36results about How to "Full details" patented technology

The invention discloses a Web GIS (geographical information system)-based comprehensive alarm display method. The method comprises the following steps: importing the tower and plant station data with geographic information to a GIS spatial database, and connecting the towers in the same circuit to form a circuit based on the sequence of tower numbers according to the circuit and the tower numbers of the towers; importing the information in the GIS spatial database; calling a display interface provided by a GIS service platform; performing layered drawing on power equipment on a Web browser according to equipment types and voltage grades to form a comprehensive display page; associating a service sub-system with the comprehensive display page to organize all comprehensive alarms according to the equipment, and organizing all comprehensive alarms associated with the same equipment together to form an comprehensive alarm array of the equipment. By adopting the Web GIS-based comprehensive alarm display method, the alarm information of sub-systems such as a power transmission and transformation monitoring system, a power grid scheduling automation system and a prudential system is integrated, and the alarms are classified, so that the comprehensive alarm information of the power grid can be learnt more visually, intuitively and thoroughly.

The invention relates to a pig face recognition method adopting a multi-channel convolutional neural network. The pig face recognition method is technically characterized by comprising the steps of collecting a pig face data image and constructing a pig face data set for multi-channel convolutional network training; constructing a multi-channel convolutional network model: extracting eye featuresby using two shallow networks, fusing the eye features, extracting the overall image features of the pig face by using a deep network, and inputting a fusion result of the last three networks into a standard support vector machine; training a multi-channel convolutional network model and performing parameter adjustment optimization; testing the recognition performance of the network model to obtain a trained network model; and shooting and numbering the pig individuals with the face images stored in the database, and inputting shot image data into the trained network model to obtain a pig facerecognition result. According to the pig face recognition method, the image features with large individual differences are added on the basis of end-to-end, so that the robustness is high, and non-contact pig face individual recognition is achieved, and the stress response of pigs is reduced, and the automatic management level of a pig farm is improved.

The invention discloses a salt and pepper noise filtering method based on a similar function, comprising the steps of setting an image filtering window in a 8-bit gray level image, determining noise pixels or suspected noise pixels through central pixel gray level values, and otherwise, judging the pixels to be signal pixels, establishing a marking matrix to mark the noise pixels, eliminating the pixel points in the filtering window, wherein the gray levels of the pixel points are identical to that of the central pixels, constituting a first signal point set by using the signal points which satisfy the marking matrix elements, processing the signals in the first signal point collection to obtain a first output image, setting the marking matrix element corresponding to the position of the output pixel in the output image to finish the filtering processing. The invention has better capability of denoising and maintaining details.

The invention relates to an intravascular image fusion method and system and an image acquisition device, and the method comprises the steps: collecting a first modal image and a second modal image ina blood vessel at the same time, and enabling the first modal image and the second modal image to belong to different types of modal images; fusing the first modal image and the second modal image byadopting an image fusion algorithm to obtain a corresponding fusion image. According to the method, the OCT image and the IVUS image are fused, the advantages of the OCT image and the IVUS image arebrought into full play, meanwhile, the complementary defects of the OCT image and the IVUS image are overcome, in other words, the advantages of the strong tissue penetrating power of the IVUS and thehigh resolution of the OCT image are brought into full play, and finally the high-quality fused image is obtained.

The invention discloses a face image synthesis method based on adaptive expression. By using the method, problems that synthesized image definition is low and details are not complete in an existing method are solved. The method comprises the following steps of firstly, processing a database, carrying out image filtering on all the images, then partitioning the images and extracting an image block characteristic so as to obtain one training image block dictionary and two photograph block dictionaries; secondly, testing whether a photograph block includes edge information or face characteristic information, according to that, selecting different dictionaries and searching a neighbor block; and finally, using a Markov network model to obtain image blocks to be synthesized, fusing all the image blocks to be synthesized and acquiring the synthesized image. Compared to the prior art, by using the method, a synthesis result possesses high definition and complete details and can be used for face retrieval and identification.

The invention discloses a face figure synthetic method based on coupling neighbor indexes, mainly solving the problem that a present method does not consider the training figure effect in neighbor selection. The method comprises the steps of: 1, diving a training figure sample set, a training photograph sample set and a test sample set; 2, diving all images into image blocks and forming block sets; 3, diving training photograph block sets and corresponding image blocks sets into a plurality of subsets; 4, establishing the indexes of each pair of photograph-image block in the training photograph block subsets and training image block subsets to K neighbor pairs thereof; 5, searching for a nearest neighbor of each test photograph block, and furthermore obtaining K neighbors thereof; 6, utilizing a Markov weight network to solve a reconstruction weight; 7, solving an image block to be synthesized; and 8, iteratively executing steps 5-7 for N times, and obtaining a finally synthesized image through fusion. Compared with the prior art, the synthesized image has higher definition and more complete details, and can be used for face retrieval and identification.

The present invention provides a resolution improvement based film and TV works renovation method for film and TV works with low resolution and low definition. The method includes the specific steps of firstly, acquiring a resolution of an original video and a target resolution, and calculating a zoom ratio; then, segmenting an input video into an image frame set according to a certain segmentation manner; then, performing conversion according to a prestored mapping relation, so as to obtain high resolution video frames; and finally, combing the high resolution video frames to obtain a high resolution video. The prestored mapping relation is obtained by learning a hybrid expert model, and the process is finished offline in a computer. The method has the advantages of good adaptability, fast speed, good effect, expandability and so on.

The invention discloses a gas ash microscopic image segmentation method and system based on a full convolution residual network, and belongs to the technical field of image processing. The method comprises the following steps: S1, constructing a data set; S2, constructing a full convolutional neural network model; S3, constructing a full convolution residual error network model; and S4, carrying out segmentation test on the gas ash microscopic image. According to the invention, the gas ash microscopic image can be segmented accurately; an image segmentation effect comparison experiment is carried out; the FCRN network shows a good segmentation effect, the MIoU index reaches 90.15%, the contour of the segmented image is clear, details are complete, semantic segmentation of the gas ash microscopic image is realized, a foundation is laid for subsequent accurate identification of gas ash components, and the method is worthy of popularization and application.

The invention discloses a face portrait compositing method based on Bayesian inference, which mainly solves a problem that an existing method does not consider similarity constraints between adjacent image blocks in a neighbor finding or weight solving stage. The scheme of the face portrait composting method comprises the steps of 1, dividing a training portrait sample set, a training photo sample set and a test sample set; 2, dividing all images into image blocks and forming block sets; 3, dividing the training photo block set and the corresponding portrait block set into a plurality of subsets; 4, selecting nearest neighbor blocks from a training photo-portrait block set; 5, carrying out neighbor block reselection on the nearest neighbor blocks according to an Euclidean distance in the training photo-portrait block set, and solving a linear combination weight of the reselected neighbor blocks; 6, solving portrait blocks to be composited according to the neighbor blocks and the combination weight; and 7, iteratively executing the steps 5-6 for N times, and fusing to acquire a final composite portrait. The face portrait composting method has the advantages of high resolution of a composite result and more complete details, and can be applied to face retrieval and recognition.

The invention discloses a small-sample high-resolution remote sensing image scene classification method. The method mainly comprises the following three parts: firstly, preparing source domain and target domain training sample sets, respectively selecting a proper amount of images from different remote sensing scene data sets to respectively construct a source training sample set with a large number of samples and a target domain training sample set with a small number of samples, and simulating the condition of small sample classification; secondly, AlexNet is selected as a DCNN main network, and deep convolution feature extraction is carried out on the sample image in combination with an FPN network layer fusion strategy; and finally, realizing feature distribution migration from a source domain to a target domain by using an improved distribution adaptation algorithm WS-BDA based on single sample weighting, and training a target classifier by using migrated feature data to finish classification of remote sensing scene images.

The invention discloses a procedure for emergency room cardiopulmonary resuscitation (ERCPR), and relates to the technical field of cardiopulmonary resuscitation operation processes. When cardiac arrest is diagnosed, four teams (divided into four roles A, B, C and D) are immediately formed, cardiopulmonary resuscitation is immediately carried out by the group members according to a C-A-B principle, A/C pressing is conducted till the Lucas cardiopulmonary resuscitation machine is installed, then A assists B in preparing tracheal intubation articles, B carries out airway management, firstly, a balloon mask is used for ventilation, trachea intubation is conducted after Lucas are installed, the A and C are matched with the B at a ratio of 30: 2 for pressing and ventilation, C connects a defibrillation monitor to the patient, immediately holds the carotid artery pulsation by a hand to judge the pressing quality for 5 seconds after defibrillation preparation is made, then immediately defibrillates if a defibrillable heart rate exists, places Lucas together with A after defibrillation (or not defibrillated), C establishes a vein channel, draws blood and applies medicines, D prepares Lucasand starts the machine, A installs the machine after self-inspection is completed, and D is responsible for recording time and controlling cardiopulmonary resuscitation quality.