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Polarization vision: a new sensory approach to image understanding

计算机科学 人工智能 极化(电化学) 计算机视觉 模块化设计 图像传感器 色调 感知 光学 物理 心理学 操作系统 物理化学 神经科学 化学
作者
Lawrence B. Wolff
出处
期刊:Image and Vision Computing [Elsevier BV]
卷期号:15 (2): 81-93 被引量:213
标识
DOI:10.1016/s0262-8856(96)01123-7
摘要

Overviewed are recent results of a new general approach to image understanding and computer vision utilizing the sensing of polarization of light. Whereas human vision is oblivious to components of light polarization, polarization parameters of light are shown to provide an important visual extension to intensity and color significantly expanding the application potential for image understanding. A physical state of polarization can be visualized directly in human terms as a particular hue and saturation, and this paper utilizes such a scheme presenting images of ordinary scenes as never seen before by humans in the domain of polarization vision. Metaphorically, humans are 'color blind' with respect to the perception of polarization, and even though this does not appear to inhibit human visual performance, we show how polarization vision is a sensory augmentation that can significantly enhance both automated image understanding and even possibly improve human visual performance itself under certain conditions. Sensors, calledpolarization cameras, have been developed that automatically sense components of partial linear polarization and computationally process these components to produce polarization images. Prototypes of different polarization camera sensors have been presented in earlier literature. A recent advancement in the design of polarization cameras has made it possible to interface low-cost modular components with almost any existing imaging device converting it into an automatic polarization camera. This compatibility with small portable imaging devices is making polarization imaging for the first time accessible to a number of application areas outside the laboratory, both outdoors and underwater, revealing polarization vision as a vast new visually augmented domain with unique capabilities. This paper presents various results from three on-going field applications: natural object recognition, inspection of ship hulls for damage, and marine biology.

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