Visual-Thermal Camera Dataset Release and Multi-Modal Alignment without Calibration Information
This dataset and alignment method address the problem of multi-modal data fusion for researchers investigating common features across visual and thermal image streams, potentially simplifying data preparation for various applications.
This paper introduces a new dataset of visual and thermal camera data and a method to align these multi-modal frames at a pixel level without requiring intrinsic or extrinsic calibration information. The alignment process utilizes the Mattes Mutual Information Metric to register the images, providing both raw and aligned data, along with calibration parameters.
This report accompanies a dataset release on visual and thermal camera data and details a procedure followed to align such multi-modal camera frames in order to provide pixel-level correspondence between the two without using intrinsic or extrinsic calibration information. To achieve this goal we benefit from progress in the domain of multi-modal image alignment and specifically employ the Mattes Mutual Information Metric to guide the registration process. In the released dataset we release both the raw visual and thermal camera data, as well as the aligned frames, alongside calibration parameters with the goal to better facilitate the investigation on common local/global features across such multi-modal image streams.