Convolutional neural network (CNN) algorithm is a very important branch of deep learning research, which has been widely applied in many fields and achieved excellent results, especially in computer vision, where convolutional neural network has made breakthroughs in image classification and object detection. Convolutional neural network architecture can realize more efficient network training through the final combination of different modules, and the convolutional neural network training does not need to actively extract image features and can directly carry out end-to-end training and prediction. At first, this paper analyzed some problems of the current image recognition and expounds the progress of convolution neural network in image recognition and then studied the traditional algorithm of target recognition, including traditional recognition algorithm framework of target, the target orientation, feature extraction, classifier classification, etc., and the traditional target recognition algorithm is compared with those of the target recognition algorithm of deep learning. On the basis of the above research, an improved model of CNN is proposed, which focuses on the structural design and network optimization of convolutional neural network and designs a more efficient convolutional neural network. Test experiments verify the effectiveness of the proposed model, which not only achieves lower error rate, but also greatly reduces the number of network parameters and has stronger learning ability.
Clouding computing and virtualization technology have brought immense effects to software organizations including software testing region. At the same time the method and related technology will be changed a lot. Some concepts, structures and characters are described in this paper. Software testing should be regarded as a service. Four typical patterns of cloud testing and the relations among them are showed in the paper. Combined with the virtualization technology, the frame of TaaS (Testing as a Service) is defined too. In the meantime, cloud testing and traditional software testing is compared with each other. At last, the main research contents about software testing in virtualization environment are summarized.
This paper describes the techniques of the processing of automatic segmention vertebra from MRI image. The final choice is use regional growth to segment the vertebra automatically. When processing the image with algorithm of regional growth, use the Contour-tracing method to deal with the region of spine in order to extract contour. Then use mathematical morphology on the experimental results as post-processing.
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