森林工程
森林工程
삼림공정
FOREST ENGINEERING
2012年
3期
14-17
,共4页
实木地板%缺陷分割%区域生长%形态学重构
實木地闆%缺陷分割%區域生長%形態學重構
실목지판%결함분할%구역생장%형태학중구
wood floor%defects segmentation%regional growth%morphological reconstruction
针对传统区域生长方法中,由于噪声种子存在及种子点单步邻域搜索所导致的分割时间长、检测精度低的问题,提出基于形态学重构的实木地板在线缺陷分割方法。方法首先定义不同阈值下的两幅模版图像,其中低阈值图像用于种子优化,高阈值模版用作种子膨胀生长;通过定义腐蚀终止准则,完成低阈值图像下的缺陷骨架提取;运用“去毛刺”操作,最终实现缺陷骨架内的种子点优选;然后,运用测地膨胀,结合高阈值模版,完成板材缺陷区域的快速生长;最后,应用“孔洞填充”、“去毛刺”优化边缘,实现缺陷目标的提取。实验分别在像素512‘512、256’256和128’128下进行,通过与传统区域生长方法的比较,表明方法实现了缺陷区域的准确分割,分割速度与精度能够满足地板在线分选要求。
針對傳統區域生長方法中,由于譟聲種子存在及種子點單步鄰域搜索所導緻的分割時間長、檢測精度低的問題,提齣基于形態學重構的實木地闆在線缺陷分割方法。方法首先定義不同閾值下的兩幅模版圖像,其中低閾值圖像用于種子優化,高閾值模版用作種子膨脹生長;通過定義腐蝕終止準則,完成低閾值圖像下的缺陷骨架提取;運用“去毛刺”操作,最終實現缺陷骨架內的種子點優選;然後,運用測地膨脹,結閤高閾值模版,完成闆材缺陷區域的快速生長;最後,應用“孔洞填充”、“去毛刺”優化邊緣,實現缺陷目標的提取。實驗分彆在像素512‘512、256’256和128’128下進行,通過與傳統區域生長方法的比較,錶明方法實現瞭缺陷區域的準確分割,分割速度與精度能夠滿足地闆在線分選要求。
침대전통구역생장방법중,유우조성충자존재급충자점단보린역수색소도치적분할시간장、검측정도저적문제,제출기우형태학중구적실목지판재선결함분할방법。방법수선정의불동역치하적량폭모판도상,기중저역치도상용우충자우화,고역치모판용작충자팽창생장;통과정의부식종지준칙,완성저역치도상하적결함골가제취;운용“거모자”조작,최종실현결함골가내적충자점우선;연후,운용측지팽창,결합고역치모판,완성판재결함구역적쾌속생장;최후,응용“공동전충”、“거모자”우화변연,실현결함목표적제취。실험분별재상소512‘512、256’256화128’128하진행,통과여전통구역생장방법적비교,표명방법실현료결함구역적준학분할,분할속도여정도능구만족지판재선분선요구。
Due to the problems of noise existed, time consuming, and lower detection precision in region growth, this paper proposed a novel method using morphological reconstruction technique to conduct on-line defects detection for wood floors. Firstly, this method used two gray thresholds to get two gray images. The lower threshold image was used for seeds optimization, while the higher threshold image was used for seed expansion growth. Skeleton extraction of defects for the lower threshold image was accom- plished by defining corrosion stop criteria. The burr operation was then employed on the lower threshold image to achieve seeds optimization. Secondly, the expansion, holes filling and burr optimization were exerted separately on the high threshold image to accomplish rapid growth of lumber defect area defects extraction. The experiments were carried out in the resolutions of 512 × 512, 256 × 256, and 128× 128, respectively. By contrasting with the traditional region growth method, the tests showed that the proposed method could guarantee the accuracy and the speed for wood floor on-line defects detection.