IET CV: Spectral Imaging Powered Computer Vision Call for Papers

IET Computer Vision

Special Issue on:
Spectral Imaging Powered Computer Vision



Recent advances in spectral imaging technology make it more convenient
and affordable to capture data within and beyond the visual
spectrum. They enable computers and AI agents to better observe,
understand and interact with the world. Efforts in this area also lead
to the construction of new datasets in different modalities such as
infrared, ultraviolet, fluorescent, multispectral, and hyperspectral,
bringing new opportunities to computer vision research and

Extensive research has been undertaken during the past few years to
process, learn and use data captured by spectral imaging
technology. Nevertheless, many challenges remain unsolved in computer
vision, for example, low-quality image, sparse input, the high
dimensionality of data, high cost of data labelling, and lack of
methods to analyse and use data in the context of their unique
properties. In many mid-level and high-level computer vision tasks,
such as object segmentation, detection and recognition, image
retrieval and classification, video tracking and understanding,
methods that can effectively explore the advantages of spectral
information are yet to be developed. Moreover, effective data fusion
in different modalities to develop a robust vision system is still an
open problem. New computer vision methods and applications are
urgently needed to advance this research area.

The goal of this special issue is to provide a forum for researchers,
developers, and users in the broad artificial intelligence community
to present their novel and original computer vision research powered
by spectral imaging technology. Survey papers addressing relevant
topics are also welcome.

Topics of interest include, but are not limited to:

Spectral imaging process
*  Spectral image/video enhancement and reconstruction
*  Object detection and recognition
*  Image retrieval and classification
*  Motion and tracking
*  Visual Localisation and navigation
*  3D reconstruction
*  Video analysis and understanding
* Representation learning, weakly-supervised learning, and contrastive learning of spectral data
* Domain adaption
* Multimodal learning, registration, and fusion
* Large-scale datasets and benchmarking
* Applications in biometrics, medicine, document processing, autonomous driving, and robotic vision
* New applications of spectral imaging

Submission Deadline: December 31, 2022
Publication Date: August, 2023

* Jun Zhou
* Pedram Ghamisi
* Naoto Yokoya
* Fengchao Xiong
* Lei Tong