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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment - Tapa blanda
Sinopsis
Part I Introduction
1 Overview and Contributions
2 Developments in Mobile Robot Localization Research
3 A Computer Vision System for Visual Perception in Unknown Environments
Part II Unsupervised Learning
4 Theory: Clustering
5 Algorithm I: A Fast Approximate EMST Algorithm for High-Dimensional Image Data
6 Algorithm II: An Efficient K-medoids Clustering Algorithm for Large Scale Image Data
7 Algorithm III: Enhancing Complete Linkage Clustering via Boundary Point Detection
8 Algorithm IV: A New Fast k-Nearest Neighbor-Based Clustering Algorithm
Part III Supervised Learning and Semi-Supervised Learning
9 Theory: K-nearest Neighbor Classifiers
10 Application I: A Fast Image Retrieval Method Based on Quantization Tree
11 Application II: A Fast Incremental Spectral Clustering Algorithm for Image Segmentation
Part IV Reinforcement Learning
12 Theory: Human-Like Localization Inspired by a Hippocampal Memory Mechanism
13 Application I: A Developmental Robotic Paradigm Using Working Memory Learning Mechanism
14 Application II: An Autonomous Vision System Based Sensor-Motor Coordination for Open Space Detection
15 Application III: Visual Percepts Learning for Mobile Robot Localization in An Indoor Environment
16 Application IV: An Automatic Natural Scene Recognition Method for Mobile Robot Localization in An Outdoor Environment
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