Real-Time Full Color Multiband Night Vision

Toet, Alexander; Hogervorst,

doi:10.5772/10136

Cited by 5 publications

(4 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the statistical approach can even be used with imagery from sources like artificial terrain databases or Google Earth [80] (for demonstrations see [82] and [83]). The specificity of the lookup-table color remapping has the additional advantage that it enables to selectively enhance and emphasize details of interest (e.g., camouflaged targets) in a given scene [79, 87, 88]. …”

Section: Methodsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

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

View full text Add to dashboard Cite

show abstract

“…In the colour image fusion methodology the individual image inputs are first enhanced by filtering them with a feedforward center-surrounding shunting neural network. The operation serves to [57]:…”

Section: Vision and Thermal Fusionmentioning

confidence: 99%

Multi-Sensor Data Fusion Techniques for RPAS Detect, Track and Avoid

Cappello¹,

Sabatini²,

Ramasamy³

2015

SAE Technical Paper Series

View full text Add to dashboard Cite

Accurate and robust tracking of intruders and objects is of growing interest amongst the Remotely Piloted Aircraft System (RPAS) scientific community. The ability of Multi-Sensor Data Fusion (MSDF) algorithms to support effective Detect, Track and Avoid (DTA) mission-and safety-critical tasks, and to aid in accurately predicting future states of intruders is explored in detail. RPAS are currently not equipped to routinely access all classes of airspace primarily due to the incipient development of a certifiable on-board Detect-and-Avoid (DAA) system. By employing a unified approach to cooperative and non-cooperative DAA, as well as providing enhanced Communication, Navigation and Surveillance (CNS) services, a cohesive logical framework for the development of an airworthy DAA capability can be achieved. In order to perform effective detection of intruders, a number of high performance, reliable and accurate avionics sensors and/or systems are adopted including non-cooperative sensors (visual and thermal cameras, LIDAR, acoustic sensors, etc.) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B), Traffic Collision Avoidance System (TCAS), etc.). In this paper MSDF techniques required for sensor and system information fusion are critically analysed and their algorithms are presented. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based on maneuvering and non-maneuvering tasks. Furthermore, an artificial neural network is conceptualised to exploit the use of statistical learning methods, which act on combined information obtained from UKF and PF.

show abstract

“…[6][7][8][9][10] The underlying assumption is that mapping the different spectral bands to a given color space can increase the dynamic range of a sensor system, 11 and can enhance the feature contrast and reduce the visual clutter, resulting in better human visual scene recognition, object detection, and depth perception. 19 [12][13][14][15][16][17] However, most color mappings do not achieve color constancy or produce color images with an unnatural appearance, thereby seriously degrading observer performance.…”

Section: Color Night Visionmentioning

confidence: 99%

“…1 When there is only an indexed daylight color image available representing an environment similar to the one in which the multispectral sensor suite will be deployed, a mapping can still be established by transferring the color statistics of the daylight image to the multispectral image. 1,19,53 For the sake of completeness, we will briefly describe our color transformation here, using the example shown in Fig. Note that the statistical approach can even be used with imagery from sources like artificial terrain databases or Google Earth 52 (for demonstrations see http://www.scivee.tv/node/29094 and http://www.scivee.tv/ node/29095).…”

Section: Color Remappingmentioning

confidence: 99%

Perceptual evaluation of color transformed multispectral imagery

et al. 2014

Self Cite

View full text Add to dashboard Cite

Color remapping can give multispectral imagery a realistic appearance. We assessed the practical value of this technique in two observer experiments using monochrome intensified (II) and long-wave infrared (IR) imagery, and color daylight (REF) and fused multispectral (CF) imagery. First, we investigated the amount of detail observers perceive in a short timespan. REF and CF imagery yielded the highest precision and recall measures, while II and IR imagery yielded significantly lower values. This suggests that observers have more difficulty in extracting information from monochrome than from color imagery. Next, we measured eye fixations during free image exploration. Although the overall fixation behavior was similar across image modalities, the order in which certain details were fixated varied. Persons and vehicles were typically fixated first in REF, CF, and IR imagery, while they were fixated later in II imagery. In some cases, color remapping II imagery and fusion with IR imagery restored the fixation order of these image details. We conclude that color remapping can yield enhanced scene perception compared to conventional monochrome nighttime imagery, and may be deployed to tune multispectral image representations such that the resulting fixation behavior resembles the fixation behavior corresponding to daylight color imagery. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms

show abstract

Real-Time Full Color Multiband Night Vision

Cited by 5 publications

References 82 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

Multi-Sensor Data Fusion Techniques for RPAS Detect, Track and Avoid

Perceptual evaluation of color transformed multispectral imagery

Contact Info

Product

Resources

About