计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2014年
4期
219-222
,共4页
目标检测%ISM形状模型%目标描述表%局部特征%目标分割
目標檢測%ISM形狀模型%目標描述錶%跼部特徵%目標分割
목표검측%ISM형상모형%목표묘술표%국부특정%목표분할
Target detection%Implicit shape model%Codebook%Local feature%Target segmentation
提出一种能适应不同方向目标检测，可以有效减小训练样本集数量的目标检测算法。算法包含两部分：ISM（Implicit Shape Model）形状模型生成和目标检测。ISM形状模型中包含目标描述表和 ISM形状模型两部分。目标检测时将图像中的局部特征与目标描述表进行匹配，同时结合 ISM形状模型生成投票空间。通过在投票空间中搜索局部极大值，并采用自顶向下的分割和MDL 算法来剔除虚假目标，获取图像中的目标检测结果。编程实现了该算法，并用汽车、摩托车、行人等典型目标进行了目标检测试验。试验结果证明该算法对复杂背景下目标检测有较好的性能。用含不同角度目标的图像与原算法进行了对比实验，实验结果表明提高了算法对目标角度变化的适应能力。
提齣一種能適應不同方嚮目標檢測，可以有效減小訓練樣本集數量的目標檢測算法。算法包含兩部分：ISM（Implicit Shape Model）形狀模型生成和目標檢測。ISM形狀模型中包含目標描述錶和 ISM形狀模型兩部分。目標檢測時將圖像中的跼部特徵與目標描述錶進行匹配，同時結閤 ISM形狀模型生成投票空間。通過在投票空間中搜索跼部極大值，併採用自頂嚮下的分割和MDL 算法來剔除虛假目標，穫取圖像中的目標檢測結果。編程實現瞭該算法，併用汽車、摩託車、行人等典型目標進行瞭目標檢測試驗。試驗結果證明該算法對複雜揹景下目標檢測有較好的性能。用含不同角度目標的圖像與原算法進行瞭對比實驗，實驗結果錶明提高瞭算法對目標角度變化的適應能力。
제출일충능괄응불동방향목표검측，가이유효감소훈련양본집수량적목표검측산법。산법포함량부분：ISM（Implicit Shape Model）형상모형생성화목표검측。ISM형상모형중포함목표묘술표화 ISM형상모형량부분。목표검측시장도상중적국부특정여목표묘술표진행필배，동시결합 ISM형상모형생성투표공간。통과재투표공간중수색국부겁대치，병채용자정향하적분할화MDL 산법래척제허가목표，획취도상중적목표검측결과。편정실현료해산법，병용기차、마탁차、행인등전형목표진행료목표검측시험。시험결과증명해산법대복잡배경하목표검측유교호적성능。용함불동각도목표적도상여원산법진행료대비실험，실험결과표명제고료산법대목표각도변화적괄응능력。
We present a target detection algorithm which is able to adapt to target detection with different directions and effectively cut down the number of training sample sets.The algorithm consists of two parts:implicit shape model (ISM)generation and target detection. The implicit shape model contains target codebook and implicit shape model itself.In target detection procedure,local characteristics in test image are matched with target codebook,at the same time the implicit shape model is combined to generate voting space.By searching local maximum values in voting space,and applying the top-down segmentation and MDL algorithm to weed out the false targets,the target detection results are acquired from test image.We implement the algorithm proposed in the paper through programming,and carry out the target detection test by using typical targets of cars,motorcycles,pedestrians,etc.Test results indicated that this algorithm has good performance in detecting the targets with complex background.Contrast experiments for the images with different target angles and for the original algorithm are carried out,the experimental result indicates that the algorithm improves the ability in adapting to the target angle changes.
