matlab圖像處理外文翻譯外文文獻英文文獻基于視覺的礦井救援機器人場景識別-wenkub
2022-11-28 16:06:45 本頁面
【正文】 s chosen should be invariant to some extent according to the changes of scale, rotation and viewpoint etc. In this paper, we use 4 features monly adopted in the munity 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 it[5]. ASM and ENT: Angular second moment and entropy, which are two texture descriptors. H: Hue, which is used to describe the fundamental information 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 can?t adequately exhibit the individual descriptor or feature?s contribution to similarity measurement. As indicated in , the input image (a) es from different view of (b). But the distance between (a) and (b) puted by Jefferey divergence is larger than (c). To solve the problem, we design a new match algorithm based on fuzzy 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 (b) and (c) are demonstrated by . As indicated, this method can measure the similarity effectively between two patterns. Similarity puted using fuzzy strategy 5 Experiments and analysis The localization system has been implemented on a mobile robot, which is built by our laboratory. The vision system is posed of a CCD camera and a framegrabber IVC4200. The resolution of image is set to be 400320 and the sample frequency is set to be 10 frames/s. The puter system is posed 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 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. Compa
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