matlab圖像處理外文翻譯外文文獻(xiàn)英文文獻(xiàn)基于視覺的礦井救援機(jī)器人場景識別-文庫吧在線文庫
【正文】 ndoor environments. Because HMM is adopted to represent and 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 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 second 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 localized can?t 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 bined. The process can be summarized as: when localization succeeds, the current landmark sequence is added to the acpanying observation sequence of the matched vertex unrepeatedly according to their orientation (including the angle of the image from which the salient local region and the heading of the robot e). The parameters of HMM are learned again. Compared 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 robot?s movement and higher ratio of recognition and fewer amount of vertices 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 Conclusions 1) Salient local image features are extracted to replace the whole image to participate 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 feature?s contribution to recognition, which improves the reliability of landmarks. 3) HMM is used 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. 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(jī)器人場景識別 CUI Yian(崔益安 ), CAI Zixing(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(gè)新的場景識別系統(tǒng)，可 應(yīng)用于 緊急情況 下 礦山救援機(jī)器人 的 定位。礦 井 救援機(jī)器人的開發(fā)是為了在緊急情況下進(jìn)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。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。 但是它并不能捕捉到個(gè) 別 特征 對 場景 識別的貢獻(xiàn)。 因此，場景識別可以轉(zhuǎn)化為對 HMM評價(jià)問題 ，這使得 識別具有 魯棒性。為了驗(yàn)證我們方法對位置標(biāo)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(shí)，做了一些實(shí)驗(yàn)。 在論文中 隱馬爾可夫模型是用來組織從 當(dāng)前 的現(xiàn)場提取的 位置標(biāo)志 ， 并且設(shè)置 拓?fù)?圖的頂點(diǎn)用以實(shí)現(xiàn) 部分 信息的 恢復(fù)能力。 取一張 ， 一共得到 8個(gè) 圖像 。 因此， 選擇的 描述或 特征相應(yīng)于比例、旋轉(zhuǎn)和視角的變化應(yīng)該在 一定 范圍內(nèi)保持恒定不變 ， 在本文中， 我們使用 社會上通常采用的四個(gè)特征， 簡單地描述 如下： GO：漸變的方向。 而在數(shù)
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