Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning. In particular, we investigate multiple ways of enforcing the epipolar constraint in flow estimation. To alleviate a "chicken-and-egg" type of problem encountered in dynamic scenes where multiple motions may be present, we propose a low-rank constraint as well as a union-of-subspaces constraint for training. Experimental results on various benchmarking datasets show that our method achieves competitive performance compared with supervised methods and outperforms state-of-the-art unsupervised deep-learning methods.Comment: CVPR 201

GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction

This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202

Rotor fault classification technique and precision analysis with kernel principal component analysis and multi-support vector machines

To solve the diagnosis problem of fault classification for aero-engine vibration over standard during test, a fault diagnosis classification approach based on kernel principal component analysis (KPCA) feature extraction and multi-support vector machines (SVM) is proposed, which extracted the feature of testing cell standard fault samples through exhausting the capability of nonlinear feature extraction of KPCA. By computing inner product kernel functions of original feature space, the vibration signal of rotor is transformed from principal low dimensional feature space to high dimensional feature spaces by this nonlinear map. Then, the nonlinear principal components of original low dimensional space are obtained by performing PCA on the high dimensional feature spaces. During muti-SVM training period, as eigenvectors, the nonlinear principal components are separated into training set and test set, and penalty parameter and kernel function parameter are optimized by adopting genetic optimization algorithm. A high classification accuracy of training set and test set is sustained and over-fitting and under-fitting are avoided. Experiment results indicate that this method has good performance in distinguishing different aero-engine fault mode, and is suitable for fault recognition of a high speed rotor

Multi-damage detection in composite structure

In this paper a pre-stack reverse-time migration concept of signal processing techniques is developed and adapted to guided-wave propagation in composite structure for multi-damage imaging by experimental studies. An anisotropic laminated composite plate with a surface-mounted linear piezoelectric ceramic (PZT) disk array is studied as an example. At first, Mindlin Plate Theory is used to model Lamb waves propagating in laminates. The group velocities of flexural waves are also derived from dispersion relations and validated by experiments. Then reconstruct the response wave fields with reflected data collected by the linear PZT array. Reverse-time migration technique is then performed to back-propagate the reflected energy to the damages using a two-dimensional explicit finite difference algorithm and damages are imaged. Stacking these images together gets the final image of multiple damages. The results show that the pre-stack migration method is hopeful for damage detection in composite structures

Spatial Steerability of GANs via Self-Supervision from Discriminator

Generative models make huge progress to the photorealistic image synthesis in recent years. To enable human to steer the image generation process and customize the output, many works explore the interpretable dimensions of the latent space in GANs. Existing methods edit the attributes of the output image such as orientation or color scheme by varying the latent code along certain directions. However, these methods usually require additional human annotations for each pretrained model, and they mostly focus on editing global attributes. In this work, we propose a self-supervised approach to improve the spatial steerability of GANs without searching for steerable directions in the latent space or requiring extra annotations. Specifically, we design randomly sampled Gaussian heatmaps to be encoded into the intermediate layers of generative models as spatial inductive bias. Along with training the GAN model from scratch, these heatmaps are being aligned with the emerging attention of the GAN's discriminator in a self-supervised learning manner. During inference, human users can intuitively interact with the spatial heatmaps to edit the output image, such as varying the scene layout or moving objects in the scene. Extensive experiments show that the proposed method not only enables spatial editing over human faces, animal faces, outdoor scenes, and complicated indoor scenes, but also brings improvement in synthesis quality.Comment: This manuscript is a journal extension of our previous conference work (arXiv:2112.00718), submitted to TPAM

Expression of peroxiredoxins in the human testis, epididymis and spermatozoa and their role in preventing H2O2-induced damage to spermatozoa

Introduction. High levels of reactive oxygen species (ROS) have potential toxic effects on testicular function and sperm quality. Peroxiredoxins (PRDXs) are enzymes with a role as ROS scavenger. The aim of the study was to reveal the presence and localization of PRDXs in human testis, epididymis and spermatozoa, and the protective roles of PRDX2 and PRDX6 in sperm motility. Material and methods. The presence and localization of PRDXs in the human testis, epididymis and spermatozoa were detected by immunohistochemistry, western blot and immunofluorescence. The effect of anti-peroxidative damage to spermatozoa was examined by adding H2O2 to the recombinant protein-treated spermatozoa. Results. There were strong signals of PRDX1 in spermatogonia and round spermatids; PRDX2 in the round spermatids; PRDX4 and 5 in spermatogonia; PRDX6 in Sertoli cells. PRDXs were also found in epididymal epithelial cells where the expression of PRDX1, 4, 5, 6 in the cauda was higher than in the caput of epididymis. PRDX1-6 immunoreactivity was found throughout acrosome, post-acrosomal region, equatorial segment, neck and cytoplasmic droplet, midpiece and principal piece. The H2O2-induced reduction in sperm motility was reversed by recombinant PRDX2 or PRDX6 in a dose-dependent manner. Conclusions. PRDX1-6 in the human testis and epididymis presented cell-specificity. PRDX2 and 6 are potential antioxidant protectors for human spermatozoa