基于区域梯度压缩的少纹理目标候选框提取算法

Texture-less Object Candidate Box Extraction Algorithm Based on Region Orientation Compression

针对某些应用中目标表面纹理较少,目标检测困难的问题,提出了一种基于区域梯度压缩的少纹理目标候选框提取算法。该算法是对模板匹配算法OCM的改进。算法对局部区域梯度方向进行压缩,保持了较低的计算复杂度,并且提出了新的梯度方向压缩方法与相似度衡量方法。实验证明,该算法相较于OCM算法,在产生接近数量候选框的情况下,召回率提高了6.5%;在召回率接近时,产生的候选框数量减少了41.9%。

In some applications,the objects to be detected don’t have enough surface texture informations,which causes a great challenge to the accurate object detection.Aiming at above issue,this paper proposes a candidate box extraction algorithm for texture-less object based on region orientation compression.This algorithm is an improvement on the template matching algorithm OCM.The algorithm compresses the local area edge orientation and maintains a low computational complexity,the algorithm proposes new orientation compressing method and similarity measurement method.Experiments results show that compared with the OCM,the algorithm can improve the recall rate by 6.5%.When the recall rate is close,the algorithm reduce the amount of candidate boxes by 41.9%.