一种基于彩色图像分割的车牌检测方法

提出一种彩色图像车牌检测方法,主要包括三个步骤,首先对车牌图像执行多尺度区域生长分割,然后在分割结果中进行车牌区域的粗定位,最后对粗定位提取的车牌区域作精确定位分析.区域生长分割方法可以产生封闭的区域,容易提取其形状信息,而长矩形是车牌目标一个显著特征,因此可以通过形状信息检测车牌.在图像分割中,采取多尺度的分割方法,生成多个不同细节程度的分割结果.在每个分割图像中搜索具有车牌外形特征的图像区域,初步检测出候选车牌目标.根据车牌字符的大小和笔画特征,对候选车牌区域进行精确判别分析,得到最终的车牌检测结果.实验部分验证了车牌检测的有效性.     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

双目视觉测量在乒乓球运动速度分析中的应用

本文以乒乓球运动为例,提出了一种使用AVT函数从视频序列中分解帧,然后逐帧进行预处理和区域生长分割,从而实现目标提取的方法.通过放置在乒乓球台上方两侧的双目视觉测量系统对严乓乓球的运动速度等参数进行了测量,进行了数字图像处理技术在球类运动分析中的探索.     

Road Extraction Using Airborne LiDAR Data in Mountainous Areas

Based on the spatial distribution and characteristics of LiDAR points cloud of roads in mountainous areas，an effective method for road extraction from airborne LiDAR data is proposed in this research.First，the morphological filtering method is applied to remove above\|ground points cloud (such as buildings，transmission lines and vegetation etc.).Second，a region growing algorithm with multiple rules is used to extract and optimize the road points cloud.Finally，the road boundaries are located and tracked by using Freeman chain code method.Moreover，the mathematical morphology refining processing is used to extract the central line of mountainous road.The experimental results show that the proposed method is effective to extract road information in mountainous areas，and the completeness，accuracy and quality are 93.87%，93.84％，88.43％，respectively.     

基于阈值法与区域生长法综合集成的图像分割法

本文首先对一类因照明不均匀使得图像质量受到一定影响的这样一类比较复杂的图像特点及其分割难点做分析,然后提出一种新的基于阈值法与区域生长法综合集成的图像分割方法。实验结果表明,该分割算法不仅适用于较简单的图像分割问题,而且适用于分割因照明不均匀使得图像质量受到一定影响的这样一类较复杂的图像的分割,且分割时间可进一步缩短。     

A robust approach to edge detection of scanned point data

In reverse engineering, segmentation is used to divide a point data set into subsequent regions according to its shape. It is vital for interpretation of discrete scanned data since surface reconstruction can be accomplished one-by-one on a given region. Edge detection is crucial to the segmentation process. The level of edge detection depends on the complexity of the part, and it determines the eventual success or failure of the reverse engineering (RE) process. This paper proposes a novel approach to the edge detection of 3D points based on a region growing technique. The proposed algorithm consists of two parts. First, polygonal meshes are generated to the scanned point data using the Delaunay triangulation algorithm. Second, the normal vector and the area of a polygonal mesh are checked to find boundary meshes using cost criteria (angle criterion and area criterion) based upon a region growing technique. The region growing technique aggregates meshes into a region until the area of aggregated meshes reaches an area threshold from a series of seed meshes. The proposed edge detection method is found to be effective when compared with other methods.     

Study on Power Tower Extraction and Fast Positioning from Airborne LiDAR Data

Airborne LiDAR has become an important technique for transmission line digitalization,reconstruction and safety inspection.Moreover,accurately and efficiently extracting the position of each tower from massive point clouds is basic and important task for the applications in power industry.In this study,a method was proposed to efficiently extract the point clouds and fast determine the position of power towers using airborne LiDAR data.Firstly,the point clouds of power towers were automatically separated from raw data based on the spatial distribution characteristics of airborne LiDAR data.Secondly,each power tower was efficiently detected using a region\|growing algorithm.Finally,a least square linear fitting method was used to determine the accurate position of each power tower.The new proposed method was applied to several LiDAR data sets in areas with high voltage transmission lines.Results indicated that the integrity of the power towers’ points is up to 91.1%,and the accuracy of center positions is high enough with the medium error of 13.5 cm.Additionally,our study also concluded that the proposed method is robust and applicable even the point density is relatively low.     

基于颜色和纹理特征提取彩色图像的有意义区域

针对猪肉眼肌彩色图像中眼肌区域的提取，提出一种基于颜色和纹理特征的彩色图像分割方法，实现了眼肌区域的自动分割。该研究在RGB彩色空间下进行，改进以往区域生长算法中种子点的选取方法，实现了基于彩色图像的区域生长算法，并比较以灰度为特征，以颜色为特征和以颜色与纹理相结合为特征进行区域生长的分割效果。研究结果表明，基于R、G、B和纹理特征的改进区域生长算法具．有较好的分割效果。