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(chǎng)景識(shí)別-文庫吧
2025-10-14 16:06 本頁面
【正文】 re, the selected features can not represent the scene thoroughly according to the stateofart pattern recognition, which makes recognition not reliable[7]. So in this work a new recognition system is presented, which is more reliable and effective if it is used in a plex 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 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 Markov model is adopted to anize 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 detection Researches on biological vision system indicate that anism (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. Inspired by those, we use centersurround difference method to detect salient regions in multiscale image spaces. The opponencies of color and texture are puted to create the saliency map. Followup, subimage 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. 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 works[12]. 3 Scene recognition and localization Different from other scene recognition systems, our system doesn?t 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 robot locates. Although the map?s geometric layout is ignored by the localization system, it is useful for visualization and debugging[13] 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 anize 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 omniimages. From Experiment on viewpoint changes each image, salient local regions are detected and 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 sal
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