503 Result(s)
within
English
Image Processing and Computer Vision
Yi**ang Chen
Chapter
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Chapter and Conference Paper
Evolving Epidemic Management Rules Using Deep Neuroevolution: A Novel Approach to Inspection Scheduling and Outbreak Minimization
In epidemic management, the unpredictable dynamics of outbreaks, the constraints of available resources, and the complexity of variable interactions pose a significant challenge in designing effective manageme...
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Chapter and Conference Paper
FFANet: Dual Attention-Based Flow Field Aware Network for 3D Grid Classification and Segmentation
Deep learning-based approaches for three-dimensional (3D) grid understanding and processing tasks have been extensively studied in recent years. Despite the great success in various scenarios, the existing app...
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Chapter and Conference Paper
A CNN-Based Real-Time Dense Stereo SLAM System on Embedded FPGA
Simultaneous localization and map** (SLAM) is the task to estimate agent’s ego-motion in the map and reconstruct the 3D geometric of an unknown environment in parallel. Although many SLAM algorithms have bee...
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Chapter and Conference Paper
A Group Genetic Algorithm for Energy-Efficient Resource Allocation in Container-Based Clouds with Heterogeneous Physical Machines
Containers are quickly gaining popularity in cloud computing environments due to their scalable and lightweight characteristics. However, the problem of Resource Allocation in Container-based clouds (RAC) is m...
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Chapter and Conference Paper
Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior
Magnetic resonance imaging (MRI) is commonly used for studying infant brain development. However, due to the lengthy image acquisition time and limited subject compliance, high-quality infant MRI can be challe...
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Chapter and Conference Paper
Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust
EXplainable machine learning (XML) has recently emerged as a promising approach to address the inherent opacity of machine learning (ML) systems by providing insights into their reasoning processes. This paper...
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Chapter and Conference Paper
An Efficient Scheduling Algorithm for Multi-mode Tasks on Near-Data Processing SSDs
Near-Data Processing (NDP) architectures have been proposed to alleviate the large overhead of data movement between the host and the Computational Storage Device (CSD) by offloading tasks to the CSD. In NDP a...
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Chapter and Conference Paper
Multi-view Neighbor-Enriched Contrastive Learning Framework for Bundle Recommendation
Bundle recommendation aims to recommend a group of items with a similar theme to users. The previous methods devoted to alleviating the data sparsity problem. However, they either modeled the intuitive interac...
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Chapter and Conference Paper
A Study on Domain Adaptation for Audio-Visual Speech Enhancement
This paper presents the DA-AVSE system developed for the ASRU 2023 Audio-Visual Speech Enhancement (AVSE) Challenge. We initially employed three well-established AVSE models: MEASE, MTMEASE, and PLMEASE. These...
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Chapter and Conference Paper
Tech United Eindhoven Middle Size League Winner 2023
The RoboCup Middle Size League (MSL) aims to promote robotics research through robots playing autonomous soccer. In this league, robots play 5 vs 5 on a field of 22 by 14 m. The research in the MSL focuses on ...
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Chapter and Conference Paper
Global-to-Local Feature Mining Network for RGB-Infrared Person Re-Identification
RGB-Infrared person Re-Identification (RGB-IR ReID) is a challenging matching task that retrieves a RGB/infrared pedestrian image from the existing infrared/RGB set captured by non-overlap** visible or infra...
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Chapter and Conference Paper
Joint Time-Domain and Frequency-Domain Progressive Learning for Single-Channel Speech Enhancement and Recognition
Single-channel speech enhancement for automatic speech recognition (ASR) has been extensively researched. Traditional methods usually directly learn clean target, which may introduce speech distortions and lim...
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Chapter and Conference Paper
Fast, Memory-Efficient Spectral Clustering with Cosine Similarity
Spectral clustering is a popular and effective method but known to face two significant challenges: scalability and out-of-sample extension. In this paper, we extend the work of Chen (ICPR 2018) on the speed s...
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Chapter and Conference Paper
Dynamic Path Planning Based on Traffic Flow Prediction and Traffic Light Status
Traffic flow prediction and path planning are crucial components of effective intelligent transportation systems research. The intelligent transportation system can optimize vehicle driving routes by utilizing...
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Chapter and Conference Paper
Optimizing GNN Inference Processing on Very Long Vector Processor
Graph Neural Network (GNN) has shown great success in graph learning. However, within the complexity of the real-world tasks and the big graph datasets, current GNN models become increasingly bigger and more c...
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Chapter and Conference Paper
Genetic Programming with Adaptive Reference Points for Pareto Local Search in Many-Objective Job Shop Scheduling
Genetic Programming (GP) is a well-known technique for generating dispatching rules for scheduling problems. A simple and cost-effective local search technique for many-objective combinatorial optimization pro...
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Chapter and Conference Paper
MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
Develo** and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lac...
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Chapter and Conference Paper
Complex Glyph Enhancement for License Plate Generation
The complex glyphs of license plates usually comes with a long-tail distribution, leading to poor recognition performance of the tail class. Supplementing the training data with generated license plates is an ...
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Chapter and Conference Paper
GB-CosFace: Rethinking Softmax-Based Face Recognition from the Perspective of Open Set Classification
State-of-the-art face recognition methods typically take the multi-classification pipeline and adopt the softmax-based loss for optimization. Although these methods have achieved great success, the softmax-bas...
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Chapter and Conference Paper
LSMD-Net: LiDAR-Stereo Fusion with Mixture Density Network for Depth Sensing
Depth sensing is critical to many computer vision applications but remains challenge to generate accurate dense information with single type sensor. The stereo camera sensor can provide dense depth prediction ...
