140results about How to "Automatic extraction" patented technology

The invention discloses a picture classification system applied in communication devices, comprising a setting module, an extracting module, a characteristic identification module and a classifying module, wherein, the setting module is used for setting the class of a picture and the storing position of different classes of pictures and is used for defining the basic characteristics of the picture and setting characteristic correspondence ratio; the extracting module utilizes synchronize multimedia integration language to decode the received MMS and extracts pictures contained in the MMS; the characteristic identification module is used for indentifying the characteristic of the picture; an indentified characteristic is compared with the set basic characteristic, if the correspondence degree of indentified characteristic with one of the basic characteristics reaches a preset characteristic correspondence ratio, the class to which the basic characteristic belongs is confirmed to be the class to which the extracted picture belongs; the classifying module is used for storing the picture to the storing position corresponding to the class of picture according to the confirmed class. The invention also provides a picture classification method. The invention can automatically classify and store pictures received by the communication device.

The invention relates to a vanishing point based method for automatically extracting and classifying ground movement measurement image line segments. The method comprises the following steps of: firstly, analyzing the attribute of ground object space parallel characteristic lines of a target and calculating vanishing points of an image of the target on the basis of image sequences acquired by ground movement measurement; secondly, extracting characteristic line segments based on an image edge according to the Hough transform of a polar coordinate parameter area extremum and the continuous change of image greyscale; and then, classifying the extracted plane line segments by using vanishing points; and finally integrating the classified line segments according to a total least square method and conditions of the plane adjacency and overlapping degree of the line segments so as to extract image characteristic line segments which correspond to multiple ground object space parallel lines. The invention can improve the indoor processing capability of a movement measurement system and promote the research on automatic recognition, mapping, three-dimensional reconstruction and the like of multiple ground objects (such as buildings, roads and the like).

The invention provides a calligraphy font library automatic restoration method and system based on style migration. The calligraphy font library automatic restoration method comprises the steps that input fonts and standard style fonts are set; the input font image is input into a coding module, and potential feature information is obtained by the coding module; the conversion module converts thefeature information into feature information of standard style fonts; the decoding module performs processing to obtain a generated font image; the input font image and the generated font image are input into a discriminator, and the probability that the generated font image is a real standard style font is output; similarly, the input font image and the standard style font image are input into adiscriminator to obtain the probability that the standard style font image is a real standard style font; and finally loss functions of the generator and the discriminator are obtained according to the two probabilities. The optimizer adjusts the generator and the discriminator according to the loss functions until the loss functions of the generator and the discriminator converge to obtain a trained generator; a complete font library of standard style fonts can be obtained by adopting the trained generator.

The invention discloses a logistic transportation dispatching system. The logistic transportation dispatching system comprises order input equipment, a server, a data storage and a vehicle control device; the order input equipment is used for inputting information of cargoes needing to be transported, and the information comprises the cargo types, the cargo quantity, the delivery place and the destination; the server is connected with the order input equipment and used for calculating the transportation cost and obtaining an optimal transportation scheme; the data storage is connected with the server and used for storing logistic transportation information; and the vehicle control device is installed on a vehicle and used for positioning the vehicle and transmitting a transportation instruction. According to the logistic transportation dispatching system, the control device is installed on the vehicle and used for positioning the vehicle and transmitting the transportation instruction to the vehicle through the server, and therefore unified managing and dispatching of the vehicle are achieved.

A precedent database is generated by registering location indicating information that shows location of each of a plurality of precedent data. A knowledge database is generated by repeating processes of carrying out a search of this precedent database according to a specified search condition formula and registering knowledge names representing results of the search in correlation with the search condition formula. Component and failure names in the knowledge database are inserted as items of a generated FMEA sheet. Items of the FMEA sheet are specified and displayed such that corresponding text data can be referenced.

The invention provides an electrocardiosignal classifying method and system based on LRF-ELM and BLSTM. The electrocardiosignal classifying method includes the steps: acquiring electrocardiosignal data, preprocessing the electrocardiosignal data to obtain a dataset, and using the electrocardiosignal data in the dataset as input data of a neural network; using a LRF-ELM network as a feature extractor, learning spatial information in the electrocardiosignal data, and through three stacked random convolution and pooling processes, extracting feature data of different dimensions in the electrocardiosignal data; and after fusion, using the extracted feature data as input of a sequence learning stage, adopting a deep BLSTM network to carry out sequence learning, and finally outputting electrocardiosignal classifying results. According to the electrocardiosignal classifying method and system based on the LRF-ELM and the BLSTM, time information and spatial information of electrocardiosignals are taken into account at the same time, and therefore, not only can electrocardiosignal features be extracted efficiently and rapidly, but also good classification and identification properties are ensured.