兵工自动化
兵工自動化
병공자동화
ORDNANCE INDUSTRY AUTOMATION
2013年
8期
84-87
,共4页
仿射变换%仿射不变性%不变特征提取
倣射變換%倣射不變性%不變特徵提取
방사변환%방사불변성%불변특정제취
affine transform%affine invariant feature%invariant feature extraction
从不同角度、距离获取的图像中提取不受视点因素影响的仿射不变特征,是图像目标识别、图像几何校正、景象匹配、图像检索等领域的共性问题。从仿射几何的角度出发,在对仿射变换、仿射不变性进行研究的基础上,利用仿射几何的不变性提取仿射不变特征量。针对同底面积比基元进行目标识别存在的问题,引入改进局部不变量,并提出3种基元特征提取算法,以排除锯齿点干扰进行角点提取。Matlab仿真分析过程中,对图像进行基元特征提取,采用改进局部不变量进行计算、比较及识别,能够有效建立图像特征值比较模型。实验结果表明:该局部不变量能较好地识别相同目标,用于不同目标间的分类识别时能取得好的分类效果。
從不同角度、距離穫取的圖像中提取不受視點因素影響的倣射不變特徵,是圖像目標識彆、圖像幾何校正、景象匹配、圖像檢索等領域的共性問題。從倣射幾何的角度齣髮,在對倣射變換、倣射不變性進行研究的基礎上,利用倣射幾何的不變性提取倣射不變特徵量。針對同底麵積比基元進行目標識彆存在的問題,引入改進跼部不變量,併提齣3種基元特徵提取算法,以排除鋸齒點榦擾進行角點提取。Matlab倣真分析過程中,對圖像進行基元特徵提取,採用改進跼部不變量進行計算、比較及識彆,能夠有效建立圖像特徵值比較模型。實驗結果錶明:該跼部不變量能較好地識彆相同目標,用于不同目標間的分類識彆時能取得好的分類效果。
종불동각도、거리획취적도상중제취불수시점인소영향적방사불변특정,시도상목표식별、도상궤하교정、경상필배、도상검색등영역적공성문제。종방사궤하적각도출발,재대방사변환、방사불변성진행연구적기출상,이용방사궤하적불변성제취방사불변특정량。침대동저면적비기원진행목표식별존재적문제,인입개진국부불변량,병제출3충기원특정제취산법,이배제거치점간우진행각점제취。Matlab방진분석과정중,대도상진행기원특정제취,채용개진국부불변량진행계산、비교급식별,능구유효건립도상특정치비교모형。실험결과표명:해국부불변량능교호지식별상동목표,용우불동목표간적분류식별시능취득호적분류효과。
It is common problems in image target recognition, image geometric correction, scene matching, image retrieval and other areas that extracted from viewpoint factors affine invariant feature from different angles and distance of image acquisition. Based on the affine transformation, affine invariance study use of affine geometric invariability extraction of affine invariant feature quantity starting from the point of view of affine geometry. According to the same base area ratio primitives for target recognition problems into improving local invariant, and puts forward three kinds of primitive feature extraction algorithm to eliminate sawtooth point interference with angle point extraction. Do image elementary feature extraction, using improved local invariant calculation, comparison and recognition in Matlab simulation analysis process. It can effectively establish image characteristic value comparison model. The experimental results show that the local invariant can more accurately recognize the same goal, and it can obtain better classification effect when used for different target classification recognition between.