In order to overcome the puzzle that in the complex illumination condition, the traditional LBP image feature recognition algorithm neglects the contrast between local areas and abandons some important texture feature information, which makes it difficult to eliminate the nonlinear distortion caused by intense illumination changes, this paper proposed an improved image feature recognition algorithm. The algorithm first normalizes the face image, and then projects the pixel contrast value into a certain interval with the help of the ALBP algorithm, due to that the image after processing has good illumination invariance, the image features can be recognized better by using this method. The results of comparative experiment demonstrated that the recognition rate of ALBP algorithm is 84%, and that of LBP algorithm is 75%, the former is 9% higher than the latter. The research shows that the ALBP recognition method can eliminate the nonlinear distortion of the image well under complex illumination.
Aiming at the fact that the watershed algorithm appears very sensitive to various noises, such as being easy to cause over segmentation, pseudo-edge, edge discontinuity, and other phenomena, the paper explores an image edge extraction algorithm with improved watershed algorithm model. This improved model adopts the morphological method to compute the optimal threshold for maximizing the separation of foreground color and background color of the image and then applies this threshold to limit the path cost function of the algorithm model to narrow the search range and improve the execution speed of the algorithm so that the edge information of the image can be extracted more clearly. Comparative experimental studies show that the edges of the images extracted by the proposed algorithm are clearer. The results show that the improved algorithm model is more suitable for edge image extraction than other algorithms.
Aiming at the poor application of diagnosis of infant neuromotor diseases such as cerebral palsy based on computer vison, an OpenPose-based detection model for infant cerebral palsy by extracting features from infant spontaneous motion is proposed. Firstly, the deep separable convolution and the residual network structure is used to reduce the degradation of network operation and improve the detection accuracy of joint points. Then, it gives a loss function model based on Smooth L1 to improve the detection accuracy of infant motion features. Finally, motion characteristics are assigned to support vector machine to classify the infant cerebral palsy, which realizes pre-diagnosis with several features. Experiments conducted on dataset show the accuracy of this proposed method is 7~8% higher than others and reduce the amount of calculation to 1/9 of the original and accuracy of prediction can reach 91.89%. The results show that the detection model is feasible and effective for on-line pre-diagnosis of infant cerebral palsy.
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