資源簡介
超像素圖像分割是對圖像進行分析和理解的關鍵步驟，在圖像處理領域具有重要的研究和應用價值。本題目需要實現一種基于SLIC超像素圖像分割的算法。首先利用SLIC對圖像進行超像素分割處理，把原圖像分割成大小相似、形狀規則的超像素，以超像素中心點的五維特征值作為原始數據點進行聚類，確定多體素數目和分割邊界。SLIC具有以下優勢：1）生成的超像素如同細胞一般緊湊整齊，鄰域特征比較容易表達；2）不僅可以分割彩色圖，也可以兼容分割灰度圖；3）需要設置的參數非常少，默認情況下只需要設置一個預分割的超像素的數量。相比其他的超像素分割方法，SLIC在運行速度、生成超像素的緊湊度、輪廓保持方面都比較理想。
代碼片段和文件信息
#include?“slic.h“
/*
?*?Constructor.?Nothing?is?done?here.
?*/
Slic::Slic（）?{
}
/*
?*?Destructor.?Clear?any?present?data.
?*/
Slic::~Slic（）?{
????clear_data（）;
}
/*
?*?Clear?the?data?as?saved?by?the?algorithm.
?*
?*?Input?:?-
?*?Output:?-
?*/
void?Slic::clear_data（）?{
????clusters.clear（）;
????distances.clear（）;
????centers.clear（）;
????center_counts.clear（）;
}
/*
?*?Initialize?the?cluster?centers?and?initial?values?of?the?pixel-wise?cluster
?*?assignment?and?distance?values.
?*
?*?Input?:?The?image?（IplImage*）.
?*?Output:?-
?*/
void?Slic::init_data（IplImage?*image）?{
????/*?Initialize?the?cluster?and?distance?matrices.?*/
????for?（int?i?=?0;?i?width;?i++）?{?
????????vector?cr;
????????vector?dr;
????????for?（int?j?=?0;?j?height;?j++）?{
????????????cr.push_back（-1）;
????????????dr.push_back（FLT_MAX）;
????????}
????????clusters.push_back（cr）;
????????distances.push_back（dr）;
????}
????
????/*?Initialize?the?centers?and?counters.?*/
????for?（int?i?=?step;?i?width?-?step/2;?i?+=?step）?{
????????for?（int?j?=?step;?j?height?-?step/2;?j?+=?step）?{
????????????vector?center;
????????????/*?Find?the?local?minimum?（gradient-wise）.?*/
????????????CvPoint?nc?=?find_local_minimum（image?cvPoint（ij））;
????????????CvScalar?colour?=?cvGet2D（image?nc.y?nc.x）;
????????????
????????????/*?Generate?the?center?vector.?*/
????????????center.push_back（colour.val[0]）;
????????????center.push_back（colour.val[1]）;
????????????center.push_back（colour.val[2]）;
????????????center.push_back（nc.x）;
????????????center.push_back（nc.y）;
????????????
????????????/*?Append?to?vector?of?centers.?*/
????????????centers.push_back（center）;
????????????center_counts.push_back（0）;
????????}
????}
}
/*
?*?Compute?the?distance?between?a?cluster?center?and?an?individual?pixel.
?*
?*?Input?:?The?cluster?index?（int）?the?pixel?（CvPoint）?and?the?Lab?values?of
?*?????????the?pixel?（CvScalar）.
?*?Output:?The?distance?（double）.
?*/
double?Slic::compute_dist（int?ci?CvPoint?pixel?CvScalar?colour）?{
????double?dc?=?sqrt（pow（centers[ci][0]?-?colour.val[0]?2）?+?pow（centers[ci][1]
????????????-?colour.val[1]?2）?+?pow（centers[ci][2]?-?colour.val[2]?2））;
????double?ds?=?sqrt（pow（centers[ci][3]?-?pixel.x?2）?+?pow（centers[ci][4]?-?pixel.y?2））;
????
????return?sqrt（pow（dc?/?nc?2）?+?pow（ds?/?ns?2））;
????
????//double?w?=?1.0?/?（pow（ns?/?nc?2））;
????//return?sqrt（dc）?+?sqrt（ds?*?w）;
}
/*
?*?Find?a?local?gradient?minimum?of?a?pixel?in?a?3x3?neighbourhood.?This
?*?method?is?called?upon?initialization?of?the?cluster?centers.
?*
?*?Input?:?The?image?（IplImage*）?and?the?pixel?center?（CvPoint）.
?*?Output:?The?local?gradient?minimum?（CvPoint）.
?*/
CvPoint?Slic::find_local_minimum（IplImage?*image?CvPoint?center）?{
????double?min_grad?=?FLT_MAX;
????CvPoint?loc_min?=?cvPoint（center.x?center.y）;
????
????for?（int?i?=?center.x-1;?i?????????for?（int?j?=?center.y-1;?j?????????????CvScalar?c1?=?cvGet2D ?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????1314594??2018-11-07?09:36??SLIC-Superpixels-master\2012-SLIC_superpixels?compared?to?state-of-the-art?superpixel?methods-TPAMI-Achanta12.pdf
????.......????164271??2013-05-13?04:32??SLIC-Superpixels-master\dog.png
????.......?????57263??2013-05-13?04:32??SLIC-Superpixels-master\dog_segmentation.png
????.......???????611??2013-05-13?04:32??SLIC-Superpixels-master\README.md
????.......?????12759??2013-05-13?04:32??SLIC-Superpixels-master\slic.cpp
????.......??????2515??2013-05-13?04:32??SLIC-Superpixels-master\slic.h
????.......??????1217??2013-05-13?04:32??SLIC-Superpixels-master\test_slic.cpp
?????目錄??????????0??2018-11-07?12:15??SLIC-Superpixels-master
-----------?---------??----------?-----??----
??????????????1553230????????????????????8
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