Qualitative and quantitative comparisons of multispectral night vision colorization techniques

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

The Objective Evaluation Index (OEI) for Evaluation of Night Vision Colorization Techniques

A night vision colorization technique can produce colorized imagery with a naturalistic and stable color appearance by processing multispectral night vision (NV) imagery. The multi‐ spectral images typically include visual-band (e.g., red, green, and blue (RGB), or intensified) imagery and infrared imagery (e.g., near infrared (NIR) and long wave infrared (LWIR)). Although appropriately false-c...

Evaluation of Six Night Vision Enhancement Systems: Qualitative and Quantitative Support for Intelligent Image Processing

OBJECTIVE An evaluation study was conducted to answer the question of which system properties of night vision enhancement systems (NVESs) provide a benefit for drivers without increasing their workload. BACKGROUND Different infrared sensor, image processing, and display technologies can be integrated into an NVES to support nighttime driving. Because each of these components has its specific ...

Improving Night Time Driving Safety Using Vision-Based Classification Techniques

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the ...

An Exploration of Color Fusion with Multispectral Images for Night Vision Enhancement

Multispectral images usually present complimentary information such as visual-band imagery and infrared imagery (near infrared or long wave infrared). There are strong evidences that the fused multispectral imagery (in gray scales) increases the reliability of interpretation (Rogers & Wood, 1990; Essock et al., 2001) and thus good for machine analysis (computer vision); whereas the colorized mu...