【作者】 袁海东；

【导师】 马华东；

【作者基本信息】 北京邮电大学 ， 计算机科学与技术， 2008， 博士

【摘要】 本论文主要从边缘检测、直线检测和文本区域检测三个方面对低层视觉特征检测技术进行研究。论文提出了梯度和相位信息相结合的特征检测模型,可靠地实现边缘特征检测;为提取边缘特征中蕴含的直线特征和文本位置信息,设计了自适应直线特征检测算法,并研究了视频文本区域检测算法。本文的主要贡献如下:(1)提出了梯度和相位信息相结合的边缘特征检测模型—GP模型。GP模型实现了梯度信息和相位信息的有机结合,弥补了梯度模型依赖于图像对比度的不足,解决了相位一致性模型存在的“特征断裂”问题,同时解决了彩色图像的相位一致性检测问题。通过理论分析和大量实验,验证了新模型可以可靠地进行低层特征检测,能够服务于不同的视觉信息处理应用。(2)提出基于Hough变换的自适应直线特征检测算法。根据图像空间分辨率的特点,确定了基于直线方向选择Hough变换参数空间分辨率的原则。算法依据Hough变换对直线方向的初始估计,得到粗略的参数空间分辨率,然后在局部参数空间范围内以迭代的方式进行多分辨率Hough变换,对直线参数逐步求精,并在迭代的过程中由更精确的参数实现对参数空间分辨率的修正,这种反馈策略使算法能够达到对直线特征的精确检测和快速收敛。进一步提出了更加完善的直线端点检测算法,通过设计合理的离散点排序与连接策略,算法在不同离散程度的点集中都能够精确地检测到完整的直线特征。(3)提出基于梯度和相位信息的视频文本区域检测方法。利用差分图像像素行(列)的粗糙度特征和自适应阈值,实现对文本区域的快速检测。为进一步实现对文本区域的精确检测与定位,基于GP模型在画面局部区域检测文本关键特征点,因此能够有效抑制图像复杂背景的干扰,同时显著提高了算法的效率。此方法对不同的视频画面质量、不同复杂程度的背景以及不同外观的文本是鲁棒的。本文提出的模型与算法,已应用于邮票数字博物馆的数字资源处理,以及视频文本区域检测、视频质量分析,取得了较好的应用效果。更多还原

【Abstract】 This thesis mainly focuses the research on the following three aspects: edge detection, straight line detection and video text detection, which are belong to the low-level visual feature detection. We proposed a novel feature detection model based on gradient and phase to implement edge feature detection reliably. To extract straight line and video text location information from the edge feature, we design and implement adaptive straight line detection algorithm and video text detection and localization algorithm respectively. The main contributions of this thesis are as follows:(1) An edge feature detection model based on gradient and phase (GP model) is proposed. This model achieves the combination of gradient and phase information. The use of GP model for detecting features has significant advantages over gradient-based feature detection methods which are sensitive to variations in image contrast. Meanwhile, our model can solve the "feature separation" problem which exists in phase congruency model. Moreover, our model can solve color image’s phase congruency checking problem. Through the theoretic analysis and a large scale of experiments, we showe the GP model can implement low-level feature detection reliably, and can be well applied to visual information processing applications.(2) A novel Hough-based algorithm for straight line feature detection is proposed. According to the spatial resolution of the image and the direction of the line, the proper parameter resolution can be selected. First, the algorithm provides a coarse parameter resolution based on a low-resolution Hough transform which is used to initially identify approximate line direction. Second, flexible coarse-to-fine iterations will be performed until the accurate line direction parameter is obtained. It first analyzes the local parameter space at a coarse resolution and then zooms down into the vicinity of the peak at successive iterations, while the Hough transform is performed on a successively revised parameter resolution. The rapid convergence and accurate detection can be achieved benefit from the feedback strategy. Meanwhile we design a more comprehensive straight line end-points detection algorithm, which ensures to detect line segments in point set of varying discrete degrees at a high precision. (3) A novel gradient and phase based approach to video text detection and localization is presented. Through utilizing the adaptive threshold and the statistics coarseness feature of each horizontal (vertical) pixel line of difference image, the text location detection is carried out fast and effectively. To suppress the complex background interference, and perform the video text localization accurately and effectively, we detect the text key feature point based on the GP model in the local area. The proposed method is robust to various video image quality, background complexities and text appearances.These model and algorithms are applied to stamp digital museum project, and video text detection, and video quality analysis, which achieve satisfied effect.更多还原