This paper presents novel methods to predict the surface area and volume of the ham through a camera. This implies that the conventional weight measurement to obtain the object's volume can be neglected and hence it is economically effective. Both of the measurements are obtained in following two ways: manually and automatically. The former is assumed as the true or exact measurement and the latter is through a computer vision technique together with some geometrical analysis that includes mathematical derived functions. For the automatic implementation, most of the existing approaches extract the features of the food material based on handcrafted features and to the best of our knowledge this is the first attempt to estimate the surface area and volume on ham with deep learning features. We address the estimation task with a Mask Region-based CNN (Mask R-CNN) approach, which well performs the ham detection and semantic segmentation from a video. The experimental results demonstrate that the algorithm proposed is robust as promising surface area and volume estimation are obtained for two angles of the ellipsoidal ham (i.e., horizontal and vertical positions). Specifically, in the vertical ham point of view, it achieves an overall accuracy of up to 95% whereas the horizontal ham reaches 80% of accuracy.

No abstract

IPv6 backbone Network has been deployed in 2004 in China, such as CNGI, but the main IPv6 network equipment was provided by foreign supplies. Although we are facing the danger of IPv4 addresses exhaustion, but IPv6 Customer Premise Network was not deployed widely, and the applications based on IPv6 Network were very few. Besides the international influence, the main factors are that IPv6 sample applications with commercial value were few and there are not stimulus-encouraged policies of IPv6 CPN deployment. It is lack of cooperation and integration between the profession and the needs of the industry, and lack of participation and competition for the relevant international standards because there is lack of consideration of long-term interests and enterprises concern about short-term benefits. It has affected that we grabbed the commanding height of the next generation network in a certain degree, and there are some security risks. This paper briefly introduces the construction of IPv6 network with safe, reliable and cost-effective, some suggestions are shown for the accelerating deployment and sample applications of IPv6 networks. Hopefully it gives some reference to accelerate the construction of IPv6 network and enhance network security, especially the IPv6 Customer Premise Network.

Video surveillance has various applications in various fields and industries. However, the rapid development of video processing technology has made video surveillance information susceptible to multiple malicious attacks. At present, the state-of-the-art methods, including the latest deep learning techniques, cannot get satisfactory results when addressing video surveillance object forgery detection (VSOFD) due to the following limitations: (i) lack of VSOFD-specific features for effective processing and (ii) lack of effective deep network architecture designed explicitly for VSOFD. This paper proposes a new detection scheme to alleviate these limitations. The proposed approach first extracted VSOFD-specific features via residual-based steganalysis feature (RSF) from the spatial-temporal-frequent domain. Key clues of video frames can be more effectively learned from RSF, instead of raw frame images. Then, the RSF feature is used to form the residual-based steganography feature vector group (RSFVG), which serves as the input of our following network. Finally, a new VSOFD-specific deep network architecture called parallel-DenseNet-concatenated-LSTM (PDCL) network is designed, which includes two improved CNN and RNN modules. The improved CNN module fuses and processes the coarse-to-fine feature extraction and simultaneously preserves the frame independence in video frames. The improved RNN module learns the correlation features between the adjacent frames to identify forgery frames. Experimental results show that the proposed scheme using the PDCL network with RSF can achieve high performance in test error, precision, recall, and F1 scores in our newly constructed dataset (SYSU-OBJFORG + newly generated forgery video clips). Compared to existing SOTA methods, our framework achieves the best F1 score of 90.33%, which is greatly improved by nearly 8%.