基于BOW的工业机器人视觉特征提取技术研究

发布时间：2018-06-07 23:40

本文选题：特征提取 + 特征表达　；参考：《安徽工程大学》2017年硕士论文

【摘要】：随着当今社会数字化和信息化程度的不断提高,视觉信息越来越多以数字图像的形式存在于人们日常的生活及生产中。作为图像处理和机器人视觉的重要组成部分,特征提取技术可以对视觉信息进行高效的处理,获得人们需要的信息,给生活和工业生产带来便利。它在工业机器人视觉技术、卫星遥感技术、信息检索技术及图像处理等方面应用非常广泛。作为工业机器人的眼睛,机器人视觉技术是工业机器人必不可少的,而特征提取技术是工业机器人视觉领域最重要的技术之一。本文以工业机器人对物体的识别和分类为应用背景,结合来自文本处理领域的BOW(Bag of Words,词袋模型)模型,对工业机器人视觉特征提取技术展开研究。以SIFT(Scale Invariant Feature Transformation,尺度不变特征变换)特征提取算法为主要切入点,针对SIFT算法自身存在的缺陷对其做出改进。同时,结合SIFT特征将文本领域中的BOW模型进行重新设计,形成一种新的特征表达模型,使得SIFT特征提取算法能更好应用于工业机器人中。本文的主要研究内容及创新性如下:(1)本课题针对目前工业机器人对于复杂背景环境下物体识别和判断能力不足的问题对特征提取算法展开研究。通过大量的阅读文献和试验,确定以对复杂背景下图像特征提取相对较好的SIFT算法为主要入手点展开研究。(2)针对SIFT特征提取算法对边缘及非线性变化光照处理能力不足的缺点,结合Laplacian边缘算子改善边缘特征提取效果不好的问题;同时,提出一种反正切归一化法代替原有的归一化方法,来改善非线性变化光照条件对特征提取干扰大的问题。(3)经过大量的研究发现SIFT特征在机器人物体识别领域应用较多是在物体的匹配上,因为SIFT特征不能被现有的分类器直接分类。针对这一点,本文利用文本分类领域的BOW模型结合PCA(Principal Component Analysis,主成分分析)技术,提出一种新的特征表达模型。将SIFT提取的视觉特征重新进行表达,再输入分类器进行分类,从而实现对物体的识别和判断。(4)针对现存工业机器人安全防护技术的不足,结合本文研究成果提出一种基于机器人视觉技术的工业机器人安全防护技术的方案,并通过实验验证了该方案的可行性。以上的研究及创新内容最后以实验的方式进行验证。实验结果表明,本文对算法的改进是成功的,设计的特征表达模型具有可靠性,本文的研究成果具有实际应用价值。
[Abstract]:With the development of digitization and informatization, more and more visual information exists in people's daily life and production in the form of digital images. As an important part of image processing and robot vision, feature extraction technology can efficiently process visual information, obtain the information that people need, and bring convenience to life and industrial production. It is widely used in industrial robot vision, satellite remote sensing, information retrieval and image processing. As the eyes of industrial robot, robot vision technology is indispensable to industrial robot, and feature extraction is one of the most important technologies in the field of industrial robot vision. In this paper, based on the recognition and classification of objects by industrial robots, combined with the Bow bag of Wordsmodel (word bag model) model from the field of text processing, the visual feature extraction technology of industrial robots is studied. Based on the sift / scale variant feature transformation (scale invariant feature transformation) algorithm, this paper improves the sift algorithm in view of its defects. At the same time, a new feature representation model is formed by redesigning the Bow model in the text domain with sift features, which makes the sift feature extraction algorithm better applied to industrial robots. The main contents and innovations of this paper are as follows: (1) in this paper, we study the feature extraction algorithm for the problem that the current industrial robot has insufficient ability to recognize and judge objects in complex background. Through reading a lot of literature and experiments, it is determined that the sift algorithm, which is relatively good for image feature extraction in complex background, is the main starting point of the research. (2) aiming at the shortcoming of sift feature extraction algorithm for edge and nonlinear illumination processing, it is pointed out that sift feature extraction algorithm has insufficient ability to deal with edge and nonlinear variation illumination. Combining with Laplacian edge operator to improve the effect of edge feature extraction is a problem, at the same time, a new method is proposed to replace the original normalization method. After a lot of research, it is found that sift feature is widely used in the field of robot object recognition in object matching. Because sift features cannot be directly classified by existing classifiers. In order to solve this problem, a new feature representation model is proposed in this paper, which is based on the Bow model in the field of text classification and PCA-Principal component Analysis (PCA) technology. The visual features extracted by sift are re-expressed, and then input into the classifier to classify the objects, so as to realize the recognition and judgment of objects. (4) aiming at the shortcomings of the existing safety protection technology of industrial robots, Based on the research results of this paper, a scheme of industrial robot safety protection based on robot vision technology is proposed, and the feasibility of the scheme is verified by experiments. The above research and innovation content is verified by experiment. The experimental results show that the improved algorithm is successful and the designed feature representation model is reliable. The research results in this paper have practical application value.
【学位授予单位】：安徽工程大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP242.2

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