民族团结进步模范matlab图像处理外文翻译外文文献
附录A 英文原文
Scene recognition for mine rescue robot
localization bad on vision
CUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)
Abstract：A new scene recognition system was prented bad on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system us monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. The landmarks are organized by using HMM to reprent the scene where the robot is, and fuzzy logic strategy is ud to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluation problem of HMM. The contributions of the skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.
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游戏精神Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model跆拳道动作
1 Introduction
内参选编Search and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for tho trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods bad on camera can be mainly classified into geometric, topological or hybrid ones[2]. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization.怎么学做面包>冬至为什么吃饺子
冰窗花Currently most scene recognition methods are bad on global image features and have two distinct stages: training offline and matching online.
