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MPL-Boosted Integrable Features Pool for pedestrian detection
2011 18th IEEE International Conference on Image Processing, 2011This paper presents a fast and accurate pedestrian detection method. To find a balance between speed and accuracy, we propose a Multi-Pose Learning Boosted Integrable Features Pool (MPL-Boosted IFP) approach. Our method achieves high recall-rate while taking the speed-advantage of cascade-of-rejectors approach. We build different types of feature sets,
Junqiang Wang, Huadong Ma
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Ask the Image: Supervised Pooling to Preserve Feature Locality
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014In this paper we propose a weighted supervised pooling method for visual recognition systems. We combine a standard Spatial Pyramid Representation which is commonly adopted to encode spatial information, with an appropriate Feature Space Representation favoring semantic information in an appropriate feature space.
FANELLO, SEAN RYAN +4 more
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Using Semantic Features for Enhancing Car Pooling System
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018Nowadays the demand on carpooling system increases due to the need to decrease car crowdedness, saving fuel cost, decrease pollution, etc. Carpooling services depend on combining different passengers in one car who are willing to go to the same place in specific time.
Mena Samy, Abeer Mohamed Elkorany
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Weighted Feature Pooling Network in Template-Based Recognition
2019Many computer vision tasks are template-based learning tasks in which multiple instances of a specific concept (e.g. multiple images of a subject’s face) are available at once to the learning algorithm. The template structure of the input data provides an opportunity for generating a robust and discriminative unified template-level representation that ...
Zekun Li 0007 +3 more
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Motion feature based melt pool monitoring for selective laser melting process
Journal of Materials Processing Technology, 2022Xin Lin +2 more
exaly
Learning to Match Features with Geometry-Aware Pooling
2023Jiaxin Deng, Xu Yang, Suiwu Zheng
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