Eight-Channel Multispectral Image Database for Saliency Prediction

Abstract

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

“…An optical filtered SPI is a common alternative to other SPI techniques owing to the simple imaging principle; in other words, it is used to place an external filter in front of a monochromatic imager and acquire images at each spectral band sequentially. In addition to using a filter wheel [18] or color filter array [19], acousto-optic tunable filters (AOTF) [20] and LCTF [21] are frequently used because they can freely switch different spectral band at high speed (ms/or faster) and contain no mechanical moving components. However, the spatial resolution and the optical throughput of an optical filter SPI system are essentially limited by its filtering performance such as bandwidth and energy transmittance of light, respectively.…”