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器人場景識別-全文預(yù)覽
【正文】 對比率 和光照變化 敏感 。 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。 基于 攝像頭 的定位 可以主要分為幾何 法、 拓?fù)?法 或混合 法 。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 。 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(biāo)志 。170176。 salient image。 scene recognition。 hidden Markov model 1 Introduction Search and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine 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 ones[2]. With its feasibility and effectiveness, scene recognition bees one of the important technologies 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 global features that represent the scene. Some approaches were used to analyze the dataset 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 features including color, texture and edge density to represent the image. For example, ZHOU et al[4] 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 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 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 intelligibility[6]. But it cannot capture the contribution of individual feature for scene recognition. 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 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.
環(huán)評公示相關(guān)推薦
鄂ICP備17016276號-1