Rob Hess的SIFT程序详细解释.docx

/* default number of sampled intervals per octave sub-level 每个尺度空间的采样间隔数*/初始值为3#define SIFT_INTVLS 3/* default sigma for initial gaussian smoothing 高斯平滑的数目*/初始值是1.6#define SIFT_SIGMA 1.6/* default threshold on keypoint contrast |D(x)| 阈值点 取值0-1 小于这个即剔除低对比度的伪特征点*/ 值越大，被剔除的特征点就越多#define SIFT_CONTR_THR 0.04/初始值是0.04/* default threshold on keypoint ratio of principle curvatures */主曲率阈值r=10，大于10即去掉边缘响应点#define SIFT_CURV_THR 10/初始值是10/* double image size before pyramid construction? 金字塔构造 */在建立尺度空间之前如果原图像放大一倍则取值为1，否则为0#define SIFT_IMG_DBL 1/* default width of descriptor histogram array */descr_width:计算特征描述符时邻域子块的宽度，#define SIFT_DESCR_WIDTH 4/初始值为4/* default number of bins per histogram in descriptor array */计算特征描述符时将特征点邻域进行投影的方向数，默认为8，分别是0，45，90，135 180，225，270，315共8个方向。#define SIFT_DESCR_HIST_BINS 8/* assumed gaussian blur for input image 为了产生更多的keypointsLowe在SIFT算法的开始通过线性插值将原始图像扩大一倍，并且进行了一个=0.5的高斯平滑预处理 */#define SIFT_INIT_SIGMA 0.5/初始值是0.5/* width of border in which to ignore keypoints */忽略特征点的边界宽度#define SIFT_IMG_BORDER 5/* maximum steps of keypoint interpolation before failure 关键点插值的最大间隔*/#define SIFT_MAX_INTERP_STEPS 5/* default number of bins in histogram for orientation assignment 主方向分配的直方图数量*/#define SIFT_ORI_HIST_BINS 36/* determines gaussian sigma for orientation assignment */特征点主方向分配时高斯平滑为1.5倍特征点所在的尺度#define SIFT_ORI_SIG_FCTR 1.5/* determines the radius of the region used in orientation assignment */主方向分配时使用的区域半径#define SIFT_ORI_RADIUS 3.0 * SIFT_ORI_SIG_FCTR/* number of passes of orientation histogram smoothing 主方向直方图平滑*/#define SIFT_ORI_SMOOTH_PASSES 2/* orientation magnitude relative to max that results in new feature 方向与最大值的%80比较*/初始值0.8 阈值#define SIFT_ORI_PEAK_RATIO 0.8/* determines the size of a single descriptor orientation histogram 主方向直方图描述符尺寸*/初始值3.0#define SIFT_DESCR_SCL_FCTR 3.0/* threshold on magnitude of elements of descriptor vector 描述符向量元素幅度的阈值*/初始值是0.2 128维SIFT描述子中大于0.2的维度量截取为0.2#define SIFT_DESCR_MAG_THR 0.2/* factor used to convert floating-point descriptor to unsigned char 数据类型转换 */浮点数转换为无符号字符的因子值#define SIFT_INT_DESCR_FCTR 512.0/* returns a features detection data 特征提取函数,img代表需要进行特征提取的图像，feat用来存储提取的特征向量，函数返回值为特征点数，如果失败返回-1 */#define feat_detection_data(f) ( (struct detection_data*)(f-feature_data) )特征点检测函数分析int sift_features( IplImage* img, struct feature* feat )return _sift_features( img, feat, SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR,SIFT_CURV_THR, SIFT_IMG_DBL, SIFT_DESCR_WIDTH,SIFT_DESCR_HIST_BINS );/*Finds SIFT features in an image using user-specified parameter values. Alldetected features are stored in the array pointed to by a feat.param img the image in which to detect featuresparam feat a pointer to an array in which to store detected featuresparam intvls the number of intervals sampled per octave of scale spaceparam sigma the amount of Gaussian smoothing applied to each image levelbefore building the scale space representation for an octaveparam cont_thr a threshold on the value of the scale space functionf$left|D(hatx)right|f$, where f$hatxf$ is a vector specifyingfeature location and scale, used to reject unstable features; assumespixel values in the range 0, 1param curv_thr threshold on a features ratio of principle curvatures主曲率used to reject features that are too edge-likeparam img_dbl should be 1 if image doubling prior to scale spaceconstruction is desired or 0 if notparam descr_width the width, f$nf$, of the f$n times nf$ array oforientation histograms used to compute a features descriptorparam descr_hist_bins the number of orientations in each of thehistograms in the array used to compute a features descriptorreturn Returns the number of keypoints stored in a feat or -1 on failuresee sift_keypoints()intvls: 每个尺度空间的采样间隔数，默认值为3.sigma: 高斯平滑的数量，默认值1.6.contr_thr:判定特征点是否稳定，取值（0，1），默认为0.04，这个值越大，被剔除的特征点就越多。curv_thr:判定特征点是否边缘点，默认为6.img_dbl:在建立尺度空间前如果图像被放大了1倍则取值为1，否则为0.descr_width:计算特征描述符时邻域子块的宽度，默认为4.descr_hist_bins:计算特征描述符时将特征点邻域进行投影的方向数，默认为8，分别是0，45，90，135，180，215，270，315共8个方向。/intvls: 每个尺度空间的采样间隔数，默认值为3.sigma: 高斯平滑的数量，默认值1.6.contr_thr:判定特征点是否稳定，取值（0，1），默认为0.04，这个值越大，被剔除的特征点就越多。curv_thr:判定特征点是否边缘点，默认为6.img_dbl:在建立尺度空间前如果图像被放大了1倍则取值为1，否则为0.descr_width:计算特征描述符时邻域子块的宽度，默认为4.descr_hist_bins:计算特征描述符时将特征点邻域进行投影的方向数，默认为8，分别是0，45，90，135/int _sift_features( IplImage* img, struct feature* feat, int intvls,double sigma, double contr_thr, int curv_thr,int img_dbl, int descr_width, int descr_hist_bins )IplImage* init_img;IplImage* gauss_pyr, * dog_pyr;CvMemStorage* storage;CvSeq* features; /CvSeq定义非固定元素的序列int octvs, i, n = 0;/* check arguments 检查图像值是否合理*/if( ! img )fatal_error( NULL pointer error, %s, line %d, _FILE_, _LINE_ );if( ! feat )fatal_error( NULL pointer error, %s, line %d, _FILE_, _LINE_ );/* build scale space pyramid; smallest dimension of top level is 4 pixels 建立金字塔尺度空间*/init_img = create_init_img( img, img_dbl, sigma ); /将图像转为8位灰度图像，然后再高斯平滑octvs = log( MIN( init_img-width, init_img-height ) ) / log(2) - 2;gauss_pyr = build_gauss_pyr( init_img, octvs, intvls, sigma ); /构建高斯尺度空间dog_pyr = build_dog_pyr( gauss_pyr, octvs, intvls ); /构建DOG尺度空间storage = cvCreateMemStorage( 0 );features = scale_space_extrema( dog_pyr, octvs, intvls, contr_thr,curv_thr, storage ); /返回特征点尺度，位置calc_feature_scales( features, sigma, intvls ); /在每一组中计算尺度值if( img_dbl ) /如果图像放大了一倍adjust_for_img_dbl( features ); /在构造尺度空间之前将图像的坐标及尺度扩大一倍calc_feature_oris( features, gauss_pyr ); /计算关键点主方向compute_descriptors( features, gauss_pyr, descr_width, descr_hist_bins );/* sort features by decreasing scale and move from CvSeq to array 按照降序尺度为特征点排序*/cvSeqSort( features, (CvCmpFunc)feature_cmp, NULL );n = features-total;*feat = calloc( n, sizeof(struct feature) );*feat = cvCvtSeqToArray( features, *feat, CV_WHOLE_SEQ );/复制序列中的元素到一个连续的内存块中for( i = 0; i width, init_img-height ) ) / log(2) - 2;gauss_pyr = build_gauss_pyr( init_img, octvs, intvls, sigma );dog_pyr = build_dog_pyr( gauss_pyr, octvs, intvls );storage = cvCreateMemStorage( 0 );features = scale_space_extrema( dog_pyr, octvs, intvls, contr_thr,curv_thr, storage );calc_feature