計算機視覺CV 之 CMT跟蹤算法分析四

1 前言

在上一部分我們已經分析到了計算特徵點的縮放和旋轉，這裏最後分析去掉不好的特徵點的方法。

2 最後步驟分析

vote的基本思想就是這些特徵點相對中心的相對距離在把縮放旋轉考慮進去之後是相對不變的，也就是按道理下一幀的特徵點相對中心的位置是不變的。

但是由於圖像本身的變化，不可能得到完全一樣的相對位置，這個時候，有一些會離中心近，有一些會偏差很大。那麼，作者就採用聚類的方法，選擇最大的一類作爲最好的特徵點。其他的不要。

上面這個圖應該可以很好的理解這個過程。再看作者自己官網上的圖大家應該可以理解。

代碼上作者找了一個聚類的庫來做，具體我沒有深入分析了：

void Consensus::findConsensus(const vector<Point2f> & points, const vector<int> & classes,
        const float scale, const float rotation,
        Point2f & center, vector<Point2f> & points_inlier, vector<int> & classes_inlier)
{

    //If no points are available, reteurn nan
    if (points.size() == 0)
    {
        center.x = numeric_limits<float>::quiet_NaN();
        center.y = numeric_limits<float>::quiet_NaN();

        return;
    }

    //Compute votes 計算投票：基本方法就是計算點相對於正規化且計算其旋轉加縮放後的點的相對位置 保持相對一致
    vector<Point2f> votes(points.size());
    for (size_t i = 0; i < points.size(); i++)
    {
        votes[i] = points[i] - scale * rotate(points_normalized[classes[i]], rotation);
    }

    t_index N = points.size();

    float * D = new float[N*(N-1)/2]; //This is a lot of memory, so we put it on the heap
    cluster_result Z(N-1);

    //Compute pairwise distances between votes
    //計算votes點之間的相對距離
    int index = 0;
    for (size_t i = 0; i < points.size(); i++)
    {
        for (size_t j = i+1; j < points.size(); j++)
        {
            //TODO: This index calculation is correct, but is it a good thing?
            //int index = i * (points.size() - 1) - (i*i + i) / 2 + j - 1;
            // 計算相對距離
            D[index] = norm(votes[i] - votes[j]);
            index++;
        }
    }

    MST_linkage_core(N,D,Z);

    union_find nodes(N);

    //Sort linkage by distance ascending
    std::stable_sort(Z[0], Z[N-1]);

    //S are cluster sizes
    int * S = new int[2*N-1];
    //TODO: Why does this loop go to 2*N-1? Shouldn't it be simply N? Everything > N gets overwritten later
    for(int i = 0; i < 2*N-1; i++)
    {
        S[i] = 1;
    }

    t_index parent = 0; //After the loop ends, parent contains the index of the last cluster
    for (node const * NN=Z[0]; NN!=Z[N-1]; ++NN)
    {
        // Get two data points whose clusters are merged in step i.
        // Find the cluster identifiers for these points.
        t_index node1 = nodes.Find(NN->node1);
        t_index node2 = nodes.Find(NN->node2);

        // Merge the nodes in the union-find data structure by making them
        // children of a new node
        // if the distance is appropriate
        if (NN->dist < thr_cutoff)
        {
            parent = nodes.Union(node1, node2);
            S[parent] = S[node1] + S[node2];
        }
    }

    //Get cluster labels
    int * T = new int[N];
    for (t_index i = 0; i < N; i++)
    {
        T[i] = nodes.Find(i);
    }

    //Find largest cluster
    int S_max = distance(S, max_element(S, S + 2*N-1));

    //Find inliers, compute center of votes
    points_inlier.reserve(S[S_max]);
    classes_inlier.reserve(S[S_max]);
    center.x = center.y = 0;

    for (size_t i = 0; i < points.size(); i++)
    {
        //If point is in consensus cluster
        if (T[i] == S_max)
        {
            points_inlier.push_back(points[i]);
            classes_inlier.push_back(classes[i]);
            center.x += votes[i].x;
            center.y += votes[i].y;
        }

    }

    center.x /= points_inlier.size();
    center.y /= points_inlier.size();

    delete[] D;
	delete[] S;
	delete[] T;

}

通過這樣的算法得到inlier

然後在代碼中，作者又做了一次匹配，matchlocal，在我看來和findconsensus的目的是一樣的，也是通過相對的點的距離來判定是不是要的特徵，然後在對這些特徵做一次匹配，是就選進來，最後將inlier的點和matchlocal的點合併，作爲最終的特徵點。

matchlocal的代碼如下：

void Matcher::matchLocal(const vector<KeyPoint> & keypoints, const Mat descriptors,
        const Point2f center, const float scale, const float rotation,
        vector<Point2f> & points_matched, vector<int> & classes_matched)
{

    if (keypoints.size() == 0) {
        return;
    }

    //Transform initial points
    vector<Point2f> pts_fg_trans;
    pts_fg_trans.reserve(pts_fg_norm.size());
    for (size_t i = 0; i < pts_fg_norm.size(); i++)
    {
        // 同樣是計算相對位置
        pts_fg_trans.push_back(scale * rotate(pts_fg_norm[i], -rotation));
    }

    //Perform local matching
    for (size_t i = 0; i < keypoints.size(); i++)
    {
        //Normalize keypoint with respect to center
        Point2f location_rel = keypoints[i].pt - center;

        //Find potential indices for matching
        vector<int> indices_potential;
        for (size_t j = 0; j < pts_fg_trans.size(); j++)
        {
            // 計算位置偏差
            float l2norm = norm(pts_fg_trans[j] - location_rel);

            // 設置一個閾值
            if (l2norm < thr_cutoff) {
                indices_potential.push_back(num_bg_points + j);
            }

        }

        //If there are no potential matches, continue
        if (indices_potential.size() == 0) continue;

        //Build descriptor matrix and classes from potential indices
        Mat database_potential = Mat(indices_potential.size(), database.cols, database.type());
        for (size_t j = 0; j < indices_potential.size(); j++) {
            database.row(indices_potential[j]).copyTo(database_potential.row(j));
        }

        //Find distances between descriptors
        vector<vector<DMatch> > matches;
        // 對選出的特徵點進行特徵匹配
        bfmatcher->knnMatch(descriptors.row(i), database_potential, matches, 2);

        vector<DMatch> m = matches[0];

        float distance1 = m[0].distance / desc_length;
        float distance2 = m.size() > 1 ? m[1].distance / desc_length : 1;

        if (distance1 > thr_dist) continue;
        if (distance1/distance2 > thr_ratio) continue;

        int matched_class = classes[indices_potential[m[0].trainIdx]];

        points_matched.push_back(keypoints[i].pt);
        classes_matched.push_back(matched_class);
    }

}

好了，由於時間關係，CMT算法就分析到這了。有很多不足，可能也分析不到位甚至有錯的地方，請批評指正。

文章原創，轉載麻煩註明出處：blog.csdn.net/songrotek