matlab圖像處理中英文翻譯文獻(共12頁)

1、精選優(yōu)質(zhì)文檔-傾情為你奉上附錄A 英文原文Scene recognition for mine rescue robot localization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自興), 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 during

2、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 using

3、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 make th

4、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; matchin

5、g 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 whet

6、her 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 technologie

7、s 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 glob

8、al 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 featu

9、res 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 from

10、 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 illumination ch

11、anges. 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 recognit

12、ion. 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 system i

13、s 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 number of

14、 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, hidden

15、 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 ind

16、icate 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. Inspi

17、red 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 landm

18、ark 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,

19、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 localiza

20、tionDifferent 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 where ro

21、bot 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 the

22、 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 form

23、ed 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-D70 c

24、amera 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 alg

25、orithm14. The 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

26、 on environment 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 valu

27、e, which must 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 o

28、riginal image. 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 communi

29、ty that are briefly 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 in

30、formation of the 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

31、features contribution 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 fu

32、zzy logic for 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

33、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 frame-grabb

34、er IVC-4200. The resolution of image is set to be 400×320 and the sample frequency is set to be 10 frames/s. The computer system is composed of 1 GHz processor and 512 M memory, which is carried by the robot. Presently the robot works in indoor environments.Because HMM is adopted to represent a

35、nd recognize the scene, our system has the ability to capture the discrimination about distribution of salient local image regions and distinguish similar scenes effectively. Table 1 shows the recognition result of static environments including 5 laneways and a silo. 10 scenes are selected from each

36、 environment and HMMs are created for each scene. Then 20 scenes are collected when the robot enters each environment subsequently to match the 60 HMMs above.In the table, “truth” means that the scene to be localized matches with the right scene (the evaluation value of HMM is 30% greater than the s

37、econd high evaluation). “Uncertainty” means that the evaluation value of HMM is greater than the second high evaluation under 10%. “Error match” means that the scene to be localized matches with the wrong scene. In the table, the ratio of error match is 0. But it is possible that the scene to be loc

38、alized cant match any scenes and new vertexes are created. Furthermore, the “ratio of truth” about silo is lower because salient cues are fewer in this kind of environment.In the period of automatic exploring, similar scenes can be combined. The process can be summarized as: when localization succee

39、ds, the current landmark sequence is added to the accompanying observation sequence of the matched vertex un-repeatedly according to their orientation (including the angle of the image from which the salient local region and the heading of the robot come). The parameters of HMM are learned again.Com

40、pared with the approaches using appearance features of the whole image (Method 2, M2), our system (M1) uses local salient regions to localize and map, which makes it have more tolerance of scale, viewpoint changes caused by robots movement and higher ratio of recognition and fewer amount of vertices

41、 on the topological map. So, our system has better performance in dynamic environment. These can be seen in Table 2. Laneways 1, 2, 4, 5 are in operation where some miners are working, which puzzle the robot.6 Conclusions1) Salient local image features are extracted to replace the whole image to par

42、ticipate in recognition, which improve the tolerance of changes in scale, 2D rotation and viewpoint of environment image.2) Fuzzy logic is used to recognize the local image, and emphasize the individual features contribution to recognition, which improves the reliability of landmarks.3) HMM is used

43、to capture the structure or relationship of those local images, which converts the scene recognition problem into the evaluation problem of HMM.4) The results from the above experiments demonstrate that the mine rescue robot scene recognition system has higher ratio of recognition and localization.

44、Future work will be focused on using HMM to deal with the uncertainty of localization.附錄B 中文翻譯基于視覺的礦井救援機器人場景識別CUI Yi-an(崔益安), CAI Zi-xing(蔡自興), WANG Lu(王 璐)摘要：基于模糊邏輯和隱馬爾可夫模型（HMM），論文提出了一個新的場景識別系統(tǒng)，可應(yīng)用于緊急情況下礦山救援機器人的定位。該系統(tǒng)使用單眼相機獲取機器人所處位置的全方位的礦井環(huán)境圖像。通過采用中心環(huán)繞差分法，從圖像中提取突出的位置圖像區(qū)域作為自然的位置標志。這些標志通過使用HMM有機組織起來代

45、表機器人坐在場景，模糊邏輯算法用來匹配場景和位置標志。通過這種方式，定位問題，即系統(tǒng)的現(xiàn)場識別問題，可以轉(zhuǎn)化為對HMM的評價問題。這些技術(shù)貢獻使系統(tǒng)具有處理比率變化、二維旋轉(zhuǎn)和視角變化的能力。實驗結(jié)果還證明，該系統(tǒng)在靜態(tài)和動態(tài)礦山環(huán)境中都具有較高的識別和定位的成功率。關(guān)鍵字：機器人定位；場景識別；突出圖像；匹配算法；模糊邏輯；隱馬爾可夫模型1 介紹在機器人領(lǐng)域搜索和救援災(zāi)區(qū)是一個新興而富有挑戰(zhàn)性的課題。礦井救援機器人的開發(fā)是為了在緊急情況下進入礦井為被困人員查找可能的逃生路線，并確定該線路是否安全。定位識別是這個領(lǐng)域的基本問題?；跀z像頭的定位可以主要分為幾何法、拓撲法或混合法。憑借其可行性和

46、有效性，場景識別成為拓撲定位的重要技術(shù)之一。目前，大多數(shù)場景識別方法是基于全局圖像特征，有兩個不同的階段：離線培訓(xùn)和在線匹配。在訓(xùn)練階段，機器人收集其所工作環(huán)境的圖像，并處理這些圖像提取出能表征該場景的全局特征。一些方法直接分析圖像數(shù)據(jù)得到一些基本特征，比如PCA方法。但是，PCA方法是不能區(qū)分特征的類別。另一種方法使用外觀特征包括顏色、紋理和邊緣密度來表示圖像。例如，周等人用多維直方圖來描述全局外觀特征。此方法簡單，但對比率和光照變化敏感。事實上，各種全局圖像特征，所受來自環(huán)境變化的影響。LOWE提出了SIFT方法，該方法利用關(guān)注點尺度和方向所形成的描述的相似性獲得特征。這些特征對于圖像縮放

47、、平移、旋轉(zhuǎn)和局部光照不變是穩(wěn)定的。但SIFT可能產(chǎn)生1 000個或更多的興趣點，這可能使處理器大大減慢。在匹配階段，近鄰算法（NN）因其簡單和可行而被廣泛采用。但是它并不能捕捉到個別特征對場景識別的貢獻。在實驗中，NN在表達兩種部分之間的相似性時效果并不足夠好。此外，所選的特征并不能徹底地按照國家模式識別標準表示場景，這使得識別結(jié)果不可靠。因此，在這些分析中提出了一種新的識別系統(tǒng)，如果使用在復(fù)雜的礦井環(huán)境中它將更加可靠和有效。在這個系統(tǒng)中，我們通過提取突出的圖像局部區(qū)域作為位置標志用以替代整個圖像，改善了信息的穩(wěn)定性，從而處理比率、二維旋轉(zhuǎn)和視角的變化。興趣點數(shù)量有效減少，這使得處理更加容易

48、。模糊識別算法用以識別鄰近位置的位置標志，它可以增強個別特征對場景識別的作用。由于它的部分信息恢復(fù)能力，采用隱馬爾可夫模型組織這些位置標志，它可以捕捉到的結(jié)構(gòu)或標志之間的關(guān)系。因此，場景識別可以轉(zhuǎn)化為對HMM評價問題，這使得識別具有魯棒性。2 局部圖像區(qū)域不變形的檢測生物視覺系統(tǒng)的研究表明，生物體（像果蠅）在觀察周圍環(huán)境時，經(jīng)常因為他們的行為習慣注意場景中確定的特殊區(qū)域或者局部圖像信息。這些區(qū)域可以當作天然的位置標志有效地表示和區(qū)別不同環(huán)境。受這些啟示，我們利用中心環(huán)繞差分法檢測多尺度圖像空間突出的區(qū)域。計算顏色和紋理的相似度用以繪制突出區(qū)域的地圖。隨后，以地圖突出位置為中心的分圖像，被定義為位置標志區(qū)域。位置標志區(qū)域的大小可以根據(jù)該區(qū)域圖像梯度方向的變化自適應(yīng)決定。移動機器人的導(dǎo)航要求當環(huán)境有一定程度變化時自然位置標志能被穩(wěn)定地檢測出來。為了驗證我們方法對位置標志檢測的的可重復(fù)性，我們已經(jīng)在圖像比例、二維旋轉(zhuǎn)和視角等變化時，做了一些實驗。圖1表明當視角變化時因為它的突出效果大門能被檢測出來。關(guān)于比率和旋轉(zhuǎn)更詳細的分析和結(jié)果可以在我們以前的論文中發(fā)現(xiàn)。圖1 關(guān)于視角變化的實驗3 場景識別和定位與其他場景識別系統(tǒng)不同的是，我們的不需要離線培訓(xùn)。換句話說，在前進中，我們不必對場景分類。當機器人徘徊時，在固定時間間隔內(nèi)捕獲的場景用于生成拓撲地圖