农业工程学报
農業工程學報
농업공정학보
2013年
1期
158-163
,共6页
机器视觉%图像分割%特征提取%分水岭算法%Zernike 矩
機器視覺%圖像分割%特徵提取%分水嶺算法%Zernike 矩
궤기시각%도상분할%특정제취%분수령산법%Zernike 구
computer vision%image segmentation%feature extraction%watersheds%Zernike moments
果实轮廓特征的测量提取是了解水果等农作物发育过程中内部生理生态变化的重要手段.该文提出了一种基于 Zernike 矩边缘检测的分水岭算法,并将该算法应用于葡萄果粒的轮廓特征提取.与传统的标记驱动分水岭算法相比,该算法利用 Zernike 矩边缘检测避免了标记对于轮廓的破坏,较好的保护了目标轮廓,从而减少了后续处理,提高了检测效率.最后,将用该算法所得到的轮廓和用传统的标记驱动分水岭算法所得到的轮廓进行比较,验证了该算法的可行性.该算法具有较高的检测效率,相较传统算法提高约6.9%左右,能够满足连续提取葡萄果粒的轮廓特征的要求.该方法可用于实时检测葡萄果粒的几何特征的变化.
果實輪廓特徵的測量提取是瞭解水果等農作物髮育過程中內部生理生態變化的重要手段.該文提齣瞭一種基于 Zernike 矩邊緣檢測的分水嶺算法,併將該算法應用于葡萄果粒的輪廓特徵提取.與傳統的標記驅動分水嶺算法相比,該算法利用 Zernike 矩邊緣檢測避免瞭標記對于輪廓的破壞,較好的保護瞭目標輪廓,從而減少瞭後續處理,提高瞭檢測效率.最後,將用該算法所得到的輪廓和用傳統的標記驅動分水嶺算法所得到的輪廓進行比較,驗證瞭該算法的可行性.該算法具有較高的檢測效率,相較傳統算法提高約6.9%左右,能夠滿足連續提取葡萄果粒的輪廓特徵的要求.該方法可用于實時檢測葡萄果粒的幾何特徵的變化.
과실륜곽특정적측량제취시료해수과등농작물발육과정중내부생리생태변화적중요수단.해문제출료일충기우 Zernike 구변연검측적분수령산법,병장해산법응용우포도과립적륜곽특정제취.여전통적표기구동분수령산법상비,해산법이용 Zernike 구변연검측피면료표기대우륜곽적파배,교호적보호료목표륜곽,종이감소료후속처리,제고료검측효솔.최후,장용해산법소득도적륜곽화용전통적표기구동분수령산법소득도적륜곽진행비교,험증료해산법적가행성.해산법구유교고적검측효솔,상교전통산법제고약6.9%좌우,능구만족련속제취포도과립적륜곽특정적요구.해방법가용우실시검측포도과립적궤하특정적변화.
Image segmentation and the extraction of target contour are key technologies to realize continuous non-destructive detection of crop geometric features based on machine vision, which can help understanding the development of internal physiological and ecological changes in fruits and other crops during growth. In this paper, a new watershed algorithm based on Zernike-moment edge detection was proposed to extract grape fruit contour features. The algorithm first acquired a rough outline of the target based on watershed algorithm marked by Zernike-moment. Then through contour refinement template algorithm, a single-pixel contour was obtained. In the meanwhile, false edges were removed by template algorithm. Finally, contour tracking was conducted to get the real contour of the target. Compared with traditional marker driven watershed algorithm, the proposed algorithm can avoid contour destruction caused by tag via Zernike-moment edge detection. In this way, the target contour is well preserved, so that post-processing can be reduced and detection efficiency is improved. At last, by comparing the contour obtained by the proposed algorithm with that by traditional marker driven watershed algorithm, the feasibility of the algorithm was demonstrated. Because of high performance of detection, the algorithm is able to meet the requirements of continuous contour feature extraction of grape fruit. The method can be applied to the real-time detection of grape fruit geometrical feature changes.