3D Image Reconstruction Of Cattle Face

As one of the five pastoral regions in China,Inner Mongolia Autonomous Region is an important base of animal husbandry.It has become a development strategic goal to promote the comprehensive and deep integration of the new generation of information technology and animal husbandry.Among them,the individual identification of cattle is of great significance to the intelligent farming and insurance industry.In order to manage individual cattle more accurately,three-dimensional identification method can provide richer spatial information than two-dimensional identification method.In 3D recognition,the 3D reconstruction of cattle face is very important,which has certain application value for the intelligent management of animal husbandry.The three-dimensional image reconstruction of cattle face is studied in this paper,including the following aspects:1.A multi-view cattle face image acquisition method was designed in a complex environment.Videos of cattle face from multiple perspectives were conducted,and images of cattle face from different angles at the same time were selected to make the non-rigid cattle face approximately rigid.Cattle face videos from four different angles of were collected by this method,which provided a lot of original data for the establishment of subsequent image dataset.2.A dataset of cattle face images was established in this paper.The key frames were extracted from the cattle face videos from different angles.The improved clustering key frame extraction method was used to obtain several groups of images with complete cattle face,which effectively reduced the redundancy of key frames.Then the quality of the images was evaluated and a group of images with the best effect was selected to establish the cattle face dataset.The establishment of cattle face image dataset provides the feasibility for the 3D reconstruction of cattle face.3.The 3D reconstruction algorithm of cattle face was studied in this paper.The improved SIFT and ORB fusion method was used to extract and match feature points from cattle face dataset.The matching point pairs of the two kinds of feature points complemented each other,and the matching logarithm was increased which compared with that of the single kind of feature points.Then the camera self-calibration method was used to calibrate the camera.The sparse point cloud was reconstructed by the method of Structure From Motion.C/PMVS was used to reconstruct the dense point cloud.Finally,the surface reconstruction and texture mapping were carried out to realize 3D image reconstruction of cattle face.The 3D reconstruction model of the cattle face completely rebuilt the actual cattle face,and the reconstruction effect was clear with strong visualization.