical importance in the decision-making process. Glasses in the combat environment have considerable disadvantages: degradation of short term visual performance can occur as dust and sweat accumulate on lenses during a mission or patrol; long term visual performance can diminish as lenses become increasingly scratched and pitted; during periods of intense physical trauma, glasses can be knocked off the soldier’s face and lost or broken. Although refractive surgery offers certain benefits on the battlefield when compared to wearing glasses, it is not without potential disadvantages. As a byproduct of refractive surgery, elevated optical aberrations can be induced, causing decreases in contrast sensitivity and increases in the symptoms of glare, halos, and starbursts. Typically, these symptoms occur under low light level conditions, the same conditions under which most military operations are initiated. With the advent of wavefront aberrometry, we are now seeing correction not only of myopia and astigmatism but of other, smaller optical aberrations that can cause the above symptoms. In collaboration with the Warfighter Refractive Eye Surgery Program and Research Center (WRESP-RC) at Fort Belvoir and Walter Reed National Military Medical Center (WRNMMC), the overall objective of this study is to determine the impact of wavefront guided (WFG) versus wavefront-optimized (WFO) photorefractive keratectomy (PRK) on military task visual performance. Psychophysical perception testing was conducted before and after surgery to measure each participant’s performance regarding target detection and identification using thermal imagery. The results are presented here.

Benchmarking image fusion system design parameters

A clear and absolute method for discriminating between image fusion algorithm performances is presented. This method can effectively be used to assist in the design and modeling of image fusion systems. Specifically, it is postulated that quantifying human task performance using image fusion should be benchmarked to whether the fusion algorithm, at a minimum, retained the performance benefit achievable by each independent spectral band being fused. The established benchmark would then clearly represent the threshold that a fusion system should surpass to be considered beneficial to a particular task. A genetic algorithm is employed to characterize the fused system parameters using a Matlab® implementation of NVThermIP as the objective function. By setting the problem up as a mixed-integer constraint optimization problem, one can effectively look backwards through the image acquisition process: optimizing fused system parameters by minimizing the difference between modeled task difficulty measure and the benchmark task difficulty measure. The results of an identification perception experiment are presented, where human observers were asked to identify a standard set of military targets, and used to demonstrate the effectiveness of the benchmarking process.

Benchmarking Image Fusion Algorithm Performance

Registering two images produced by two separate imaging sensors having different detector sizes and fields of view requires one of the images to undergo transformation operations that may cause its overall quality to degrade with regards to visual task performance. This possible change in image quality could add to an already existing difference in measured task performance. Ideally, a fusion algorithm would take as input unaltered outputs from each respective sensor used in the process. Therefore, quantifying how well an image fusion algorithm performs should be base lined to whether the fusion algorithm retained the performance benefit achievable by each independent spectral band being fused. This study investigates an identification perception experiment using a simple and intuitive process for discriminating between image fusion algorithm performances. The results from a classification experiment using information theory based image metrics is presented and compared to perception test results. The results show an effective performance benchmark for image fusion algorithms can be established using human perception test data. Additionally, image metrics have been identified that either agree with or surpass the performance benchmark established.