SIFT特征点匹配与消除错配：BBF，RANSAC(2)

TAG:

while iterations < k //进行K次迭代

maybe_inliers := n randomly selected values from data

maybe_model := model parameters fitted to maybe_inliers

consensus_set := maybe_inliers

for every point in data not in maybe_inliers

if point fits maybe_model with an error smaller than t //错误小于阈值t

add point to consensus_set //成为同盟，加入consensus set

if the number of elements in consensus_set is > d //同盟军已经大于d个人，够了

(this implies that we may have found a good model,

now test how good it is)

better_model := model parameters fitted to all points in consensus_set

this_error := a measure of how well better_model fits these points

if this_error < best_error

(we have found a model which is better than any of the previous ones,

keep it until a better one is found)

best_model := better_model

best_consensus_set := consensus_set

best_error := this_error

increment iterations

return best_model, best_consensus_set, best_error

2. RANSAC去除错配：

H = ransac_xform( feat1, n1, FEATURE_FWD_MATCH, lsq_homog, 4, 0.01,homog_xfer_err, 3.0, NULL, NULL );

nm = get_matched_features( features, n, mtype, &matched );

/* initialize random number generator */

rng = gsl_rng_alloc( gsl_rng_mt19937 );

gsl_rng_set( rng, time(NULL) );

in_min = calc_min_inliers( nm, m, RANSAC_PROB_BAD_SUPP, p_badxform ); //符合这一要求的内点至少得有多少个

p = pow( 1.0 - pow( in_frac, m ), k );

i = 0;

while( p > p_badxform )//p>0.01

{

sample = draw_ransac_sample( matched, nm, m, rng );

extract_corresp_pts( sample, m, mtype, &pts, &mpts );

M = xform_fn( pts, mpts, m );

if( ! M )

goto iteration_end;

in = find_consensus( matched, nm, mtype, M, err_fn, err_tol, &consensus);

if( in > in_max ) {

if( consensus_max )

free( consensus_max );

consensus_max = consensus;

in_max = in;

in_frac = (double)in_max / nm;

}

else

free( consensus );

cvReleaseMat( &M );

iteration_end:

release_mem( pts, mpts, sample );

p = pow( 1.0 - pow( in_frac, m ), ++k );

}

/* calculate final transform based on best consensus set */

if( in_max >= in_min )

{

extract_corresp_pts( consensus_max, in_max, mtype, &pts, &mpts );

M = xform_fn( pts, mpts, in_max );

in = find_consensus( matched, nm, mtype, M, err_fn, err_tol, &consensus);

cvReleaseMat( &M );

release_mem( pts, mpts, consensus_max );

extract_corresp_pts( consensus, in, mtype, &pts, &mpts );

M = xform_fn( pts, mpts, in );

思考中的一些问题：

features间的对应关系，记录在features->fwd_match里（matching feature from forward

imge）。

1. 数据是nm个特征点间的对应关系，由它们产生一个3*3变换矩阵（xform_fn = hsq_homog函数，此要>=4对的对应才可能计算出来咯~），此乃模型model。

2. 然后开始找同盟军（find_consensus函数），判断除了sample的其它对应关系是否满足这个模型（err_fn = homog_xfer_err函数，<=err_tol就OK~），满足则留下。

3. 一旦大于当前的in_max，那么该模型就升级为目前最牛的模型。（最最原始的RANSAC是按错误率最小走的，我们这会儿已经保证了错误率在err_tol范围内，按符合要求的对应数最大走，尽量多的特征能匹配地上）

4. 重复以上3步，直到(1-w^m)^k<=p_badxform (即0.01)，模型就算找定~

5. 最后再把模型和同盟军定一下，齐活儿~

声明：以上代码参考Rob Hess的SIFT实现。

(ijuliet)

搜索

热门标签:

SIFT特征点匹配与消除错配：BBF，RANSAC(2)