A comparison of sensor resolution assessment by human vision versus custom software for Landolt C and triangle resolution targets

Pinkus, Alan R.; Dommett, David W.; Task, H. Lee

doi:10.1117/12.2017643

Cited by 4 publications

(3 citation statements)

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“…Sequences of frames can be used for the development of dynamic image fusion algorithms. TRICLOBS imagery has been used successfully in previous studies to develop new color mapping schemes to give multi-band night vision imagery a realistic color appearance [73, 86], to design new image fusion schemes [63], to assess the added value of color fused image representations for human observation [5], to construct an augmented reality nighttime surveillance system [80], and to evaluate a synthetic observer approach to multisensory resolution assessment [93, 94]. …”

Section: Discussionmentioning

confidence: 99%

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Toet¹,

Hogervorst²,

Pinkus

2016

PLoS ONE

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The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4–0.7μm), near-infrared (NIR, 0.7–1.0μm) and long-wave infrared (LWIR, 8–14μm) motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian) people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation) all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer). The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false) color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic) image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs can be used to define color mappings that give the multi-band imagery a realistic color appearance.

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Section: Discussionmentioning

confidence: 99%

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Toet¹,

Hogervorst²,

Pinkus

2016

PLoS ONE

Self Cite

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“…Most significantly, it can be linked directly to the operation of the human visual system (HVS) by assuming that the HVS acts as an ideal observer degraded by noise (which is a line of reasoning that has been very successful in predicting the results of psychophysics experiments). This is preferable to either classical CV approaches (edge and corner detection, such as in [12], although the exact method used in that series of works is not published) which do retain interpretability but are still a questionable analogy to the behavior of the HVS. Stated more plainly, we believe that the template-correlator-based synthetic observer is likely closer in function and performance to a human observer than other potential approaches.…”

Section: Synthetic Observermentioning

confidence: 99%

Automated image-based range performance measurement using TOD

Short¹

2023

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV

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Image processing (including histogram equalization, local area processing, and edge sharpening) is a key component of practical electro-optical imaging systems. Despite this, the range performance impact of such processing remains difficult to quantify, short of running a full human perception experiment. The primary difficulty is that current analytic range performance models—best exemplified by the Targeting Task Performance (TTP) model—can only account for linear and shift-invariant (LSI) image effects. We present our efforts towards developing a quantitative, image-based range performance metric that does not require LSI assumptions. Our proposed metric is based on a Triangle Orientation Discrimination (TOD) target set and observer task, with automatic scoring accomplished through a simple template correlator. The approach is compatible with both synthetic and real imagery.

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“…Over the past several years we have produced five SPIE papers dealing with a quantitative procedure for determining the resolution of an imaging sensor 1,2,3,4,5 . Although these papers dealt with a variety of ancillary issues as well (such as relating vision-based resolution assessment to semi-automated resolution measurement, relating actual target recognition performance to resolution, exploring triangles as an alternative resolution target, etc.)…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the repeatability of a Landolt-C based automated sensor resolution assessment methodology

Pinkus

Dommett

Task³

2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

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This paper describes a novel sensor resolution assessment chart and procedure based on the Landolt C. This automated resolution assessment procedure does not rely on human vision as the primary means of interpreting resolution chart results. Seven sensors across four different spectral bands and several geometric resolutions were assessed a total of five times each to determine the repeatability (confidence interval) for this automated procedure. The results are presented and compared with previous studies. INTRODUCTIONOver the past several years we have produced five SPIE papers dealing with a quantitative procedure for determining the resolution of an imaging sensor 1,2,3,4,5 . Although these papers dealt with a variety of ancillary issues as well (such as relating vision-based resolution assessment to semi-automated resolution measurement, relating actual target recognition performance to resolution, exploring triangles as an alternative resolution target, etc.) our basic approach for resolution assessment was to obtain sensor images of a resolution assessment target at different distances. These images were then analyzed using a custom software algorithm to see how well the software could correctly determine the orientation of the resolution target (usually a Landolt C) as a function of distance. From these data we could then calculate a "resolution" from the sigmoid curve fit to these data. The resolution target was positioned in all four cardinal orientations and several images were analyzed for each orientation and each distance. Each of the images analyzed was actually an average of eight single exposures that were captured sequentially to reduce the effects of noise.Although the Automatic Landolt C Orientation Recognition (ALCOR) software algorithm automatically produced its best estimate of the orientation of the C, it was necessary to manually designate the center of the C, which required human input to the process. The procedure of having to acquire a relatively large number of sensor images at a multitude of distances for the various orientations of a single C was somewhat time consuming taking several hours to assess a single sensor. The results of these studies were marginally successful, but the time required for the resolution assessment was unacceptable. The primary purpose of the current effort is to develop a much faster method to objectively determine the resolution of a sensor requiring minimal human input.The Landolt C was selected as the resolution target of choice, but rather than probe sensor resolution by adjusting the distance between the target and the sensor, a chart was designed that consisted of several Cs of different sizes. The chart was designed as a 6x6 matrix of cells with a C in the center of each cell, resulting in a total of 36 Cs in the chart (see Figure 1). The Cs were arranged with smaller Cs in the center and larger Cs around the outside. ________________________*alan.pinkus@us.af.mil, phone 1 937-255-8767; Fax 1 937-255-8366; http://www.wpafb.af.mil/afrl/711HPW Signal ...

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A comparison of sensor resolution assessment by human vision versus custom software for Landolt C and triangle resolution targets

Cited by 4 publications

References 0 publications

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Automated image-based range performance measurement using TOD

Evaluation of the repeatability of a Landolt-C based automated sensor resolution assessment methodology

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