A structural similarity-inspired performance assessment model for multisensor image registration algorithms

Abstract

In order to assess the performance of multisensor image registration algorithms that are used in the multirobot information fusion, we propose a model based on structural similarity whose name is vision registration assessment model. First of all, this article introduces a new image concept named superimposed image for testing subjective and objective assessment methods. Therefore, we assess the superimposed image but not the registered image, which is different from previous image registration assessment methods that usually use reference and sensed images. Then, we calculate eight assessment indicators from different aspects for superimposed images. After that, vision registration assessment model fuses the eight indicators using canonical correlation analysis, which is used for evaluating the quality of an image registration results in different aspects. Finally, three kinds of images which include optical images, infrared images, and SAR images are used to test vision registration assessment model. After evaluating three state-of-the-art image registration methods, experiments indict that the proposed structural similarity-motivated model achieved almost same evaluation results with that of the human object with the consistency rate of 98.3%, which shows that vision registration assessment model is efficient and robust for evaluating multisensor image registration algorithms. Moreover, vision registration assessment model is independent of the emotional factors and outside environment, which is different from the human.

Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration

“…We introduce a structural similarity metric-based loss in our total loss equation for training the deep learningbased registration network. The Structural Similarity Index Measurement (SSIM) is initially introduced for image quality assessment [37] and later utilized in the traditional image registration to some extent [49]. SSIM is a combination of three different kinds of measurements: brightness Note: All the brain images are scaled to the same data range for display purpose with the exception of the Gaussian disc images, bottom left in Fig.…”

Multimodal fusion for robotics

“…In another paper, 9 a hand and club tracking framework based on recognition with a complex descriptor combining histograms of oriented gradients and spatial–temporal vector is proposed to obtain their movement trajectories in golf video. The sixth paper 10 introduces a new image concept named superimposed image for testing subjective and objective assessment methods. The last paper of this part 11 presents a method of probabilistic soft assignment recognition scheme based on Gaussian mixture models to recognize similar actions.…”