?? opencv用戶手冊之圖像處理部分（之一）：梯度、邊緣與角點（中文翻譯） - hunnish的opencv專欄.htm
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<P class=Blurb><SPAN
style="FONT-FAMILY: 宋體; mso-ascii-font-family: Helvetica; mso-hansi-font-family: Helvetica">精確角點位置</SPAN></P><PRE>void cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners,
int count, CvSize win, CvSize zero_zone,
CvTermCriteria criteria );
</PRE>
<P>
<DL>
<DT>image
<DD>輸入圖像.
<DT>corners
<DD>輸入角點的初始坐標，也存儲精確的輸出坐標
<DT>count
<DD>角點數目
<DT>win
<DD>搜索窗口的一半尺寸。如果 <CODE><SPAN lang=EN-US>win</SPAN></CODE><SPAN
lang=EN-US>=(5,5) 那么使用 5*2+1 × 5*2+1 = 11 × 11 大小的搜索窗口</SPAN>
<DT>zero_zone
<DD>死區的一半尺寸，死區為不對搜索區的中央位置做求和運算的區域。它是用來避免自相關矩陣出現的某些可能的奇異性。當值為<SPAN lang=EN-US>
(-1,-1) 表示沒有死區。</SPAN>
<DT>criteria
<DD>求角點的迭代過程的終止條件。即角點位置的確定，要么迭代數大于某個設定值，或者是精確度達到某個設定值。 <CODE><SPAN
lang=EN-US>criteria</SPAN></CODE><SPAN lang=EN-US> 可以是最大迭代數目，也可以是精確度</SPAN>
</DD></DL>
<P>函數 <SPAN lang=EN-US><A
href="file:///C:/DOCUME~1/user/LOCALS~1/Temp/FrontPageTempDir/pvw1.htm#decl_cvFindCornerSubPix">cvFindCornerSubPix</A>
通過迭代來發現具有子象素精度的角點位置，或如圖所示的放射鞍點（radial saddle points）。</SPAN></P>
<P><IMG src="" align=center> </P>
<P>Sub-pixel accurate corner locator is based on the observation that every
vector from the center <CODE>q</CODE> to a point <CODE>p</CODE> located within a
neighborhood of <CODE>q</CODE> is orthogonal to the image gradient at
<CODE>p</CODE> subject to image and measurement noise. Consider the
expression:</P><PRE>ε<SUB>i</SUB>=DI<SUB>p<SUB>i</SUB></SUB><SUP>T</SUP>?(q-p<SUB>i</SUB>)
</PRE>
<P>where <CODE>DI<SUB>p<SUB>i</SUB></SUB></CODE> is the image gradient at the
one of the points <CODE>p<SUB>i</SUB></CODE> in a neighborhood of
<CODE>q</CODE>. The value of <CODE>q</CODE> is to be found such that
<CODE>ε<SUB>i</SUB></CODE> is minimized. A system of equations may be set up
with <CODE>ε<SUB>i</SUB></CODE>' set to zero: </P><PRE>sum<SUB>i</SUB>(DI<SUB>p<SUB>i</SUB></SUB>?DI<SUB>p<SUB>i</SUB></SUB><SUP>T</SUP>)?q - sum<SUB>i</SUB>(DI<SUB>p<SUB>i</SUB></SUB>?DI<SUB>p<SUB>i</SUB></SUB><SUP>T</SUP>?p<SUB>i</SUB>) = 0
</PRE>
<P>where the gradients are summed within a neighborhood ("search window") of
<CODE>q</CODE>. Calling the first gradient term <CODE>G</CODE> and the second
gradient term <CODE>b</CODE> gives:</P><PRE>q=G<SUP>-1</SUP>?b
</PRE>
<P>The algorithm sets the center of the neighborhood window at this new center
<CODE>q</CODE> and then iterates until the center keeps within a set
threshold.</P>
<HR>
<H3><A name=decl_cvGoodFeaturesToTrack>GoodFeaturesToTrack</A></H3>
<P class=Blurb><SPAN
style="FONT-FAMILY: 宋體; mso-ascii-font-family: Helvetica; mso-hansi-font-family: Helvetica">確定圖像的強角點</SPAN></P><PRE>void cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image, CvArr* temp_image,
CvPoint2D32f* corners, int* corner_count,
double quality_level, double min_distance,
const CvArr* mask=NULL );
</PRE>
<P>
<DL>
<DT>image
<DD>輸入圖像，<SPAN lang=EN-US>8-位或浮點32-比特，單通道</SPAN>
<DT>eig_image
<DD>臨時浮點<SPAN lang=EN-US>32-位圖像，大小與輸入圖像一致</SPAN>
<DT>temp_image
<DD>另外一個臨時圖像，格式與尺寸與 <CODE><SPAN lang=EN-US>eig_image</SPAN></CODE><SPAN
lang=EN-US> 一致</SPAN>
<DT>corners
<DD>輸出參數，檢測到的角點
<DT>corner_count
<DD>輸出參數，檢測到的角點數目
<DT>quality_level
<DD>最大最小特征值的乘法因子。定義可接受圖像角點的最小質量因子。
<DT>min_distance
<DD>限制因子。得到的角點的最小距離。使用<SPAN lang=EN-US> Euclidian 距離</SPAN>
<DT>mask
<DD><SPAN lang=EN-US>ROI:感興趣區域。函數在ROI中計算角點，如果 mask 為 NULL，則選擇整個圖像。</SPAN>
</DD></DL>
<P>函數 <SPAN lang=EN-US><A
href="file:///C:/DOCUME~1/user/LOCALS~1/Temp/FrontPageTempDir/pvw1.htm#decl_cvGoodFeaturesToTrack">cvGoodFeaturesToTrack</A>
在圖像中尋找具有大特征值的角點。該函數，首先用<A
href="file:///C:/DOCUME~1/user/LOCALS~1/Temp/FrontPageTempDir/pvw1.htm#decl_cvCornerMinEigenVal">cvCornerMinEigenVal</A>
計算輸入圖像的每一個象素點的最小特征值，并將結果存儲到變量 </SPAN><CODE><SPAN lang=EN-US>eig_image
中。</SPAN></CODE>然后進行非最大值壓縮（僅保留<SPAN lang=EN-US>3x3鄰域中的局部最大值）。下一步將最小特征值小于
</SPAN><CODE><SPAN lang=EN-US>quality_level</SPAN></CODE><SPAN
lang=EN-US>?max(</SPAN><CODE><SPAN lang=EN-US>eig_image</SPAN></CODE><SPAN
lang=EN-US>(x,y)) 排除掉。最后，函數確保所有發現的角點之間具有足夠的距離，（最強的角點第一個保留，然后檢查新的角點與已有角點之間的距離大于
</SPAN><CODE><SPAN lang=EN-US>min_distance ）。</SPAN></CODE></P><BR><BR>
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[<A href="javascript:StorePage()">點擊此處收藏本文</A>] 發表于 2004年09月03日 9:23
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<H3></H3><A name=95841> </A>
<DIV class=post>
<DIV class=postTitle>victor 發表于2004-09-06 2:32 PM IP:
211.100.22.*</DIV>
<DIV class=postText>牛！</DIV></DIV><BR><A name=129069> </A>
<DIV class=post>
<DIV class=postTitle>大姐 發表于2004-10-09 10:52 AM IP:
61.187.54.*</DIV>
<DIV
class=postText>請問角點提取采用的是哪種算法？能否提供原文？標定模板的交叉角點的提取是不是也是采用這種算法？</DIV></DIV><BR></DIV>
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