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Chapter and Conference Paper
Vision-Based Registration Using 3-D Fiducial for Augmented Reality
One of the key issues in the realization of Augmented Reality is the registration problem. Synthetically, vision-based registration can offer superior solutions. In several existing registration methods, 2-D o...
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Chapter and Conference Paper
An Improved Colored-Marker Based Registration Method for AR Applications
Registration is crucial in an Augmented Reality (AR) system for it determines the performance of alignment between virtual objects and real scene. Colored-makers with known world coordinates are usually put in...
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Chapter and Conference Paper
A Convenient and Fast Method of Endoscope Calibration under Surgical Environment
How to get the calibration parameters of an endoscopic camera online is one of the most important steps in the three-dimensional reconstruction and draws great attention from researchers. One of general approa...
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Chapter and Conference Paper
3D Localization Algorithm for Wireless Sensor Networks Based on DCP and VRT
Localization is an important part of Wireless Sensor Networks technology, and the 3D location technology is more appropriate for real applications. We propose a novel 3D localization algorithm based on DCP (De...
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Chapter and Conference Paper
A Modified Genetic Algorithm for Agricultural By-products Logistics Delivery Route Planning Problem
Agricultural by-products collection and delivery route planning is one of the important issues of delivery scheduling optimization for agricultural regional logistics. Aimed at agricultural by-products logist...
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Chapter and Conference Paper
Smooth Multi-instance Learning for Object Detection
The problem of object localization is one of the key problems in computer vision applications. Recently, multiple-instance learning (MIL) is a kind of machine learning framework which receiving a set of insta...
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Chapter and Conference Paper
TNT: An Effective Method for Finding Correlations Between Two Continuous Variables
Determining whether two continuous variables are relevant, either linearly or non-linearly correlated, is a fundamental problem in data science. To test whether two continuous variables have a linear correlati...
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Chapter and Conference Paper
Feature Selection Based on Graph Structure
Feature selection is an important part of data preprocessing. Selecting effective feature subsets can effectively reduce feature redundancy and reduce irrelevant features, and reduce training costs. Based on t...
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Chapter and Conference Paper
A Relational Instance-Based Clustering Method with Contrastive Learning for Open Relation Extraction
Unsupervised text representations significantly narrow the gap with supervised pretraining, and relation clustering has gradually become an important method of open relational extraction (OpenRE). However, dif...
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Article
Semi-supervised partial label learning algorithm via reliable label propagation
Partial label learning (PLL) is a weakly supervised learning method that is able to predict one label as the correct answer from a given candidate label set. In PLL, when all possible candidate labels are as s...