38 Result(s)
within
Huan**g Wang
Computer Science
Image Processing and Computer Vision
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Article
BDNet: a method based on forward and backward convolutional networks for action recognition in videos
Human action recognition analyzes the behavior in a scene according to the spatiotemporal features carried in image sequences. Existing works suffers from ineffective spatial–temporal feature learning. For sho...
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Article
Thermal infrared action recognition with two-stream shift Graph Convolutional Network
The extensive deployment of camera-based IoT devices in our society is heightening the vulnerability of citizens’ sensitive information and individual data privacy. In this context, thermal imaging techniques ...
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Article
Disturbance-observer-based adaptive dynamic surface control for nonlinear systems with input dead-zone and delay using neural networks
Disturbance-observer-based adaptive neural control approach is proposed for nonlinear systems. Considering the effect caused by long input delay and dead-zone, a novel auxiliary system has been introduced to d...
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Article
MNSSp3: Medical big data privacy protection platform based on Internet of things
How to transform the growing medical big data into medical knowledge is a global topic. However, medical data contains a large amount of personal privacy information, especially electronic medical records, gen...
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Article
A high-efficiency blind watermarking algorithm for double color image using Walsh Hadamard transform
This paper presents an efficient blind watermarking algorithm for double color images using Walsh Hadamard transform (WHT). In this algorithm, the energy gathering function of WHT and the strong correlation be...
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Chapter and Conference Paper
SP-Net: Slowly Progressing Dynamic Inference Networks
Dynamic inference networks improve computational efficiency by executing a subset of network components, i.e., executing path, conditioned on input sample. Prevalent methods typically assign routers to computa...
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Chapter and Conference Paper
TTS-GAN: A Transformer-Based Time-Series Generative Adversarial Network
Signal measurements appearing in the form of time series are one of the most common types of data used in medical machine learning applications. However, such datasets are often small, making the training of d...
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Chapter and Conference Paper
R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
Recent research explosion on Neural Radiance Field (NeRF) shows the encouraging potential to represent complex scenes with neural networks. One major drawback of NeRF is its prohibitive inference time: Renderi...
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Chapter and Conference Paper
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain
Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the cu...
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Chapter and Conference Paper
Saliency Hierarchy Modeling via Generative Kernels for Salient Object Detection
Salient Object Detection (SOD) is a challenging problem that aims to precisely recognize and segment the salient objects. In ground-truth maps, all pixels belonging to the salient objects are positively annota...
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Chapter and Conference Paper
Design Strategies of Multifunctional Exhibition for Community Regeneration: Two Case Studies in Bei**g
The increasing performance of urban regeneration tends to the public sector and local initiatives for general sustainability concerning projects, which introduces the information dissemination media technique ...
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Chapter and Conference Paper
Mixup Without Hesitation
Mixup linearly interpolates pairs of examples to form new samples, which has been shown to be effective in image classification tasks. However, there are two drawbacks in mixup: one is that more training epoch...
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Chapter and Conference Paper
Low Contrast Chinese Rubbing Image Segmentation Based on Gradient Filtering
For more than 1,500 years, rubbing is one of the most perhaps the oldest of the techniques that have been used in printmaking. Despite the fact that image binarization has been a widely studied in the past dec...
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Chapter and Conference Paper
NAS-SCAM: Neural Architecture Search-Based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification
The segmentation and classification of different types of nuclei plays an important role in discriminating and diagnosing of the initiation, development, invasion, metastasis and therapeutic response of tumors...
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Chapter and Conference Paper
Global and Local Multi-scale Feature Fusion Enhancement for Brain Tumor Segmentation and Pancreas Segmentation
The fully convolutional networks (FCNs) have been widely applied in numerous medical image segmentation tasks. However, tissue regions usually have large variations of shape and scale, so the ability of neural...
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Chapter and Conference Paper
Ultra Fast Structure-Aware Deep Lane Detection
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed. Inspired by human perception, the recognition...
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Chapter and Conference Paper
Infrared Small Target Detection Based on Prior Constraint Network and Efficient Patch-Tensor Model
Infrared small target detection (ISTD) is a key technology in the field of infrared detection and has been widely used in infrared search and tracking systems. In this paper, a novel ISTD approach based on a p...
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Chapter and Conference Paper
Automated Segmentation of Skin Lesion Based on Pyramid Attention Network
Automatic segmentation of skin lesion in dermatoscope images is important for clinic diagnosis and assessment of melanoma. However, due to the large variations of scale, shape and appearance of the lesion area...
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Chapter and Conference Paper
High- and Low-Level Feature Enhancement for Medical Image Segmentation
The fully convolutional networks (FCNs) have achieved state-of-the-art performance in numerous medical image segmentation tasks. Most FCNs typically focus on fusing features in different levels to improve the ...
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Chapter and Conference Paper
DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs
Accurate segmentation of organs-at-risks (OARs) from Computed Tomography (CT) image is a key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. Convolutional Neural ...
