妇女节是几月几日附录A 英文原文
Scene recognition for mine rescue robot
localization bad on vision
CUI Yi—an（崔益安), CAI Zi-xing(紫砂壶鉴别中心蔡自兴）， WANG Lu（王 兔子喂什么璐)
Abstract：月经腰痛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 th
e 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.
Key words： robot location； scene recognition； salient image； matching strategy； fuzzy logic; hidden Markov model
1 Introduction
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。
Currently most scene recognition methods are bad on global image features and have two distinct stages: training offline and matching online.
During the training stage， robot collects the images of the environment where it works and process the images to extract global features that reprent the scene. Some approaches were ud to analyze the data-t of image directly and some primary features were found, such as the PCA method [3]。 However, the PCA method is not effective in distinguishing the class of features。 Another type of approach us appearance features including color， texture and edge density to reprent the image. For example， ZHOU et al［4］ ud multidimensional histograms to describe global appearance features. This method is simple but nsitive to scale and illumination changes. In fact, all kinds of global image features are suffered from the change of environment。
LOWE ［5] prented a SIFT method that us similarity invariant descriptors formed by characteristic scale and orientation at interest points to obtain the features。 The features
are invariant to image scaling, translation, rotation and partially invariant to illumination changes. But SIFT may generate 1 000 or more interest points, which may slow down the processor dramatically。
对老师的赞美
During the matching stage, nearest neighbor strategy（NN） is widely adopted for its facility and intelligibility[6]。 But it cannot capture the contribution of individual feature for scene recognition. In experiments， the NN is not good enough to express the similarity between two patterns。 Furthermore, the lected features can not reprent the scene thoroughly according to the state-of—art pattern recognition， which makes recognition not reliable［7].
So in this work a new recognition system is prented, which is more reliable and effective if it is ud in a complex mine environment. In this system, we improve the invariance by extracting salient local image regions as landmarks to replace the whole image to deal with large changes in scale， 2D rotation and viewpoint。 And the number of interest points is reduced effectively, which makes the processing easier。 Fuzzy reco
gnition strategy is designed to recognize the landmarks in place of NN， which can strengthen the contribution of individual feature for scene recognition。 Becau of its partial information resuming ability， hidden Markov model is adopted to organize tho landmarks， which can capture the structure or relationship among them. So scene recognition can be transformed to the evaluation problem of HMM， which makes recognition robust。
2 Salient local image regions detection
Rearches on biological vision system indicate that organism （like drosophila) often pays attention to certain special regions in the scene for their behavioral relevance or local image cues while obrving surroundings [8］. The regions can be taken as natural landmarks to effectively reprent and distinguish different environments. Inspired by tho, we u center—surround difference method to detect salient regions in multi—scale image spaces. The opponencies of color and texture are computed to create the saliency map。
炊烟
Follow-up， sub—image centered at the salient position in S is taken as the landmark region。 The size of the landmark region can be decided adaptively according to the changes of gradient orientation of the local image [11］。
虐字开头的成语
有数字的古诗Mobile robot navigation requires that natural landmarks should be detected stably when environments change to some extent. To validate the repeatability on landmark detection of our approach， we have done some experiments on the cas of scale, 2D rotation and viewpoint changes etc. Fig。1 shows that the door is detected for its saliency when viewpoint changes。 More detailed analysis and results about scale and rotation can be found in our previous works［12］。
