Results 11 to 20 of about 5,151,122 (293)
Strong Edge Features for Image Coding [PDF]
, 1996 Peer ...
Casas Pla, Josep Ramon +1 more
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Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics [PDF]
, 2023 Existing Cloud systems involve large volumes of data streams being sent to a centralised data centre for monitoring, storage and analytics. However, migrating all the data to the cloud is often not feasible due to cost, privacy, and performance concerns.
A Anjum (8870921) +5 more
core +8 more sources
On Classification Thresholds for Graph Attention with Edge Features
CoRR, 2022 The recent years we have seen the rise of graph neural networks for prediction tasks on graphs. One of the dominant architectures is graph attention due to its ability to make predictions using weighted edge features and not only node features. In this paper we analyze, theoretically and empirically, graph attention networks and their ability of ...
Kimon Fountoulakis +5 more
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Joint tracking of features and edges [PDF]
2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008 Sparse features have traditionally been tracked from frame to frame independently of one another. We propose a framework in which features are tracked jointly. Combining ideas from Lucas-Kanade and Horn-Schunck, the estimated motion of a feature is influenced by the estimated motion of neighboring features.
Stanley T. Birchfield +1 more
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Edge-Featured Graph Attention Network
CoRR, 2021 Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. However, most of these models concentrate on only node features during the learning process. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models.
Jun Chen, Haopeng Chen
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Richer Convolutional Features for Edge Detection [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 In this paper, we propose an accurate edge detector using richer convolutional features (RCF). Since objects in nature images have various scales and aspect ratios, the automatically learned rich hierarchical representations by CNNs are very critical and effective to detect edges and object boundaries.
Yun Liu +6 more
openaire +5 more sources
Semantic Segmentation Algorithm Combined with Edge Detection [PDF]
Jisuanji gongcheng, 2021 To address the fuzzy and inaccurate target edges in semantic segmentation,a semantic segmentation network combined with edge detection is proposed.The network consists of an edge detection network and a semantic segmentation network.The edge detection ...
WANG Nan, HOU Zhiqiang, ZHAO Mengqi, YU Wangsheng, MA Sugang
doaj +1 more source
Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs
Mathematics, 2023 Edge embedding is a technique for constructing low-dimensional feature vectors of edges in heterogeneous graphs, which are also called heterogeneous information networks (HINs).
Canwei Liu +5 more
doaj +1 more source
Shape recognition with edge-based features [PDF]
Procedings of the British Machine Vision Conference 2003, 2003 In this paper we describe an approach to recognizing poorly textured objects, that may contain holes and tubular parts, in cluttered scenes under arbitrary viewing conditions. To this end we develop a number of novel components. First, we introduce a new edge-based local feature detector that is invariant to similarity transformations. The features are
Mikolajczyk, Krystian +2 more
openaire +2 more sources
A Novel Real-Time Edge-Guided LiDAR Semantic Segmentation Network for Unstructured Environments
Remote Sensing, 2023 LiDAR-based semantic segmentation, particularly for unstructured environments, plays a crucial role in environment perception and driving decisions for unmanned ground vehicles.
Xiaoqing Yin +4 more
doaj +1 more source