In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.
Electro-optical detection systems have been widely utilized in many applications. The pointing accuracy is often seriously affected by static geometric errors. This article analyses the contributions of integrant geometric error sources by means of quaternions, and a parametric model is hence established. As to nonlinear errors, this article further proposes a semi-parametric model that is based on least squares collocation method. Test results demonstrate that both models can improve the pointing accuracy effectively, with latter offering better performance. The estimation variances in azimuth and elevation validation test have been reduced to 0.0014(°) 2 and 0.0009 (°) 2 from 0.0258(°) 2 and 0.0017(°) 2 , respectively.
Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.
Specular and strong reflections are the main problems encountered during part image defect inspection of shiny or highly reflective surfaces. In this letter, we propose an improved illumination method for defect inspection. A diffuse light source is designed based on the physics analysis of light reflection. The distribution of intensity is simulated according to a known model to verify the illumination uniformity of the source. Experiments show that defect expressivity when using the proposed illumination method has a better performance. The optical model is not only suitable for the defect detection of metal balls but also for the defect detection of planes and cylinders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.