1580 results about "Reference sample" patented technology

The invention discloses a display panel, a driving method and device thereof and a computer readable storage medium, and belongs to the technical field of display. The driving method of the display panel comprises the following steps: acquiring the number of carry points of a single reference sample in a preset frame rate control algorithm; configuring a jitter pattern according to the number of the carry points, wherein the jitter pattern is used for keeping the same number of times that each carry point presents positive polarity and negative polarity in cycle display of a specified frame number; and driving the display panel based on the jitter pattern and a preset frame rate control algorithm. According to the method, the preset frame rate algorithm is optimized by configuring the jitter pattern capable of keeping the number of times that each carry point presents the positive polarity and the negative polarity in the cycle display of the specified frame number, and the problem that the display effect is poor due to the fact that the jitter scheme of the preset frame rate algorithm is poor can be avoided.

A method of encoding or decoding video data comprises: for each respective intra prediction mode of a plurality of intra prediction modes in a most-probable mode (MPM) list: generating, based on reference samples for a template region and using the respective intra prediction mode, prediction samples for the template region; and determining a cost for the respective intra prediction mode; determining a first intra prediction mode and a second intra prediction mode in the MPM list having lowest costs; determining a preliminary prediction block for the first intra prediction mode and a preliminary prediction block for the second intra prediction mode; generating a prediction block based on a fusion of the preliminary prediction blocks weighted according to a weight for the first intra prediction mode and a weight for the second intra prediction mode.

The invention discloses a sewage plant accurate dosing method based on multi-mode automatic machine learning, and belongs to the technical field of sewage treatment. The method comprises the following steps: acquiring historical sensing data and image data of a sewage plant; extracting key features in the image by using a YOLO visual model, fusing the key features with sensor data, and constructing a multi-modal sample data set; carrying out modeling training on the fused data by adopting an automatic machine learning framework, and constructing a'water inlet-dosing-water outlet 'prediction model; generating a reference sample data set according to an existing dosing rule; inputting the sample data into the trained prediction model to obtain predicted effluent quality data, and adjusting the dosage until the predicted effluent quality of all samples meets a water quality standard condition; and finally outputting an optimized dosing sample data set. According to the invention, precision and real-time dosing of the sewage plant can be realized, chemical waste can be effectively reduced, the treatment efficiency is improved, the cost is saved, and the water quality is ensured to stably reach the standard.

A method of decoding coding units of a coding tree for an image frame from a video bitstream comprising splitting a region of the coding tree into a plurality of coding blocks, each of the coding blocks including a prediction block. Determining matrix intra prediction flags for the prediction block of each of the coding blocks, the determination based upon (i) an area of the region if the region meets a threshold, or (ii) a budget for the region if the area of the region does not meet the threshold. The method further comprises reading matrix coefficients from a memory for each prediction block determined to use matrix intra prediction according to the determined flag; and decoding the coding units using prediction blocks generated for each coding unit in the region using reference samples of each prediction block and the matrix coefficients.

The invention relates to the technical field of coal gangue recognition, in particular to a coal gangue AI recognition method and system based on infrared spectrum measurement, and the method comprises the steps: obtaining a near infrared spectrum of each contrast reference sample and a near infrared spectrum of each to-be-detected sample, and respectively recording the near infrared spectrums as each contrast infrared spectrum and each to-be-detected infrared spectrum; determining a contrast absorption vector and a to-be-detected absorption vector; determining the absorption peak saliency of each wave band; obtaining a target wave band; determining an absorption level vector of any target wave band; determining the absorption peak recognition degree and the potential information characteristic value of any target wave band in each infrared spectrum to be detected; determining the coal gangue recognition degree; and obtaining a training set and a training label of the classification model, and identifying the coal gangue in the to-be-detected sample. According to the method, the problem that the key feature information of the coal gangue cannot be accurately extracted due to the fact that the infrared spectrum data acquisition process is easily interfered by noise or redundant information is solved, so that the coal gangue recognition accuracy is improved.

The invention discloses an intelligent event studying and judging method based on machine learning, which comprises the following steps of: extracting features from multi-source event data, constructing an event fragment set with a uniform structure according to a preset time window, identifying fragments with stable features in the event fragment set as normal samples, constructing a reference model through an MCD algorithm, and analyzing the normal samples. The method comprises the following steps: extracting feature distribution in a normal state, comparing a to-be-analyzed event with a reference model, calculating feature offset, forming a behavior trajectory, determining an anomaly judgment range by adopting an elliptical envelope algorithm, and finally evaluating an event risk level and dynamically adjusting reference sample composition according to a continuous anomaly condition to realize intelligent identification and risk judgment of the event. According to the invention, dynamic perception and intelligent identification of complex events can be realized.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel (900); reconstructing samples of said luminance channel of the image block unit (902); determining a reference area for predicting samples of at least one color channel of the image block unit, wherein said reference area comprises one or more of reference samples in a neighboring block in current color channel / frame, in the neighboring of a co-located block in reference color channel / frame; and / or inside the co-located block in reference color channel / frame (904); determining filter coefficients of a filter for said predicting based on the reference samples and a shape of the filter (906); and predicting samples of at least one color channel of the image block unit by performing a convolution between the samples of said luminance channel and the filter coefficients (908).

The invention relates to the technical field of fruit detection, in particular to a blueberry acidity detection method based on hyperspectral imaging, which comprises the following steps: randomly selecting blueberry samples to be detected, and constructing a reference sample database; acquiring full-band hyperspectral image data of the to-be-detected blueberry sample by using hyperspectral imaging equipment; performing spectrum preprocessing on the hyperspectral image data; eliminating redundant information of redundant wave bands by combining a continuous projection algorithm to obtain a final characteristic wave band set, and extracting a spectral intensity matrix corresponding to the final characteristic wave band set from the hyperspectral image data based on the final characteristic wave band set; constructing a blueberry acidity detection model; and outputting the acidity predicted value of the blueberries. According to the method, the data dimension is greatly reduced, the sensitivity and modeling efficiency of the characteristic wave band are improved, and compared with a traditional destructive detection method, the method has the advantages of being high in speed, free of damage and suitable for large-scale sample detection.

An image encoding / decoding method and device according to the present disclosure can induce an intra-prediction mode of the current block on the basis of an induction mode, determine a reference sample of the current block, and generate a prediction sample of the current block on the basis of the intra-prediction mode and the reference sample.

The invention relates to the technical field of carbon emission accounting, and particularly discloses a carbon emission calculation method and system for railway infrastructure construction, and the method comprises the steps: firstly, calling basic data of a railway infrastructure from a background database and corresponding carbon emission which is verified to be real to construct a reference sample set; and carrying out embedded coding and dynamic semantic search on the basic data of the to-be-verified railway infrastructure uploaded by the user and each reference sample by adopting a data processing technology based on deep learning, so as to query response information based on the semantic query between the to-be-verified basic data and each reference sample. And the reasonable carbon emission amount of the railway infrastructure to be verified is intelligently estimated, and whether the total carbon emission amount of the railway infrastructure to be verified uploaded by the user is true or not is judged based on the reasonable carbon emission amount of the railway infrastructure to be verified. According to the method, rapid and accurate calculation and verification of the carbon emission in the railway infrastructure construction process can be realized, false declaration is avoided, and the authenticity and reliability of the carbon emission data are improved.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said luminance channel of the image block unit; determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more reference samples in a neighboring block in current color channel / frame, in the neighboring of a co-located block in reference color channel / frame; and / or inside the co-located block in reference color channel / frame; determining weights for predicting said target samples based on a ratio between a normalized luminance histogram in said reference area and a normalized luminance histogram co-locating said target samples; determining filter coefficients of a filter for said predicting based on the weights, the reference samples and a shape of the filter; and predicting samples of at least one color channel of the image block unit based on the samples of said luminance channel and the filter coefficients.

The invention provides an equipment running state optimization control method and device, electronic equipment and a storage medium. The method comprises the steps that historical data and real-time data of equipment operation are acquired, and key features related to the equipment operation state are screened out; preprocessing the historical data, dividing the preprocessed historical data into a plurality of categories, and generating a reference sample set of each category; performing weight calculation on the key features by using an analytic hierarchy process, and constructing a feature weight matrix; performing state estimation by using an improved multivariate state estimation algorithm; predicting the running state of the equipment according to the state estimation result, and judging whether the running state of the equipment is normal or not; and if the operation state of the equipment is normal, writing back an optimal control parameter generated based on the predicted operation state to the distributed control system. The parameter adjustment precision can be improved, the prediction precision can be improved, and the equipment operation efficiency can be improved.

The invention relates to the technical field of remote sensing recognition, in particular to a garlic recognition system and method based on remote sensing deep learning. According to the method, visible light and red edge wave band information in a multi-temporal remote sensing image for the phenological period of garlic are combined, the alignment precision of image data in the time sequence is improved through spectrum normalization and spatial calibration, and a multi-source classification sample set is constructed by superposing vegetation spectrum indexes and spatial sample positioning labels; a deep learning structure of semantic segmentation and an attention mechanism is introduced to enhance fine expression of spatial coherence and feature response of region recognition, the discrimination definition of a garlic region in an image is improved through channel spatial feature fusion, and a garlic pattern spot spatial boundary is subjected to continuous recombination and comparison verification with a reference sample. And the pattern spot precision and the sample consistency of the final identification area are ensured, so that the classification accuracy and the identification stability of the garlic planting area are remarkably improved under the complex background.

A video encoding or decoding apparatus and method perform existing inter prediction on a current block to generate a motion vector and predicted samples. The video encoding or decoding apparatus and method generate enhanced predicted samples for the current block using a deep learning-based video prediction network (VPN) on the basis of the motion vector, the reference samples, the predicted samples, and the like to improve encoding efficiency.

The invention discloses a single tree detection method and system based on a pre-trained large model and a ladder side tuning strategy, and relates to the technical field of forestry remote sensing intelligent monitoring, and the method comprises the steps: obtaining a remote sensing image, preprocessing the remote sensing image, and constructing a reference sample data set; using the pre-trained large model as a global feature encoder, constructing a bypass model as a local feature encoder based on a ladder side tuning strategy, and using the encoder to extract and fuse global and local features of the preprocessed remote sensing image stage by stage to obtain multi-level features; a segmentation threshold and a canopy processing strategy are preset for different scenes; constructing a double-flow feature decoder, processing the multi-level features according to a segmentation threshold value and a canopy processing strategy, and further processing the multi-level features by adopting a full convolutional network to obtain a tree canopy segmentation result; and outputting individual tree detection information in the target area. According to the method, the detection adaptability to complex scenes is improved, the dependence on large-scale annotation data is reduced, the single tree detection cost is reduced, and the efficiency is improved.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, a process is applied to the current video block based on template matching. At least one reference sample of a current template of the current video block is determined based on a block vector (BV) of the current video block during the process. The conversion is performed based on the applying.

An intra frame prediction method and apparatus based on interpolation filtering, a video encoding method and apparatus, a video decoding method and apparatus, and a system. The intra prediction method based on interpolation filtering comprises: determining, based on a determined filtering window and a reconstructed value of a sample in a reconstruction area neighboring to a current block, a filtering coefficient used to perform interpolation filtering on the current block; and performing interpolation filtering based on a sample value of a reference sample neighboring to a current sample and the filtering coefficient, to determine a predicted value of the current sample.

Provided are an image encoding / decoding method and apparatus. An image decoding method may comprise deriving chroma reference samples located around a chroma block and luma reference samples corresponding to the chroma reference samples based on an intra prediction mode of the chroma block being a cross-component linear model (CCLM) mode, deriving CCLM parameters based on the chroma reference samples and the luma reference samples, and generating a prediction sample of the chroma block based on the CCLM parameters. The chroma reference samples may be obtained from two or more chroma reference lines adjacent to the chroma block.

The invention discloses a zero sample / small sample industrial defect detection method and system, and the method comprises the steps: freezing the parameters of a CLIP image encoder in a segmentation branch, extracting image features, projecting the corrected features, carrying out the one-by-one product operation of the image features of different layers and corresponding text features, and splicing to generate an image-text similarity graph; in the classification branch, the extracted final image features are multiplied by the text features, and a positive / abnormal classification result of the image is obtained after processing through a learnable adapter; the parameters of the adapter are optimized through the difference between the output result of the minimization segmentation branch and the classification branch and the real label, and the zero sample detection mode directly generates an abnormal graph through the matrix multiplication of the image and the text features among the layers; a reference sample is introduced in a small sample detection mode, the cosine similarity between each layer of features and input sample features is calculated, and an obtained similarity graph and an anomaly graph generated by a zero sample are added to form a final anomaly thermodynamic diagram.

Provided are an image encoding / decoding method and apparatus. An image decoding method performed by an image decoding apparatus may comprise determining an intra prediction mode of a current block, performing planar intra prediction based on the intra prediction mode being a planar prediction mode and obtaining a reconstructed sample of the current block based on a prediction sample obtained based on the planar intra prediction. The performing the planar intra prediction may comprise applying filtering to an intra prediction reference sample of the current block.

The invention provides a rice planting area identification method and device based on multi-temporal remote sensing data, equipment and a storage medium. The method comprises the following steps: acquiring the multi-temporal remote sensing data according to a rice phenological period; determining a spectral index sequence of the multi-temporal remote sensing data to construct a spectral index time sequence curve, and generating planting characteristic data in the target area according to a spectral change curve; determining a spectrum time sequence template matched with the rice growth rule according to the planting characteristic data, performing matching analysis on the planting mode of each land block, and marking a land block set of rice planting mode characteristics; performing cross comparison on the plot set and the rice planting reference sample to generate a rice planting mode sample set, and constructing a rice planting area identification model based on the rice planting mode sample set; and analyzing the remote sensing data of the land parcels to be identified in the research area according to the rice planting area identification model, and determining a rice planting area identification result. According to the scheme, the accuracy of rice identification can be effectively improved.

In some embodiments, a decoder may receive, from a bit stream, a residual of a block and an indication that the residual was determined based on a refined partition edge. The decoder may determine the refined partition edge based on the indication and reference samples neighboring the block. The decoder may determine based on at least one prediction mode, a prediction for two parts of the block defined by the refined partition edge. The decoder may determine a reconstruction for the block based on the residual and the prediction of the block.

The invention discloses a wafer defect detection method based on one-shot, and belongs to the technical field of target detection, and the method comprises the following steps: inputting data, and taking preprocessed data as network input; constructing a twin network double-branch structure; training the model by a meta-learning strategy; and detecting by using a dynamic threshold value. According to the method, only one reference sample and a small number of defect samples are needed to be matched with a data enhancement technology to quickly extract sample features, model training is completed, and the detection efficiency is remarkably improved while the dependence of an algorithm on large-scale labeled data is reduced; the similarity threshold value is dynamically adjusted through the image sharpness value, compared with a traditional manual threshold value setting method, the automation degree is higher, and the false detection rate and the missing detection rate can be effectively reduced; and through a meta-learning strategy, when a new task is encountered, the model can be quickly adapted to the new task only by performing internal circulation for one time to perform fine adjustment on the model.

An intra prediction method by using cross component liner prediction mode (CCLM), includes: determining a luma block corresponding to a current chroma block; obtaining luma reference samples of the luma block based on determining L available chroma reference samples of the current chroma block, wherein the obtained luma reference samples of the luma block are down-sampled luma reference samples; calculating linear model coefficients based on the luma reference samples and chroma reference samples that correspond to the luma reference samples; and obtaining a prediction for the current chroma block based on the linear model coefficients and values of a down-sampled luma block of the luma block.

The invention provides an underground water environment monitoring system and method, and the method comprises the steps: carrying out the three-dimensional fluorescence spectrum full-wave band scanning of a known type of pollution source reference sample in a target monitoring region, and obtaining a three-dimensional fluorescence spectrum data set; performing characteristic analysis on the three-dimensional fluorescence spectrum data set to obtain fluorescence characteristic components, and determining a characteristic excitation wavelength corresponding to each fluorescence characteristic component; the classification contribution degree of each characteristic excitation wavelength is evaluated, and a characteristic excitation wavelength combination during source tracing of the pollution source is obtained through screening; performing fixed wavelength scanning on the to-be-detected underground water sample in the target monitoring area according to the characteristic excitation wavelength combination, and determining the initial type of the pollution source; and determining the relative contribution degree of the type of the pollution source of the to-be-detected underground water sample, simulating the migration trend of the pollution plume according to the underground water migration and diffusion model, and generating the position of the pollution source of the to-be-detected underground water sample. By adopting the scheme of the application, the pollution source of the underground water can be quickly identified and positioned at low cost.

The invention belongs to the technical field of image processing, and particularly relates to a coded disc surface tiny scratch recognition method based on micro texture feature analysis, which comprises the following steps: acquiring a coded disc grating texture image and converting the coded disc grating texture image into a rectangular coordinate system to obtain an expanded grey-scale map; the optimal main direction of the grating texture is determined through rotation projection analysis; for each pixel point in the grey-scale map, sampling along two sides of the optimal main direction to form a reference sample; calculating a weighted average value of the gray values of the sample points in the reference sample to obtain a trend prediction value, calculating a residual error between the gray values of the pixel points and the trend prediction value, and determining a texture deviation index in combination with the change condition of the gray values of all neighborhood pixel points of the pixel points; and finally, identifying the scratches through threshold segmentation. According to the method, the grating edge interference can be effectively inhibited under the strong periodic texture background, and the tiny scratch features can be accurately extracted.

The invention discloses an image coding prediction mode selection method, which solves the problems that in the prior art, image redundant information is not efficiently utilized in conventional image coding, and complex video coding is relatively high in application delay for low hardware cost, and the like. The absolute reference sample value and the recovery sample value of the reference image are used as reference sample values, so that the image redundant information is effectively utilized, and the compression efficiency is improved; a fixed number of coding prediction modes are set in a selection set, different coding prediction mode selection sets are determined for different scenes, and coding code streams are reduced; when the optimal coding prediction mode is selected, resource consumption and delay are reduced by means of source image information, so that high coding efficiency and good subjective effect can be realized under the condition of low cost and low delay.

The embodiment of the invention provides a chart generation method, a multi-modal data platform, computing equipment and a computer readable storage medium. The chart generation method comprises the steps of obtaining a task description text and original data of a chart generation task; based on the task description text and the original data, analyzing the chart generation task to obtain a generation scheme of the chart generation task; based on the generation scheme, retrieving a reference sample chart from a sample chart database; and executing a chart generation task on the original data by adopting the generation scheme based on the reference sample chart through the multi-modal generation model to generate a target chart. The target chart with high accuracy and high applicability can be generated even for uncommon chart types, and the diversity and quality of chart generation are effectively improved.