RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network

LiDAR point cloud frame interpolation, which synthesizes the intermediate frame between the captured frames, has emerged as an important issue for many applications. Especially for reducing the amounts of point cloud transmission, it is by predicting the intermediate frame based on the reference frames to upsample data to high frame rate ones. However, due to high-dimensional and sparse characteristics of point clouds, it is more difficult to predict the intermediate frame for LiDAR point clouds than videos. In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process. Considering the inherited characteristics of RIs differ from that of color images, we introduce spatially adaptive convolutions to extract range features adaptively, while a high-efficient flow estimation method is presented to generate optical flows. The proposed model then warps the input frames and range features, based on the optical flows to synthesize the interpolated frame. Extensive experiments on the KITTI dataset have clearly demonstrated that our method consistently achieves superior frame interpolation results with better perceptual quality to that of using state-of-the-art video frame interpolation methods. The proposed method could be integrated into any LiDAR point cloud compression systems for inter prediction.Comment: Accepted by the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting 202

Using Machine Learning to Predict the Future Development of Disease

The objective of this research is to develop a longterm risk model for the development of cardiovascular disease (CVD) because of type-2 diabetes (T2D). We use the support vector machine (SVM) and the K-nearest neighbours algorithms on the dataset collected from a longitudinal study called Framingham Heart Study, to develop the prediction models. The dataset was first balanced by the Synthetic Minority Oversampling Technique algorithm. The SVM algorithm was then used to train the model, and after tuning the parameters and training for 1000 times, the average accuracy to correctly predict the prevalence of CVD due to T2D came out as 96.5% and the average recall rate was 89.8%. Similarly, we also applied the KNN algorithm to train the dataset, and the recall rate even reaches 92.9%. The advantages of our model are: 1) it can predict with high accuracy both the risk of development of T2D and CVD simultaneously; 2) it can be used without the expensive and tedious oral glucose tolerance test. The model yielded high-performance results after training on the Framingham Heart Study dataset

Deep inter prediction via reference frame interpolation for blurry video coding

In High Efficiency Video Coding (HEVC), inter prediction is an important module for removing temporal redundancy. The accuracy of inter prediction is much affected by the similarity between the current and reference frames. However, for blurry videos, the performance of inter coding will be degraded by varying motion blur, which is derived from camera shake or the acceleration of objects in the scene. To address this problem, we propose to synthesize additional reference frame via the frame interpolation network. The synthesized reference frame is added into reference picture lists to supply more credible reference candidate, and the searching mechanism for motion candidates is changed accordingly. In addition, to make our interpolation network more robust to various inputs with different compression artifacts, we establish a new blurry video database to train our network. With the well-trained frame interpolation network, compared with the reference software HM-16.9, the proposed method achieves on average 1.55% BD-rate reduction under random access (RA) configuration for blurry videos, and also obtains on average 0.75% BD-rate reduction for common test sequences

An unsupervised optical flow estimation for lidar image sequences

In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR point clouds, are widely applied in various tasks, e.g., 3D semantic segmentation, LiDAR point cloud compression (PCC). Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC. However, the existing optical flow estimation models are likely to be unreliable for LiDAR images. In this work, we first propose a light-weight flow estimation model for LiDAR image sequences. The key novelty of our method lies in two aspects. One is that for the different characteristics (with the spatial-variation feature distribution) of the LiDAR images w.r.t. the normal color images, we introduce the attention mechanism into our model to improve the quality of the estimated flow. The other one is that to tackle the lack of large-scale LiDAR-image annotations, we present an unsupervised method, which directly minimizes the inconsistency between the reference image and the reconstructed image based on the estimated optical flow. Extensive experimental results have shown that our proposed model outperforms other mainstream models on the KITTI dataset, with much fewer parameters

Quality Identification of Sauce-Flavor Liquor Based on the Tyndall Phenomenon

There is an obvious colloid state in sauce-flavor liquor due to its unique brewing process and long storage time, which is an important quality feature of sauce-flavor. Aiming at the problems of time, cost, and the strong professionalism of the traditional quality identification method, we proposed a method to identify the quality of sauce-flavored liquor based on the Tyndall phenomenon. The influence of different wavelengths of light on the light scattering in liquor was explored, and it was concluded that the ultraviolet and blue light bands have a certain efficiency in the identification of liquor. Additionally, we analyzed the relationship between the particle size uniformity of liquor colloids and liquor quality according to the Tyndall phenomenon. We found that higher quality liquor has a brighter and lighter Tyndall path and a smaller light flooding angle due to the uniformity of the particles in it. This method can be used to achieve qualitative identification and is suitable for the identification of sauce-flavored liquor