matlab图像处理中英文翻译文献.docx

1、matlab图像处理中英文翻译文献附录A 英文原文Scene recognition for mine rescue robot localization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王 璐)Abstract：A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization dur

2、ing 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 images as natural landmarks. These landmarks are organized by us

3、ing 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 evaluation problem of HMM. The contributions of these skills mak

4、e 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.Key words: robot location; scene recognition; salient image; mat

5、ching strategy; fuzzy logic; hidden Markov model1 IntroductionSearch and rescue in disaster area in the domain of robot is a burgeoning and challenging subject1. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine

6、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 hybrid ones2. With its feasibility and effectiveness, scene recognition becomes one of the important technol

7、ogies 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 training stage, robot collects the images of the environment where it works and processes the images to extract

8、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 method is not effective in distinguishing the classes of features. Another type of approach uses appearance f

9、eatures 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 simple but sensitive to scale and illumination changes. In fact, all kinds of global image features are suffered

10、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 features. The features are invariant to image scaling, translation, rotation and partially invariant to illuminatio

11、n 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 its facility and intelligibility6. But it cannot capture the contribution of individual feature for scene reco

12、gnition. 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 to the state-of-art pattern recognition, which makes recognition not reliable7.So in this work a new recognition syst

13、em 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 as landmarks to replace the whole image to deal with large changes in scale, 2D rotation and viewpoint. And the numbe

14、r 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 the contribution of individual feature for scene recognition. Because of its partial information resuming ability, hi

15、dden 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 of HMM, which makes recognition robust.2 Salient local image regions detectionResearches on biological vision system

16、 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 surroundings 8. These regions can be taken as natural landmarks to effectively represent and distinguish different environments. I

17、nspired 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 the saliency map.Follow-up, sub-image centered at the salient position in S is taken as the landmark region. The size of the l

18、andmark 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 approa

19、ch, 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 its saliency when viewpoint changes. More detailed analysis and results about scale and rotation can be found in our previous works12.3 Scene recognition and loca

20、lizationDifferent from other scene recognition systems, our system doesnt need training offline. In other words, our scenes are not classified in advance. When robot wanders, scenes captured at intervals of fixed time are used to build the vertex of a topological map, which represents the place wher

21、e robot locates. Although the maps geometric layout is ignored by the localization system, it is useful for visualization and debugging13 and beneficial to path planning. So localization means searching the best match of current scene on the map. In this paper hidden Markov model is used to organize

22、 the extracted landmarks from current scene and create the vertex of topological map for its partial information resuming ability.Resembled by panoramic vision system, robot looks around to get omni-images. From Fig.1 Experiment on viewpoint changeseach image, salient local regions are detected and

23、formed to be a sequence, named as landmark sequence whose order is the same as the image sequence. Then a hidden Markov model is created based on the landmark sequence involving k salient local image regions, which is taken as the description of the place where the robot locates. In our system EVI-D

24、70 camera has a view field of 170. Considering the overlap effect, we sample environment every 45 to get 8 images. Let the 8 images as hidden state Si (1i8), the created HMM can be illustrated by Fig.2. The parameters of HMM, aij and bjk, are achieved by learning, using Baulm-Welch algorithm14. The

25、threshold of convergence is set as 0.001.As for the edge of topological map, we assign it with distance information between two vertices. The distances can be computed according to odometry readings.Fig.2 HMM of environmentTo locate itself on the topological map, robot must run its eye on environmen

26、t and extract a landmark sequence L1 Lk , then search the map for the best matched vertex (scene). Different from traditional probabilistic localization15, in our system localization problem can be converted to the evaluation problem of HMM. The vertex with the greatest evaluation value, which must

27、also be greater than a threshold, is taken as the best matched vertex, which indicates the most possible place where the robot is.4 Match strategy based on fuzzy logicOne of the key issues in image match problem is to choose the most effective features or descriptors to represent the original image.

28、 Due to robot movement, those extracted landmark regions will change at pixel level. So, the descriptors or features chosen should be invariant to some extent according to the changes of scale, rotation and viewpoint etc. In this paper, we use 4 features commonly adopted in the community that are br

29、iefly described as follows.GO: Gradient orientation. It has been proved that illumination and rotation changes are likely to have less influence on it5.ASM and ENT: Angular second moment and entropy, which are two texture descriptors.H: Hue, which is used to describe the fundamental information of t

30、he image.Another key issue in match problem is to choose a good match strategy or algorithm. Usually nearest neighbor strategy (NN) is used to measure the similarity between two patterns. But we have found in the experiments that NN cant adequately exhibit the individual descriptor or features contr

31、ibution to similarity measurement. As indicated in Fig.4, the input image Fig.4(a) comes from different view of Fig.4(b). But the distance between Figs.4(a) and (b) computed by Jefferey divergence is larger than Fig.4(c).To solve the problem, we design a new match algorithm based on fuzzy logic for

32、exhibiting the subtle changes of each features. The algorithm is described as below.And the landmark in the database whose fused similarity degree is higher than any others is taken as the best match. The match results of Figs.2(b) and (c) are demonstrated by Fig.3. As indicated, this method can measure the similarity effectively between two patterns.Fig.3 Similarity computed using fuzzy strategy5 Experiments and analysisThe localization system has been implemented on a mobile robot, which is built by our laboratory. The vision system is composed of a CCD camera and a