基于边缘检测的动态误差分散半色调算法

针对数字半色调技术中的传统误差分散方法因误差分散系数固定而导致图像真实细节损失的问题,提出了一种基于边缘检测的动态误差分散算法。该方法沿着每个像素的误差分散方向进行边缘检测,再根据边缘检测结果动态地选择该像素各个方向的误差分散系数,减小了误差分散过程中的误差积累。实验表明,此方法可以明显改善由于误差分散系数固定而引起的图像轮廓细节损失。     

基于数学形态变换的骨骼CT图像边缘提取

本文提出了数学形态变换结合Canny边缘算子提取图像边缘的方法,通过研究骨骼CT图像边缘提取,证明该方法几何意义明确,去噪效果明显,性能优越。     

基于小波变换的球团矿图像边缘检测

球团粒径及其分布是衡量球团矿质量的重要指标。球团矿图像的边缘检测对基于数字图像处理的球团粒径自动检测具有重要意义。但是，由于成像中的噪声及球团图像的无背景等特点，使得球团图像的边缘检测比较困难。本文利用小波变换系数模局部极大值来检测球团图像的边缘，取得了较好的效果。     

An Efficient Active Contour Model Through Curvature Scale Space Filtering

Active contour models can be successfully used in multimedia database retrieval systems if they have good accuracy and high speed. The majority of existing active contour models do not lock on to interest objects very accurately and quickly especially in complex images. The behavior of the active contour is generally controlled by its internal and external energies. Internal energy is composed of two parts; the first part acts to shorten the active contour as it iterates towards the interest object, while the second part is the curvature of the active contour and forces smoothness of active contour during its movement towards interest object. In this paper, first a reformulated internal energy is proposed to improve the computation of curvature at point v _i by making use of the three points v _{i – 1}, v _i and v _{i + 1}. Second, an accurate and high speed active contour model, SAC is proposed based on reformulating internal energy by removing the curvature part and using Gaussian filtering with low scale of smoothing. The SAC model has only one parameter that affects the internal energy of active contour and as a result of using the Curvature Scale Space (CSS)¹ technique for smoothing, the SAC model is more independent of model parameter setting and the initial snake.     

利用相似性比较提取车轮图像

提出一种从大量的列车序列图像中提取包含完整车轮的清晰图像的方法．采用两级分析的方法，首先利用直方图交叉方法比较标准图像与候选图像之间的直方图相似度；然后用小波变换对图像进行多尺度分解，从小波系数中计算图像的各个方向边缘特征；最后将这两种特征结合起来，将那些满足阈值条件的图像选择出来．实验和实际应用表明，该方法可以有效地将清晰的车轮图像提取出来。     

一种高效的逆半调算法

论文提出了一种快速的逆半调算法,在高斯低通滤波的基础上,通过带通滤波提取边界信息,并使用界限函数和中值滤波修正边界映像,最后合成逆半调结果。实验证明该算法和现有算法相比计算速度快,输出图像信噪比高。     

HL-2A Tokamak Edge Modeling with B2

The outer divertor plasma of HL-2A and its associated scrape-off plasma have been simulated using a two-dimensional multi-species fluid code of Braams with a simplified neutral gas model. HL-2A has a double-null closed divertor in separate divertor chambers above and below the nearly circular plasma tours. The computed numerical grid is developed according to an ideal magnetic surface. The calculation is involved only with pure hydrogen plasma. The emphasis has been focused on parametric studies involving variation of the assumptions made for the core plasma. The peak temperatures and the heat flux near the target are of the principal concern。     

高质量图象压缩的自适应DPCM/DCT混合编码方法

DCT是数据压缩的主要技术之一 ,它能将图象的主要能量集中在很少的几个 DCT系数上 .可是 ,DCT对于含有较多边缘的图象块并不十分有效 ,因为 DCT变换作用于这样的图象块后不能导致有效的能量集中 .文中提出了一种自适应 DPCM/ DCT混和编码方法用于解决这一问题 .对于每一个图象块 ,边缘检测器自动判断该块是否含有较强的边缘 ,如果含有较强的边缘 ,则使用DPCM编码器 ;如果不含有较强的边缘 ,则使用 DCT编码器 .DPCM编码器自动从给定的预测器中选定一个产生最小预测误差的预测器 ,然后用 Rice编码器对误差图象编码 .对于非边缘块 ,使用DCT、游程和变长编码技术 .实验结果显示文中所提出的方法在压缩比和解码时间方面优于 JPEG无失真模式和标准 JPEG模式 (当 bpp=1— 4时 )     

On the Precision in Estimating the Location of Edges and Corners

Recently, in Rohr [13], we analyzed the systematiclocalization errors introduced by local operators for detectinggrey-value corners. These errors are inherently due to thedifferential structure of the operators and, in general, areenlarged by discretization and noise effects. Here, we take thestatistical point of view to analyze the localization errorscaused by noisy data. We consider a continuous image model thatrepresents the blur as well as noise introduced by an imagingsystem. In general, the systematic intensity variations arenonlinear functions of the location parameters. For this modelwe derive analytic results stating lower bounds for the locationuncertainty of image features. The lower bounds are evaluatedfor explicit edge and corner models. We show that the precisionof localization in general depends on the noise level, on thesize of the observation window, on the width of the intensitytransitions, as well as on other parameters describing thesystematic intensity variations. We also point out that theuncertainty lower bounds in localizing these image features canin principle be attained by fitting parametric models directlyto the image intensities. To give an impression of theachievable accuracy numerical examples are presented.     

用微机CAD/CAM一体化系统─SCADAM求解锥形导叶空间密合线及密合倾角

叙述了用ＳＴＡＲＣＡＤ／ＣＡＭ求解贯流式水轮机锥形导叶空间密合线及密合面倾角的条件、方法及过程。