143results about How to "Efficient and accurate extraction" patented technology

The invention relates to a significant target detection method based on an FCN (fully convolutional network) and a CNN (convolutional neural network). The method comprises the following steps: firstly, extracting the deep semantic information by use of the FCN, wherein a fixed size of an input image is not needed, end-to-end prediction is performed, and the training complexity is reduced. Accuracy optimization is performed by adopting the CNN and extracting local features to obtain a rough detection result of the FCN. In the invention, the semantic information in an image can be extracted accurately and efficiently, and the improvement of significant target detection accuracy in a complex scene is promoted.

The invention discloses a stereoscopic vision comfort level evaluation method based on motion features inside an area of interest. The stereoscopic vision comfort level evaluation method based on the motion features inside the area of interest includes the steps: formulating a strategy of computing motion feature vectors inside a stereoscopic vision interest area, building a stereoscopic vision model which represents a non-liner relationship between stereoscopic video motion features and a stereoscopic vision comfort level through subjective testing, selecting a spatial domain mean value strategy, a minimum value integration strategy and a time domain mid-value strategy based on the motion feature vectors inside the stereoscopic vision interest area and a nonlinearity stereoscopic vision model, and formulating the stereoscopic vision comfort level evaluation method. Uniformity of between evaluation results provided and results of human-eye subjective stereoscopic comfort is good.

The invention relates to a quick blind source separating fetal electrocardioscanner and a detection method. The electrocardioscanner comprises a dual channel acquisition and analog-to-digital conversion module, an FPGA provided with an internal storage, a DSP and an ARM for separating a blind source, and an output device, wherein the dual channel acquisition and analog-to-digital conversion module is electrically connected with a common electrocardiogram lead cable and the FPGA through two data channels; the FPGA is electrically connected with the DSP through two data channels; the DSP is also sequentially electrically connected with the ARM and the output device ; and the DSP and the ARM are all externally connected with a program storage and a data storage. The electrocardioscanner adopts a high-speed acquisition and processing structure, detects fetal electrocardiosignals in aliasing electrocardiosignals in real time, adopts a blind source separation method to solve the problem that time domains and frequency domains of mother electrocardiosignals and the fetal electrocardiosignals are mutually overlapped and difficult to separate, and extracts the fetal electrocardiosignals efficiently and accurately for diagnosis.

The invention relates to a time-frequency-transformation-based blind extraction method of fetal electrocardiography, which is based on the characteristic that the time domain of an original signal is relatively sparse. The method comprises the following steps: extracting a plurality of paths of mother-fetal mixed electrocardiosignals from different parts of mother's abdomen, and preprocessing thecollected mixed signal, wherein the preprocessing comprises the steps of correcting the baseline shift of the signals, filtering off 50Hz power frequency interference, filtering off high-frequency noise interference such as myoelectricity and the like; selecting two paths of the mother-fetus mixed electrocardiosignals according to the maximal signal-to-noise ratio and separately searching mother-fetal electrocardio wave group positions of the two paths of mixed signals with an R-wave positioning technology to obtain time intervals in which the mother-fetus electrocardio in the two paths of mixed signals are relatively sparse, converting the obtained relatively sparse time intervals into a time frequency domain by using a fuzzy function, calculating signal items and cross items in each time frequency distribution, constructing a comparison function by using generalized Rayleigh's quotient, and finally separating fetal electrocardiosignals from the two paths of mother-fetal mixed electrocardiosignals. The method provided by the invention is tested on the basis of actual collected data, and can well separate fetal electrocardiosignals.

The invention provides a fetus electrocardio blind separation method based on the relative sparsity of time domains of source signals, comprising the following steps of: firstly acquiring mutually overlaid mother-fetus mixed electrocardiosignals of mother and fetus electrocardio in two paths from the body surface of the abdomen of a mother; then carrying out pretreatment on the acquired mother-fetus mixed electrocardiosignals, wherein the pretreatment includes the steps of: correcting the baseline drift of the mother-fetus mixed electrocardiosignals, and filtering the interference of power frequency of 50 Hz, the interference of high-frequency myoelectric signals, and the like; respectively positioning the mother electrocardiosignal and the fetus electrocardiosignal in the pretreated mother-fetus mixed electrocardiosignals; then searching relatively sparse time slices of the mother electrocardio and the fetus electrocardio; and finally separating the mother electrocardiosignal from the fetus electrocardiosignal by utilizing a basic blind source separation algorithm resolved through the general features of a matrix. The invention solves the problem that the time domains and frequency domains of the mother electrocardiosignal and the fetus electrocardiosignal are mutually overlaid and difficult to separate and can efficiently and accurately extract the fetus electrocardiosignal used for medical diagnosis.

An image area extracting method for extracting the desired areas from the image accurately and efficiently. The rough boundary area, in which the boundary area is roughly assigned between the desired area and areas other than the desired area, is divided into a plurality of small areas. The area extracting procedure is executed on each small area and the desired area and areas other than the desired area are detected from each small area. The boundary area mask image is formed based on the detection result and the desired area is extracted based on the boundary area mask image. Thus, the desired area can be extracted from the image accurately and efficiently.

The invention discloses a high-resolution SAR image individual building extraction method, and the method comprises the steps: firstly carrying out domain ontology modeling through combining the imaging features of a high-resolution SAR image individual building and the characteristics of a complex morphological structure, and constructing an individual building body semantic model in a high-resolution SAR image; secondly obtaining image regions with the good homogeneity and clear boundary through employing SAR image segmentation based on an object, wherein the image regions are the basic processing units extracted by building base units; combining with the related rule of the building base units in the body model, wherein the extracted image object characteristics comprise regional features, shape features, geometric features, texture features, and topological features; forming the object expression of body semantic description according to the body semantic rule and the object features, guiding the image objects to be automatically recognized as the building base units, and achieving the large-scale individual building recognition which takes the semantic knowledge as the center. The method can extract a large-scale individual building in the high-resolution SAR image accurately and quickly.

The invention is suitable for the technical field of image processing, and provides a video processing method, a video processing device and terminal equipment. The video processing method comprises the steps: obtaining a to-be-processed video; determining a scene type of each video frame in the to-be-processed video; according to the scene category of each video frame, carrying out target recognition on each video frame in the to-be-processed video through a trained first deep learning model, and obtaining a target recognition result, wherein the target recognition result indicates whether each video frame contains a target object, and the target object corresponds to the scene category; according to the target recognition result, identifying a plurality of target frames of the to-be-processed video, wherein the target frames comprise the target object; and according to the time sequence of each target frame in the to-be-processed video, sequentially combining the plurality of targetframes to obtain the target video.

The invention relates to a high-definition aerial remote sensing data automatic road extraction method; the method employs an aerial remote sensing image and DSM data of a corresponding researched area for extracting ground and overground areas, constitutes streets and extracts central lines of roads via a series of morphological conversions, including open and close operation, convex set shell, concave set shell, range conversion, watershed segmentation, and the like. The central lines and border lines of roads obtained by the disclosed method are continuous curves. Comparing to the method of the prior art, the algorithms and theories are complete, the road lines are prices, the calculations are quick, and the algorithms are good in adaptability; indicated after experiments that the disclosed method is very strong in robustness with respect to road extraction.

The invention belongs to the technical field of high voltage electrical equipment partial discharge detection and discloses a signal and noise separation method for a partial discharge signal and an information data processing terminal. A partial discharge waveform signal is collected; the partial discharge signal is extracted, a single pulse signal is acquired, a peak value and the phase of the pulses are recorded, and a phase distribution spectrum is drawn; for the extracted pulses, through multiple band pass filters, the down pulse peak value information of the corresponding filters is obtained, through principal component analysis and dimension reduction, characteristic parameters are obtained; the characteristic parameters are subjected to clustering analysis, the pulses are classified into multiple categories, a single-class pulse phase distribution spectrum is drawn according to the classification category number, the pulse peak value and the pulse phase, and the PD signal and the interference signal are determined. The method is advantaged in that the method can extract various pulse waveforms efficiently and accurately, utilizes the peak information under the multiple bandpass filters as a feature vector, can fully reflect the pulse waveform information and improves precision of separation of the interference signal and the partial discharge signal.

Provided are a screening method and system for a fundus image of glaucoma based on deep learning. The screening method comprises the steps that the fundus image to be identified is acquired; an FCN model is extracted through an optic disk to position and cut the optic disk, and optic disk sub-images are extracted and obtained; the optic disk sub-images are identified through a classification CNN model, and a classification result of the fundus image of glaucoma is obtained. According to the screening method and system for the fundus image of the glaucoma based on deep learning, identificationis only conducted on the cut and extracted optic disk, so that information processing amount is reduced, and the screening method and system can assist a doctor in improving the detection efficiency of glaucoma.

An embodiment of the invention provides a vehicle on-line detection and diagnosis system and method. The system comprises a far-end server for detecting vehicle data; an OBD detection device connected with an OBD interface of a vehicle to obtain actual OBD data of the vehicle; a handheld information acquisition device for obtaining vehicle paint thickness; and a detection terminal connected with the OBD detection device, the handheld information acquisition device and the far-end server. The detection terminal is used for comparing the actual OBD data of the vehicle and pre-stored standard operating parameters corresponding to the vehicle model to generate a diagnostic report.

The invention discloses a movie and television entity identification method based on IDCNN-crf and a knowledge graph. The method comprises the following steps: A, collecting movie and television datainformation; B, collecting a large amount of user search film and television data converted into texts through voice, and performing data analysis to obtain training data for model training; C, training the entity recognition model; D, collecting prediction data needing to be predicted, and inputting the prediction data into the entity identification model for prediction after data preprocessing;and E, carrying out verification processing on the model prediction result and outputting the result. The method provided by the invention can solve the entity identification problems of few annotation data, short text and spoken movie and television text data.

The invention relates to a screening method of dipeptide functional monomers having excellent separating capacity on saccharide. A dipeptide compound obtained on the basis of screening of standard deviation parameter D and R values is constructed, the dipeptide compound obtained through screening is further coupled with a group containing double bonds, polymerizable dipeptide functional monomers are obtained and are polymerized on surfaces of various matrixes, and a series of polymer modified chromatographic stationary phase materials are obtained. The materials have excellent separating capacity on oligomeric galactose, oligomeric fructose and sialic acid derivatives; meanwhile, the materials have excellent selective enrichment performance on the glycopeptide and separating capacity on a carbohydrate chain structure, have very high glycopeptide recovery rate and repeatability and have the wide application prospect in the fields of posttranslational modification proteomics research and the like.