CRAVES: Controlling Robotic Arm with a Vision-based Economic System

Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems.Comment: 10 pages, 6 figure

Hesperidin Protects against Acute Alcoholic Injury through Improving Lipid Metabolism and Cell Damage in Zebrafish Larvae

Alcoholic liver disease (ALD) is a series of abnormalities of liver function, including alcoholic steatosis, steatohepatitis, and cirrhosis. Hesperidin, the major constituent of flavanone in grapefruit, is proved to play a role in antioxidation, anti-inflammation, and reducing multiple organs damage in various animal experiments. However, the underlying mechanism of resistance to alcoholic liver injury is still unclear. Thus, we aimed to investigate the protective effects of hesperidin against ALD and its molecular mechanism in this study. We established an ALD zebrafish larvae model induced by 350 mM ethanol for 32 hours, using wild-type and transgenic line with liver-specific eGFP expression Tg (lfabp10α:eGFP) zebrafish larvae (4 dpf). The results revealed that hesperidin dramatically reduced the hepatic morphological damage and the expressions of alcohol and lipid metabolism related genes, including cyp2y3, cyp3a65, hmgcra, hmgcrb, fasn, and fads2 compared with ALD model. Moreover, the findings demonstrated that hesperidin alleviated hepatic damage as well, which is reflected by the expressions of endoplasmic reticulum stress and DNA damage related genes (chop, gadd45αa, and edem1). In conclusion, this study revealed that hesperidin can inhibit alcoholic damage to liver of zebrafish larvae by reducing endoplasmic reticulum stress and DNA damage, regulating alcohol and lipid metabolism

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data

Prevention of Bone Cement Displacement in Kümmell Disease without Neurological Deficits through Treatment with a Novel Hollow Pedicle Screw Combined with Kyphoplasty

Objective Displacement of bone cement following percutaneous vertebral augmentation for Kümmell disease (KD) presents a significant concern, resulting in increasing back pain and compromising daily activities. Unfortunately, current literature does not yet establish a validated and minimally invasive surgical intervention for this issue. This study aims to investigate the effects of a novel hollow pedicle screw combined with kyphoplasty (HPS‐KP) in preventing bone cement displacement following simply percutaneous kyphoplasty for the management of KD. Methods A total of 22 patients (six males, 16 females, averagely aged 77.18 ± 7.63 years) with KD without neurological deficits treated by HPS‐KP at the hospital between March 2021 and June 2022 were hereby selected, among which, there were three stage I KD cases, 12 stage II KD cases, and seven stage III KD cases according to Li's classification. Bone mineral density (BMD), spinal X‐ray, computed tomography (CT), and magnetic resonance imaging (MRI) were examined before the operation. The operation time, intraoperative blood loss, and postoperative complications were all recorded. The follow‐up focused on visual analog scale (VAS) score, Oswestry dysfunction index (ODI), anterior vertebral height (AVH), middle vertebral height (MVH), posterior vertebral height (PVH), wedge‐shape affected vertebral Cobb angle (WCA), and bisegmental Cobb angle (BCA). One‐way analysis of variance (ANOVA) followed by Bonferroni post‐hoc test was employed for performing multiple comparisons in the present study. Results All patients having received the operation successfully were followed up for more than 8 months (ranging from 8 to 18 months). The operation time, intraoperative blood loss, and BMD (T‐score) were 39.09 ± 5.64 min, 14.09 ± 3.98 ml, and − 3.30 ± 0.90 g/cm3, respectively. Statistically significant differences were observed in the VAS score, ODI, AVH, MVH, and WCA (All p  0.05). During follow‐up, five patients suffered from bone cement leakage, and one presented an adjacent vertebral fracture and no bone cement displacement. Conclusion HPS‐KP could be safe and effective in the treatment of KD without neurological deficits, effectively relieving the symptoms of patients, restoring partial vertebral height, and preventing the occurrence of bone cement displacement

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data

A Novel Image Fusion Algorithm for Visible and PMMW Images based on Clustering and NSCT

Aiming at the fusion of visible and Passive Millimeter Wave (PMMW) images, a novel algorithm based on clustering and NSCT (Nonsubsampled Contourlet Transform) is proposed. It takes advantages of the particular ability of PMMW image in presenting metal target and uses the clustering algorithm for PMMW image to extract the potential target regions. In the process of fusion, NSCT is applied to both input images, and then the decomposition coefficients on different scale are combined using different rules. At last, the fusion image is obtained by taking the inverse NSCT of the fusion coefficients. Some methodologies are used to evaluate the fusion results. Experiments demonstrate the superiority of the proposed algorithm for metal target detection compared to wavelet transform and Laplace transform

RG-GCN: A Random Graph Based on Graph Convolution Network for Point Cloud Semantic Segmentation

Point cloud semantic segmentation, a challenging task in 3D data processing, is popular in many realistic applications. Currently, deep learning methods are gradually being applied to point cloud semantic segmentation. However, as it is difficult to manually label point clouds in 3D scenes, it remains difficult to obtain sufficient training samples for the supervised deep learning network. Although an increasing number of excellent methods have been proposed in recent years, few of these have focused on the problem of semantic segmentation with insufficient samples. To address this problem, this paper proposes a random graph based on graph convolution network, referred to as RG-GCN. The proposed network consists of two key components: (1) a random graph module is proposed to perform data augmentation by changing the topology of the built graphs; and (2) a feature extraction module is proposed to obtain local significant features by aggregating point spatial information and multidimensional features. To validate the performance of the RG-GCN, the indoor dataset S3DIS and outdoor dataset Toronto3D are used to validate the proposed network via a series of experiments. The results show that the proposed network achieves excellent performance for point cloud semantic segmentation of the two different datasets

A Novel Image Fusion Algorithm for Visible and PMMW Images based on Clustering and NSCT

Aiming at the fusion of visible and Passive Millimeter Wave (PMMW) images, a novel algorithm based on clustering and NSCT (Nonsubsampled Contourlet Transform) is proposed. It takes advantages of the particular ability of PMMW image in presenting metal target and uses the clustering algorithm for PMMW image to extract the potential target regions. In the process of fusion, NSCT is applied to both input images, and then the decomposition coefficients on different scale are combined using different rules. At last, the fusion image is obtained by taking the inverse NSCT of the fusion coefficients. Some methodologies are used to evaluate the fusion results. Experiments demonstrate the superiority of the proposed algorithm for metal target detection compared to wavelet transform and Laplace transform