本文選題：圖像分割 + 區(qū)域生長�。� 參考：《農(nóng)機化研究》2017年03期

【摘要】：提出了一套基于茄子圖像的空間位置信息和顏色因子相融合的區(qū)域生長分割算法。為保證茄子圖像分割最佳的顏色空間和顏色因子,提取了50幅不同光照條件下的茄子圖像的RGB顏色空間分量灰度圖和直方圖,比較了茄子果實、葉子、莖稈和空隙等的顏色特征,得出了G-B顏色因子對于茄子果實分割最為有利的結論。按照灰度級相同和空間8鄰域連通的原則確定種子區(qū)域,進而通過掃描整幅圖像進行初始分割。融合G-B顏色因子和空間信息對初始區(qū)域進行合并,直到分割形成的區(qū)域類間距離最大時停止生長。通過頂點鏈碼與離散格林技術提取出果實的最小外接矩形,求解果實的生長位姿,試驗表明:其分割效率均大于93%,平均用時為0.32 s,能夠滿足果蔬采摘機器人對視覺系統(tǒng)的要求。
[Abstract]:A regional growth segmentation algorithm based on spatial location information and color factor fusion based on Eggplant image was proposed. In order to ensure the best color space and color factor of eggplant image segmentation, the gray map and histogram of RGB color space component of eggplant images under 50 different illumination conditions were extracted, and the fruit, leaf and stem of eggplant were compared. The color characteristics of the stalk and the gap, and the conclusion that the G-B color factor is most favorable to the eggplant fruit segmentation is drawn. The seed region is determined according to the principle of the same gray level and the space 8 neighborhood connectivity, and then the initial segmentation is carried out by scanning the whole image. The initial region is merged with the G-B color factor and the spatial information, until the segmentation is divided. The minimal outer rectangle of fruit was extracted by vertex chain code and discrete Green technique, and the growth position of fruit was solved by the vertex chain code and discrete Green technique. The experimental results showed that the segmentation efficiency was more than 93% and the average time was 0.32 s, which could meet the requirements of the visual system of fruit and vegetable harvester.
【作者單位】： 山東科技大學機械電子工程學院;濰坊學院機電與車輛工程學院;
【基金】：國家自然科學基金項目(51505337) 山東省自然科學基金項目(ZR2014EEP013)
【分類號】：S225;TP391.41
，

本文編號：1918812