A review of indirect time-of-flight technologies
Indirect time-of-flight (iToF) cameras operate by illuminating a scene with modulated light
and inferring depth at each pixel by combining the back-reflected light with different gating …
and inferring depth at each pixel by combining the back-reflected light with different gating …
ROSEFusion: random optimization for online dense reconstruction under fast camera motion
Online reconstruction based on RGB-D sequences has thus far been restrained to relatively
slow camera motions (< 1m/s). Under very fast camera motion (eg, 3m/s), the reconstruction …
slow camera motions (< 1m/s). Under very fast camera motion (eg, 3m/s), the reconstruction …
Recent advances in 3D data acquisition and processing by time-of-flight camera
Y He, S Chen - IEEE Access, 2019 - ieeexplore.ieee.org
Three-dimensional (3D) data acquisition and real-time processing is a critical issue in an
artificial vision system. The develo** time-of-flight (TOF) camera as a real-time vision …
artificial vision system. The develo** time-of-flight (TOF) camera as a real-time vision …
A spectral and spatial approach of coarse-to-fine blurred image region detection
Blur exists in many digital images, it can be mainly categorized into two classes: defocus
blur which is caused by optical imaging systems and motion blur which is caused by the …
blur which is caused by optical imaging systems and motion blur which is caused by the …
What can we learn from depth camera sensor noise?
Although camera and sensor noise are often disregarded, assumed negligible or dealt with
in the context of denoising, in this paper we show that significant information can actually be …
in the context of denoising, in this paper we show that significant information can actually be …
Tackling 3d tof artifacts through learning and the flat dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth
reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning …
reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning …
A local metric for defocus blur detection based on CNN feature learning
K Zeng, Y Wang, J Mao, J Liu, W Peng… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Defocus blur detection is an important and challenging task in computer vision and digital
imaging fields. Previous work on defocus blur detection has put a lot of effort into designing …
imaging fields. Previous work on defocus blur detection has put a lot of effort into designing …
Using transfer learning for classification of gait pathologies
TT Verlekar, PL Correia… - 2018 IEEE international …, 2018 - ieeexplore.ieee.org
Different diseases can affect an individual's gait in different ways and, therefore, gait
analysis can provide important insights into an individual's health and well-being. Currently …
analysis can provide important insights into an individual's health and well-being. Currently …
iToF-flow-based High Frame Rate Depth Imaging
Abstract iToF is a prevalent cost-effective technology for 3D perception. While its reliance on
multi-measurement commonly leads to reduced performance in dynamic environments …
multi-measurement commonly leads to reduced performance in dynamic environments …
Large-scale benchmark for uncooled infrared image deblurring
Infrared images are increasingly adopted in various applications. Therefore, motion
deblurring for infrared images is also receiving growing interest. However, deep-learning …
deblurring for infrared images is also receiving growing interest. However, deep-learning …