Study On The Key Technology In Conformation Appraisal Of Dairy Cow Based On Machine Vision

Modern appraisal technology of dairy cow is mainly processed from two aspects of capability and conformation,and the latter is more important. It can meet the practical requirement using machine vision technology to the conformation appraisal system of dairy cow, and the virtual reconstruction cow builds sound foundation for virtual anatomizing etc. The new and improved algorithm and model on the study are proposed, which widen the machine vision theory fields, and provide the reference for the correlative fields.Overseas and national machine vision theory and its application on the dairy cow conformation appraisal are analyzed in the dissertation. The theory is expatiated about imaging camera model, image pretreatment, detecting feature points, matching feature points, camera calibration etc.A multi-scale Harris algorithm for feature point detecting based on image sub-block is proposed. The Harris algorithm is studied in order to represent the image feature using a certain number feature points extracted from image. The threshold is selected experientially in detecting feature point using Harris algorithm, resulting in repetitious attempts for detecting a certain number feature points. In addition, selecting simplex threshold induces the clustered feature points in part fields and uneven distributing, so the image feature can not be well represented. In the new algorithm, the threshold is selected automatically to overcome the shortcoming of the original algorithm, which can meet the requirement of feature point's quantity. The feature points distribute proportionally, so the global image feature is commendably presented. Experiments demonstrate the good performance of the proposed algorithm in controlling fields, controlling quantity, and alleviating the data redundancy and feature points clustered.An improved SIFT algorithm for feature points matching is proposed. The matching algorithm of SIFT feature point descriptor based on Euclidean minimum distance is studied in order to improve the algorithm efficiency of image feature points matching. A large number of feature points are detected with SIFT algorithm, and every point descriptor has 128 dimensions, and distances of every feature point in first image and all the feature points in the second image need to be calculated and then sorted to find the extremum, so the algorithm efficiency is low. According to optics imaging theory and the binocular vision theory, the area of feature points matched in the second image can be reduced from row and column directions based on the feature point in the first image. The improved algorithm accelerates the speed of the algorithm 2.7 times with the same precision yielded.An automatic camera calibration method based on conventional camera calibration is proposed. The camera calibration method is studied in order to improve the efficiency and automation grade of camera calibration. The conventional camera calibration needs marking the marker point by handwork with high workload, low efficiency and unwarranted precision. The new method uses self-made calibration template and detects marker point automatically instead of handwork, which can detect and sort the feature points automatically. Experiments demonstrate that this new method has the same calibration precision as original one, but has steady reliability, strong practicability, and predigests the camera calibration course.In conclusion, the 3D reconstruction key technology including the feature points detecting and matching and camera calibration is applied for conformation appraisal of dairy cow in order to obtain the 3D coordinate data and finally obtain the parameters of dairy cow conformation.