80 Result(s)
within
Computer Science
Artificial Intelligence
Computer Communication Networks
Computer Graphics
Image Processing and Computer Vision
**hu Li
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Chapter and Conference Paper
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of ...
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Chapter and Conference Paper
VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results
Single-object tracking, also known as visual tracking, on the drone platform attracts much attention recently with various applications in computer vision, such as filming and surveillance. However, the lack o...
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Chapter and Conference Paper
VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results
Drones equipped with cameras have been fast deployed to a wide range of applications, such as agriculture, aerial photography, fast delivery, and surveillance. As the core steps in those applications, video ob...
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Chapter and Conference Paper
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, partic...
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Chapter and Conference Paper
Correction to: Iris Identification in 3D
The chapter “Iris Identification in 3D” by Fernand Cohen, Sowrirajan Sowmithran, and Chenxi Li (pp. 324–335) was not presented during the Scandinavian Conference on Image Analysis (SCIA) 2019. SCIA is embraced...
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Chapter and Conference Paper
Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views
Human body pose and shape estimation is an important and challenging task in computer vision. This paper presents a novel method for estimating 3D human body pose and shape from several RGB images, using detec...
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Chapter and Conference Paper
Bi-GANs-ST for Perceptual Image Super-Resolution
Image quality measurement is a critical problem for image super-resolution (SR) algorithms. Usually, they are evaluated by some well-known objective metrics, e.g., PSNR and SSIM, but these indices cannot provide ...
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Chapter and Conference Paper
Iris Identification in 3D
In the presence of eyelids and eyelashes movement, pupil dilation, poor lighting, blur due to movement during iris image acquisition, factors that collectively cause distortion in the iris image, 2D image-base...
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Chapter and Conference Paper
CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution
Although the accuracy of super-resolution (SR) methods based on convolutional neural networks (CNN) soars high, the complexity and computation also explode with the increased depth and width of the network. Th...
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Chapter and Conference Paper
Chasing Feet in the Wild: A Proposed Egocentric Motion-Aware Gait Assessment Tool
Despite advances in gait analysis tools, including optical motion capture and wireless electrophysiology, our understanding of human mobility is largely limited to controlled conditions in a clinic and/or labo...
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Chapter and Conference Paper
Hand-Tremor Frequency Estimation in Videos
We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and diagnosing patients suffering ...
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Chapter and Conference Paper
Removal of Visual Disruption Caused by Rain Using Cycle-Consistent Generative Adversarial Networks
This paper addresses the problem of removing rain disruption from images for outdoor vision systems. The Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal ...
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Chapter and Conference Paper
Deep Volumetric Video From Very Sparse Multi-view Performance Capture
We present a deep learning based volumetric approach for performance capture using a passive and highly sparse multi-view capture system. State-of-the-art performance capture systems require either pre-scanned...
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Chapter and Conference Paper
Interaction-Aware Spatio-Temporal Pyramid Attention Networks for Action Classification
Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with i...
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Chapter and Conference Paper
Number of Useful Components in Gaussian Mixture Models for Patch-Based Image Denoising
When using Gaussian mixture models (GMMs) as a prior for image denoising under the Bayesian maximum a posteriori (MAP) perspective, only a single prominent Gaussian component is usually selected to recover a n...
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Chapter and Conference Paper
Visual Scene Reconstruction Using a Bayesian Learning Framework
In this paper, we focus on constructing new flexible and powerful parametric framework for visual data modeling and reconstruction. In particular, we propose a Bayesian density estimation method based upon mix...
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Chapter and Conference Paper
Semantic Segmentation of Indoor-Scene RGB-D Images Based on Iterative Contraction and Merging
In this paper, we propose an iterative contraction and merging framework (ICM) for semantic segmentation in indoor scenes. Given an input image and a raw depth image, we first derive the dense prediction map f...
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Chapter and Conference Paper
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can mislead DNNs to make arbitrary...
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Chapter and Conference Paper
Real-Time Hair Rendering Using Sequential Adversarial Networks
We present an adversarial network for rendering photorealistic hair as an alternative to conventional computer graphics pipelines. Our deep learning approach does not require low-level parameter tuning nor ad-...
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Chapter and Conference Paper
Unsupervised Person Re-identification by Deep Learning Tracklet Association
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment d...
