Line Detection Algorithm Using Freeman Criteria

提出了一种简单而高效的在二值图像中检测目标物体直线边界的算法 基于Freeman提出的关于数字直线的准则和数字直线的特征 ,得出线段元是数字直线的组成部分这一性质 基于该性质 ,该算法以线段元为基本单位进行直线的构造 ,从而能高效、准确地检测出图像中物体边界中的直线 此外 ,该算法还可用于检测二值图像中物体边界的拐角This paper proposes a simple and efficient algorithm to detect line edge of objects in a binary image. Based on the criteria and characteristic of digital line suggested by Freeman, we derived that digital line is composed of sets of line segment cell. Derived from this property of line cell, in the algorithm proposed, line cells are used for connection to form line segment. It makes the algorithm very efficient and precise. This algorithm can also be used to detect corner of objects in binary images

Corner detection algorithm based on euclidean distance

拐点是数字图像中的一个重要信息载体,提出一种新的拐点检测算法,该算法并非寻找连续空间中曲率的离散近似计算方法,而是源于离散曲线的外观特征,推导出离散曲线上拐点处k个点对间欧氏距离平方和局部最小这一重要性质。基于该性质,本算法首先利用Freeman链码的性质过滤掉物体边界上明显不可能成为拐点的象素,然后在剩余的边界点中通过寻找该局部最小值定位出拐点。给出了本算法与四种著名拐点检测算法的对比实验。Corners are important information carriers in computer vision. A new algorithm was presented here to detect corners on contour in digital image. This algorithm was not going to search another way to approximately calculate the curvature of points on curves,which was defined in continuous domain,but utilized the character of corners in digital nature that the square sum of k Euclidean distance between points pair centered at a corner is locally lowest. Derived from this character,the new algorithm detected corners in a two-pass manner. First pass was to filter the points on a curve that obviously can not be corners by using Freeman chain-code. Second pass was to detect the locations of local minima of the square sum of Euclidean distance. Tests comparing the new algorithm to four famous algorithms were given

Real-Time Corner Detection in Binary Image

鉴于数字图像中的拐点通常成为重要的信息载体,因此准确、稳定和实时地检测出拐点便成为拐点检测算法面临的主要问题,针对该问题,提出了一种新的二值图像中拐点的实时检测算法。该算法与传统基于边界链码的拐点检测算法不同,其是首先构建像素的k(k>8)邻域,并将图像中物体的边界表示为k邻域链码;然后根据曲率定义的差分形式计算各边界点处的曲率;最后通过检测曲率直方图的局部峰值精确定位出拐点,并利用拐角内部像素的颜色统计信息迅速判断出拐点的凸凹性.为验证该算法的效果,给出了该算法与4种已有算法的对比实验.结果表明,该算法不仅稳定性、准确性较高,而且算法简单,实时性强,并适合于嵌入式计算环境。Presents a new real time corner detection algorithm. Corners are important information carriers in object recognition. Accurate, stable and fast detecting corners in digital image are common problems facing to corner detectors. Aiming at these problems and different from traditional corner detection algorithms, based on chain code, the algorithm constructs k(k>8) neighborhood chain codes of pixels and uses these chain codes to describe contours. Based on the differential definition of curvature, a curvature function is derived from k neighborhood chain codes. Corners are detected as those contour pixels, whose curvature the is largest in a lobe of contour curvature histogram. Convex and concave corners can be differentiated quickly by checking color attributes of pixels between corner edges. To validate the algorithm, tests comparing the new algorithm to 4 corner detection algorithms are given. The results show the new algorithm is not only accurate and stable, but also simple and fast, which make the algorithm suitable for the embedded computation environment

巴夫杜氏藻β-肌动蛋白基因的克隆和分析

目的获得巴夫杜氏藻β-肌动蛋白基因cDNA全长序列。方法以巴夫杜氏藻cDNA为模板,采用简并引物进行PCR扩增,获得533bp特异cDNA片段。在此基础上,设计特异引物,采用5’-Genome Walking和3’-RACE的方法,获得基因的5’-端DNA序列和3’-端cDNA序列,进而获得β-肌动蛋白基因cDNA全长序列。结果获得了巴夫杜氏藻β-肌动蛋白基因的特异cDNA片段、5’-端DNA和3’-端cDNA片段。经拼接后,扩增出全长cDNA。β-肌动蛋白基因cDNA全长1754bp,包括1137bp的开放读码框和617bp的3’-非翻译区序列。氨基酸序列相似性分析发现,巴夫杜氏藻β-肌动蛋白氨基酸序列与杜氏盐藻、莱茵衣藻等的同源性较高。系统发育分析表明,巴夫杜氏藻β-肌动蛋白与杜氏盐藻的相似性最高。结论首次获得了巴夫杜氏藻β-肌动蛋白基因cDNA全长序列,并发现巴夫杜氏藻β-肌动蛋白基因非常保守

巴夫杜氏藻GAPDH基因部分序列的克隆

3-磷酸甘油醛脱氢酶（GAPDH）是糖酵解途径中的一个关键酶,由于其基因表达量相对恒定,因此在定量和半定量PCR试验中常被用作内源参照基因来确定目标基因的相对表达量。从巴夫杜氏藻（Dunaliella parva）中克隆GAPDH基因,并对基因序列进行了分析。以巴夫杜氏藻cDNA为模板,根据GAPDH的保守序列设计引物,PCR扩增获得了673 bp的cDNA序列。在此基础上,采用cDNA末端快速扩增（RACE）技术,获得了913 bp的3′-cDNA序列,进而获得了cDNA部分序列1 393 bp。序列分析结果表明：巴夫杜氏藻GAPDH cDNA部分序列包括1 055 bp的编码区和338 bp的3′非翻译区。该基因的克隆为半定量和定量PCR等技术在巴夫杜氏藻分子生物学研究中的应用提供了内源参照基因