Guest Editorial: Spectral Imaging Powered Computer Vision
Jun Zhou, Fengchao Xiong, Lei Tong, Naoto Yokoya, and Pedram Ghamisi
The increasing accessibility and affordability of spectral imaging technology have revolutionized computer vision, allowing for data capture across various wavelengths beyond the visual spectrum. This advancement has greatly enhanced the capabilities of computers and AI systems in observing, understanding, and interacting with the world. Consequently, new datasets in various modalities, such as infrared, ultraviolet, fluorescent, multispectral, and hyperspectral, have been constructed, presenting fresh opportunities for computer vision research and applications.
Although significant progress has been made in processing, learning, and utilizing data obtained through spectral imaging technology, several challenges persist in the field of computer vision. These challenges include the presence of low-quality images, sparse input, high-dimensional data, expensive data labelling processes, and a lack of methods to effectively analyze and utilize data considering their unique properties. Many mid-level and high-level computer vision tasks, such as object segmentation, detection and recognition, image retrieval and classification, and video tracking and understanding, still have not leveraged the advantages offered by spectral information. Additionally, the problem of effectively and efficiently fusing data in different modalities to create robust vision systems remains unresolved. Therefore, there is a pressing need for novel computer vision methods and applications to advance this research area.
This special issue aims to provide a venue for researchers to present innovative computer vision methods driven by the spectral imaging technology.
IET Computer Vision, 17, 7, 723-725, 2023-10-03.