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Individual-Tree DBH Estimation from Airborne LiDAR Data Using MSFS-XGBoost. [PDF]
Sensors (Basel)Li P, Jia Y.
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Development and validation of machine learning-based risk prediction models of coronary artery disease using Porphyromonas gingivalis concentration. [PDF]
BMC Oral HealthZheng X +5 more
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Attentive Feedback Feature Pyramid Network for Shadow Detection
IEEE Signal Processing Letters, 2020Shadow detection is one of the most challenging issues in computer vision. Inspired by the great success of the convolutional neural network (CNN) for the problem of image restoration, learned features have been widely adopted for shadow detection. However, most existing methods still suffer from ambiguities driven by black-colored objects, which are ...
Wonjun Kim, Jinhee Kim
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Simple shadow removal using shadow depth map and illumination-invariant feature
The Journal of Supercomputing, 2021Shadows included in images provide useful information for visual scene analysis, but are also factors that negatively affect digital image analysis. Therefore, shadow detection and removal must be considered essential in the preprocessing of the digital image analysis process.
Ki-Hong Park, Yang Sun Lee 0001
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Shadow Detection by Integrating Multiple Features
18th International Conference on Pattern Recognition (ICPR'06), 2006Cast shadows of moving foreground objects in a scene often result in problems for many applications such as surveillance, object tracking/recognition, video content analysis and intelligent transportation systems. In this paper we presented an algorithm exploiting information of color, shading, texture, neighborhoods and temporal consistency to detect ...
Kuo-Hua Lo, Mau-Tsuen Yang
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Feature Analysis for Shadow Detection
Applied Mechanics and Materials, 2011In minimally invasive surgery (MIS), depth estimation is also one of the most essential challenges. With the urgent demands of improving surgical safety, a method of improving the surgeon’s perception of depth by introducing an “invisible shadow” in the operative field has been proposed.
Xiao Peng Hu, Wan Huang, Guo Min Shi
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Automatic Feature Learning for Robust Shadow Detection
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014We present a practical framework to automatically detect shadows in real world scenes from a single photograph. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple ...
Salman Hameed Khan +3 more
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From Shadow Education to “Shadow Curriculum”: Its Definitions and Features
2019This chapter theorizes ‘shadow curriculum’ as a new definition of curriculum. Highlighting the confusing character of the notion of shadow education, the authors provide conceptual discussions about the term. Then, they discuss its limitations for the field of curriculum studies.
Young Chun Kim, Jung-Hoon Jung
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