iOS上的OpenCV：與SurfFeatureDetector和FlannBasedMatcher錯誤匹配

我試圖使用OpenCV的特徵檢測工具來決定是否在較大的場景圖像中存在小樣本圖像。
我使用here的代碼作爲參考（沒有單應性部分）。iOS上的OpenCV：與SurfFeatureDetector和FlannBasedMatcher錯誤匹配

UIImage *sceneImage, *objectImage1; 
cv::Mat sceneImageMat, objectImageMat1; 
cv::vector<cv::KeyPoint> sceneKeypoints, objectKeypoints1; 
cv::Mat sceneDescriptors, objectDescriptors1; 
cv::SurfFeatureDetector *surfDetector; 
cv::FlannBasedMatcher flannMatcher; 
cv::vector<cv::DMatch> matches; 
int minHessian; 
double minDistMultiplier; 

minHessian = 400; 
minDistMultiplier= 3; 
surfDetector = new cv::SurfFeatureDetector(minHessian); 

sceneImage = [UIImage imageNamed:@"twitter_scene.png"]; 
objectImage1 = [UIImage imageNamed:@"twitter.png"]; 

sceneImageMat = cv::Mat(sceneImage.size.height, sceneImage.size.width, CV_8UC1); 
objectImageMat1 = cv::Mat(objectImage1.size.height, objectImage1.size.width, CV_8UC1); 

cv::cvtColor([sceneImage CVMat], sceneImageMat, CV_RGB2GRAY); 
cv::cvtColor([objectImage1 CVMat], objectImageMat1, CV_RGB2GRAY); 

if (!sceneImageMat.data || !objectImageMat1.data) { 
    NSLog(@"NO DATA"); 
} 

surfDetector->detect(sceneImageMat, sceneKeypoints); 
surfDetector->detect(objectImageMat1, objectKeypoints1); 

surfExtractor.compute(sceneImageMat, sceneKeypoints, sceneDescriptors); 
surfExtractor.compute(objectImageMat1, objectKeypoints1, objectDescriptors1); 

flannMatcher.match(objectDescriptors1, sceneDescriptors, matches); 

double max_dist = 0; double min_dist = 100; 

for(int i = 0; i < objectDescriptors1.rows; i++) 
{ 
    double dist = matches[i].distance; 
    if(dist < min_dist) min_dist = dist; 
    if(dist > max_dist) max_dist = dist; 
} 

cv::vector<cv::DMatch> goodMatches; 
for(int i = 0; i < objectDescriptors1.rows; i++) 
{ 
    if(matches[i].distance < minDistMultiplier*min_dist) 
    { 
     goodMatches.push_back(matches[i]); 
    } 
} 
NSLog(@"Good matches found: %lu", goodMatches.size()); 

cv::Mat imageMatches; 
cv::drawMatches(objectImageMat1, objectKeypoints1, sceneImageMat, sceneKeypoints, goodMatches, imageMatches, cv::Scalar::all(-1), cv::Scalar::all(-1), 
       cv::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); 

for(int i = 0; i < goodMatches.size(); i++) 
{ 
    //-- Get the keypoints from the good matches 
    obj.push_back(objectKeypoints1[ goodMatches[i].queryIdx ].pt); 
    scn.push_back(objectKeypoints1[ goodMatches[i].trainIdx ].pt); 
} 

cv::vector<uchar> outputMask; 
cv::Mat homography = cv::findHomography(obj, scn, CV_RANSAC, 3, outputMask); 
int inlierCounter = 0; 
for (int i = 0; i < outputMask.size(); i++) { 
    if (outputMask[i] == 1) { 
     inlierCounter++; 
    } 
} 
NSLog(@"Inliers percentage: %d", (int)(((float)inlierCounter/(float)outputMask.size()) * 100)); 

cv::vector<cv::Point2f> objCorners(4); 
objCorners[0] = cv::Point(0,0); 
objCorners[1] = cv::Point(objectImageMat1.cols, 0); 
objCorners[2] = cv::Point(objectImageMat1.cols, objectImageMat1.rows); 
objCorners[3] = cv::Point(0, objectImageMat1.rows); 

cv::vector<cv::Point2f> scnCorners(4); 

cv::perspectiveTransform(objCorners, scnCorners, homography); 

cv::line(imageMatches, scnCorners[0] + cv::Point2f(objectImageMat1.cols, 0), scnCorners[1] + cv::Point2f(objectImageMat1.cols, 0), cv::Scalar(0, 255, 0), 4); 
cv::line(imageMatches, scnCorners[1] + cv::Point2f(objectImageMat1.cols, 0), scnCorners[2] + cv::Point2f(objectImageMat1.cols, 0), cv::Scalar(0, 255, 0), 4); 
cv::line(imageMatches, scnCorners[2] + cv::Point2f(objectImageMat1.cols, 0), scnCorners[3] + cv::Point2f(objectImageMat1.cols, 0), cv::Scalar(0, 255, 0), 4); 
cv::line(imageMatches, scnCorners[3] + cv::Point2f(objectImageMat1.cols, 0), scnCorners[0] + cv::Point2f(objectImageMat1.cols, 0), cv::Scalar(0, 255, 0), 4); 

[self.mainImageView setImage:[UIImage imageWithCVMat:imageMatches]];

這工作，但我不斷收到火柴顯著量，即使在小圖像不是一個大的一部分。
下面是一個很好的示例輸出：
Good Output
這裏還有一個壞輸出的例子：
Bad Output
兩個輸出是相同的代碼的結果。小樣本圖像只有不同之處。
像這樣的結果，我不可能知道樣本圖像何時不在較大圖像中。
在做我的研究時，我發現了this stackoverflow問題。我遵循那裏給出的答案，並嘗試了「OpenCV 2計算機視覺應用程序編程手冊」一書中提出的步驟，但我無法使它適用於不同大小的圖像（似乎是cv :: findFundamentalMat函數）。

我錯過了什麼？有沒有辦法使用SurfFeatureDetector和FlannBasedMatcher來知道一個樣本圖像是一個較大圖像的一部分，而另一個樣本圖像不是？有沒有更好的方法來達到這個目的？

更新：
我更新了上面的代碼以包含我使用的完整函數，包括嘗試實際繪製單應性。另外，這裏有3個圖像 - 1個場景，以及我想在場景中找到的兩個小物體。我掌握了爪子圖標的內部百分比，而不是實際在場景中的Twitter圖標。另外，單應不繪製因爲某些原因：
Twitter Icon
Paw Icon
Scene