-
Article
Probabilistic-Based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising
The high-dimensional nature of the 4-D light field (LF) poses great challenges in achieving efficient and effective feature embedding, that severely impacts the performance of downstream tasks. To tackle this ...
-
Article
A Comprehensive Study of the Robustness for LiDAR-Based 3D Object Detectors Against Adversarial Attacks
Recent years have witnessed significant advancements in deep learning-based 3D object detection, leading to its widespread adoption in numerous applications. As 3D object detectors become increasingly crucial ...
-
Article
Unsupervised video-based action recognition using two-stream generative adversarial network
Video-based action recognition faces many challenges, such as complex and varied dynamic motion, spatio-temporal similar action factors, and manual labeling of archived videos over large datasets. How to extra...
-
Article
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors during training, thus deteriorat...
-
Article
Guest Editorial: machine learning for visual information processing & understanding
-
Article
Differentiable Deformation Graph-Based Neural Non-rigid Registration
The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface. Among the pipeline, t...
-
Article
Open AccessFunctions and mechanisms of lncRNA MALAT1 in cancer chemotherapy resistance
Chemotherapy is one of the most important treatments for cancer therapy. However, chemotherapy resistance is a big challenge in cancer treatment. Due to chemotherapy resistance, drugs become less effective or ...
-
Article
Open AccessKnowledge-map analysis of percutaneous nephrolithotomy (PNL) for urolithiasis
Percutaneous nephrolithotomy (PNL) has been used in the treatment of urolithiasis for more than 20 years. However, bibliometric analysis of the global use of PNL for urolithiasis is rare. We retrieved the lite...
-
Article
RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds
Deep learning has proven an effective tool for 3D point cloud processing. Currently, most deep set architectures are developed for sparse inputs (typically with a few thousand points), which are unable to prov...
-
Article
Learning hyperspectral images from RGB images via a coarse-to-fine CNN
Hyperspectral remote sensing is well-known for its extraordinary spectral distinguishability to discriminate different materials. However, the cost of hyperspectral image (HSI) acquisition is much higher compa...
-
Chapter and Conference Paper
DRLFNet: A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution
Light field records both spatial and angular information of light rays. By using light field cameras, 3D scenes can be reconstructed easily for further virtual reality applications. Limited by the sensor size,...
-
Chapter and Conference Paper
PUGeo-Net: A Geometry-Centric Network for 3D Point Cloud Upsampling
In this paper, we propose a novel deep neural network based method, called PUGeo-Net, for upsampling 3D point clouds. PUGeo-Net incorporates discrete differential geometry into deep learning elegantly by learn...
-
Chapter and Conference Paper
Deep Spatial-Angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithm...
-
Chapter and Conference Paper
Fast Light Field Reconstruction with Deep Coarse-to-Fine Modeling of Spatial-Angular Clues
Densely-sampled light fields (LFs) are beneficial to many applications such as depth inference and post-capture refocusing. However, it is costly and challenging to capture them. In this paper, we propose a le...
-
Chapter and Conference Paper
Random Forest with Suppressed Leaves for Hough Voting
Random forest based Hough-voting techniques have been widely used in a variety of computer vision problems. As an ensemble learning method, the voting weights of leaf nodes in random forest play critical role ...
-
Article
Motion capture data recovery using skeleton constrained singular value thresholding
Motion capture data could be missing due to imperfections during the acquisition process. Singular value thresholding (SVT) is an effective method to recover missing motion capture data. However, its effective...
-
Chapter and Conference Paper
A New Rate-Quantization Model for H.264/AVC Low-Delay Rate Control
In this paper, we present a new rate-quantization (R-Q) model for H.264/AVC low-delay rate control. Our rate model is a power function of the quantization stepsize, which is derived through theoretical analysi...
-
Chapter and Conference Paper
Two-Dimensional Rate Model for Video Coding
This paper investigates the joint impact of variations of the frame rate and quantization on the coding bit-rate. Through theoretically linking the frame rate and the quantization step to the mean absolute dif...
