图像处理 英文翻译 private declare function varptrarray lib\"msvbvm60.dll\"alias\"varptr\"_(ptr()as any)as longprivate declare sub copymemory lib\"kernel32\"alias\"rtlmovememory\"_(pdst as any，psrc as any，byval bytelen as long)private declare function getobj lib\"gdi32\"alias\"getobjecta\"_(byval hobject as long，byval ncount as long，lpobject as any)as longprivate type safearrayboundcelements as longllbound as longend typeprivate type safearray2dcdims as integerffeatures as integercbelements as longclocks as longpvdata as longbounds(0 to 1)as safearrayboundend typeprivate type bitmapbmtype as longbmwidth as longbmheight as longbmwidthbytes as longbmplanes as integerbmbitspixel as integerbmbits as longend typeprivate declare function varptrarray lib\"msvbvm60.dll\"alias\"varptr\"_(ptr()as any)as longprivate declare sub copymemory lib\"kernel32\"alias\"rtlmovememory\"_(pdst as any，psrc as any，byval bytelen as long)private declare function getobj lib\"gdi32\"alias\"getobjecta\"_(byval hobject as long。
翻译下面英文 关于数字图像处理 在这个试验中，当我们使用邻域嵌入算法的时候，我们想要结合质量和特征点评估这种算法对高分辨图象的影响。根据弗里曼等人（2002年）的观点，我们也尝试接近一个在本地信息（即规范亮度）和寻找相容的临近信息（即一阶梯度特征）之间的合理的加权因子。在pi和pj这两种小块之间距离的措施被定义为DistGrad1和DistNormL分别代表在pi和pj之间Euclidean的一阶梯度距离和pi和pj之间的规范化亮度。这两个特征向量的权重因子被近似的设为4。推理的详细内容在补充材料里面而且这个假设也在多次实验所显示的结果证明。为了独立的找出相结合的特征的贡献，我们在这部分实验中没有应用其他的改进意见。取而代之的是应用了精确的程序和SRNE算法原先使用一阶和二阶梯度结合得出的参数作为特征值做出的以下三个评价翻的好辛苦。全手工的，分不给我以后不给你翻了
帮忙翻译一段有关数字图像处理的英文 （边缘检测）一种边缘检测方法基于优化BP神经网络图像边缘的瓷砖检测精度对维和缺陷检测瓷砖检测有很大影响.阿的BP（BP）的二值图像的边缘检测神经网络并行模型，本文提出，它是适用于灰度图像边缘检测.它解决了问题的.
数字图像处理的英文翻译 2.2.1.Gross approximation of the low thresholdThe low threshold for hysteresis is very important in negative detection because these edge points will not change its state during the linkage stage of the hysteresis process.Accuracy refers to the probability that a pixel will be properly classified(regardless of whether it is positive or negative).Therefore，our proposal should consider to make maximum Ac(x)for the approximation of the low threshold.Also，for the same objective，it is important to detect enough number of positives，making minimum the number of false positives(FP)，because these pixels can be re-classified during the linkage stage of the hysteresis process.Then our proposal should consider to make maximum Pr(x)also.Thus we propose the function Low with real valuesLow：{1，.，L 1} RLow(x)=Pr(x)+Ac(x)，x∈{1，.，L 1} to characterise the edge map Gb(I)in terms of the probability that a pixel will be accurately classified(Ac(x))and considering the probability that a pixel 。
