外文文献及翻译：基于视觉的矿井救援机器人场景识别

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1、PDF外文： http:/ 3100 字  出处： Transactions of Nonferrous Metals Society of China, 2008, 18(2): 432-437 附录   英文原文  Scene recognition for mine rescue robot  localization based on vision Abstract： A new scene recognition system was presented based on fuzzy logic and hidden Markov model(

2、HMM) that can be applied in mine rescue robot localization during emergencies. The system uses 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 im

3、ages as natural landmarks. These landmarks are organized by using HMM to represent the scene where the robot is, and fuzzy logic strategy is used 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 ev

4、aluation problem of HMM. The contributions of these 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. &

5、nbsp;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 subject1. Mine rescue robot was developed

6、to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hy

7、brid ones2. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization. Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online. During the trai

8、ning stage, robot collects the images of the environment where it works and processes the images to extract global features that represent the scene. Some approaches were used to analyze the data-set of image directly and some primary features were found, such as the PCA method 3. However, the PCA m

9、ethod is not effective in distinguishing the classes of features. Another type of approach uses appearance features including color, texture and edge density to represent the image. For example, ZHOU et al4 used multidimensional histograms to describe global appearance features. This method is simpl

10、e but sensitive to scale and illumination changes. In fact, all kinds of global image features are suffered from the change of environment. LOWE 5 presented a SIFT method that uses similarity invariant descriptors formed by characteristic scale and orientation at interest points to obtain the featur

11、es. 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. During the matching stage, nearest neighbor strategy(NN) is widely adopted for

12、its facility and intelligibility6. 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 selected features can not represent the scene thoroughly according t

13、o the state-of-art pattern recognition, which makes recognition not reliable7. So in this work a new recognition system is presented, which is more reliable and effective if it is used in a complex mine environment. In this system, we improve the invariance by extracting salient local image regions

14、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 recognition strategy is designed to recognize the landmarks in place of NN, which can strengthen

15、the contribution of individual feature for scene recognition. Because of its partial information resuming ability, hidden Markov model is adopted to organize those landmarks, which can capture the structure or relationship among them. So scene recognition can be transformed to the evaluation problem

16、 of HMM, which makes recognition robust.  2 Salient local image regions detection Researches 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 observing surro

17、undings 8. These regions can be taken as natural landmarks to effectively represent and distinguish different environments. Inspired by those, we use center-surround difference method to detect salient regions in multi-scale image spaces. The opponencies of color and texture are computed to create t

18、he saliency map. 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. 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 cases of scale, 2D rotation and viewpoint changes etc. Fig.1 shows that the door is detected for