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All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Object detection in natural backgrounds predicted by discrimination performance and models

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

NASA Center ARC↗
NASA NTRS1994

Image Discrimination Models Predict Object Detection in Natural Backgrounds

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

Ahumada, Albert J., Jr.↗
NASA NTRS1995

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Ahumada, A. J., Jr.↗
NASA NTRS1995

A comparison of Image Quality Models and Metrics Predicting Object Detection

Many models and metrics for image quality predict image discriminability, the visibility of the difference between a pair of images. Some image quality applications, such as the quality of imaging radar displays, are concerned with object detection and recognition. Object detection involves looking for one of a large set of object sub-images in a large set of background images and has been approached from this general point of view. We find that discrimination models and metrics can predict the relative detectability of objects in different images, suggesting that these simpler models may be useful in some object detection and recognition applications. Here we compare three alternative measures of image discrimination, a multiple frequency channel model, a single filter model, and RMS error.

Rohaly, Ann Marie↗
NASA NTRS1988

System for detecting objects that represent a threat

A description is given of a panoramic receiver. It is used as a sensor containing several detectors that respond to various types of signal and sound. The system described can be used for detecting objects that represent a threat, by using a sensor and a system for discriminating characteristic signals.

Riedl, Guenther↗
NASA NTRS1991

Young stellar objects detected by IRAS

Fluxes of 453 IRAS point sources selected for proximity to dense gas have been accurately determined by co-adding IRAS data. From this set, 258 sources are detected in all four bands: 12, 25, 60, and 100 microns. As distance from dense molecular material increases, these sources exhibit systematic trends in color and a markedly decreasing luminosity. These trends are broadly consistent with expected evolutionary changes in these stars, with younger objects found closer to dense gas. These data suggest a subtle variation on the criterion for identifying the youngest objects detectable by IRAS, in addition to that of steeply rising spectra: a color temperature which is uniform across all four IRAS bands in the range 40-50 K. These characteristics are used as a criterion to select point sources as candidate very young pre-main-sequence stars.

Clark, Frank O.↗
NASA NTRS1965

Distant objects detected visually with optical filters

Fluorescent coating aids visual daylight detection and identification of distant objects. An object appears as a blinking light when the area is alternately scanned with transmitting and obscuring filters. This method can be effective in search and rescue operations.

Source record↗
NASA NTRS1992

The Spaceguard Survey: Report of the NASA International Near-Earth-Object Detection Workshop

Impacts by Earth-approaching asteroids and comets pose a significant hazard to life and property. Although the annual probability of the Earth being struck by a large asteroid or comet is extremely small, the consequences of such a collision are so catastrophic that it is prudent to assess the nature of the threat and to prepare to deal with it. The first step in any program for the prevention or mitigation of impact catastrophes must involve a comprehensive search for Earth-crossing asteroids and comets and a detailed analysis of their orbits. At the request of the U.S. Congress, NASA has carried out a preliminary study to define a program for dramatically increasing the detection rate of Earth-crossing objects, as documented in this workshop report.

Morrison, David↗
NASA NTRS1988

Objective detection and forecasting of Clear-Air Turbulence (CAT): A status report

Clear-air turbulence has become the largest single cause of weather-related injuries occurring in commercial carriers at cruising altitudes. A technique for objective operational CAT detection (the SCATR index) has been formulated. Its physical basis ties CAT to total energy dissipation as a response to meso- and synoptic-scale dynamical processes associated with upper-level jet stream/frontal zones. Early case studies using properly analyzed routine RAOB rawinsonde sounding data have shown promise.

Keller, John L.↗
NASA NTRS1974

Boundary and object detection in real world images

A solution to the problem of automatic location of objects in digital pictures by computer is presented. A self-scaling local edge detector which can be applied in parallel on a picture is described. Clustering algorithms and boundary following algorithms which are sequential in nature process the edge data to locate images of objects.

Yakimovsky, Y.↗
NASA NTRS1982

Far-infrared photometry of compact extragalactic objects - Detection of 3C 345

The first detection of a quasar between 10 and 1000 microns is reported. The observation permits (1) the determination of the intersection of the optical/infrared and millimeter continua; (2) more precise determination of the total luminosity; (3) the placing of limits on the contribution of any thermal dust emission to the total luminosity. The quasar is the first object ever to have been observed whose energy distribution peaks at wavelength of about 100 microns without a large contribution to the total luminosity from thermal dust emission. The observed flux density of 2.2 + or - 0.5 Jy at 100 microns and an upper limit of 0.5 + or - 0.6 Jy at 50 microns clearly define the overall energy distribution and show the quasar to be a powerful far-infrared source.

Harvey, P. M.↗
NASA NTRS2000

Image Discrimination Models for Object Detection in Natural Backgrounds

This paper reviews work accomplished and in progress at NASA Ames relating to visual target detection. The focus is on image discrimination models, starting with Watson's pioneering development of a simple spatial model and progressing through this model's descendents and extensions. The application of image discrimination models to target detection will be described and results reviewed for Rohaly's vehicle target data and the Search 2 data. The paper concludes with a description of work we have done to model the process by which observers learn target templates and methods for elucidating those templates.

Ahumada, A. J., Jr.↗
NASA NTRS2005

Multi-Sensor Fusion and Enhancement for Object Detection

This was a quick &week effort to investigate the ability to detect changes along the flight path of an unmanned airborne vehicle (UAV) over time. Video was acquired by the UAV during several passes over the same terrain. Concurrently, GPS data and UAV attitude data were also acquired. The purpose of the research was to use information from all of these sources to detect if any change had occurred in the terrain encompassed by the flight path.

Rahman, Zia-Ur↗
NASA NTRS2023

Object Detection and Pose Estimation Public Competition Sample Data

This data was created with open-source tooling for the purpose of running a public competition. This dataset is a sample of what can be generated for the commissioned competition runner to be able to set up their testing infrastructure. It contains images of publicly available spacecraft models in a Blender scene composed of a light source, a background image, and the spacecraft model with bounding box labels.

James Berck↗