2224 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.

A method of filtering an image is disclosed, the method comprising: receiving filter coefficients and first component sample values corresponding to samples surrounding a reference sample, and inputting said filter coefficients and first component sample values into a cross component filter to produce a cross component filter output; wherein said cross-component filter uses a restricted number of bits to represent said filter coefficients and / or first component sample values to produce said filter output.

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.

A method comprising: obtaining an image block unit comprising samples in color channels of one or two chrominance channels and one luminance channel: reconstructing samples of said luminance channel of the image block unit: determining parameters for at least one prediction model for predicting samples of at least one color channel of the image block unit using a prediction model based on one or more reference samples in a neighboring block in current channel / frame, one or more reference samples in a block neighboring a co-located block in a reference channel / frame; and one or more reference samples inside the co-located block in the reference channel / frame; and determining said at least one prediction model as a polynomial and / or exponential prediction model.

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.

According to one embodiment of the present disclosure, a method by which a decoding device decodes an image is provided. The method comprises the steps of: acquiring image information from a bit-stream; determining whether at least one from among a template-based inter mode derivation (TIMD) mode and decoder-side intra mode derivation (DIMD) mode is applied to the current block; generating a prediction sample of the current block on the basis of the at least one from among the TIMD mode and DIMD mode; and generating a reconstructed sample of the current block on the basis of the prediction sample of the current block, wherein the TIMD mode is derived from a template of the current block and the DIMD mode is derived on the basis of the gradient of adjacent reference samples of the current block.

Implementations of the disclosure provide a video processing apparatus and method for performing intra prediction on a video block. The video processing method may include receiving, by a processor, reference samples from a video frame of a video comprising the video block. The video processing method may further include determining, by the processor, a reference sample filter based on a size of each video block. The reference sample filter is among a plurality of reference sample filters each derived for a different video block size. The video processing method may also include applying, by the processor, the determined reference sample filter to the received reference samples. The video processing method may additionally include performing, by the processor, the intra prediction on the video block using the filtered reference samples.

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 discloses a detection method of a systemic lupus erythematosus immune spectrum based on full spectrum flow cytometry, and relates to the technical field of cell detection, and the detection method comprises the following steps: providing a fluorescein coupling antibody of a detection index; carrying out antibody titration on the fluorescein coupling antibody to obtain a dyeing concentration; the method comprises the following steps: carrying out surface staining on peripheral blood mononuclear cells of an SLE patient by adopting a fluorescein coupled antibody, carrying out intracellular mitochondrial superoxide staining, and resuspending in a detection solution to obtain a test sample; carrying out sample loading test on the test sample and the prepared single staining reference sample and negative sample, and collecting and analyzing particle information; and performing enclosure and dimension reduction analysis on the analyzed data to obtain a systemic lupus erythematosus immune spectrum and a cell activation state analysis result. According to the application, dozens of immune cell subtypes and activation states thereof in SLE can be detected under the condition of a single-tube sample, an SLE patient immune map is constructed, and a reference is provided for evaluating disease progression and formulating a treatment scheme.

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.

A video decoder may be configured to receive a block of video data that was encoded using a coding mode that includes a search process in one or more reference frames. The video decoder may prefetch reference samples in a fixed search region of at least one reference frame of the one or more reference frames, and decode the block of video data using the coding mode, including performing the search process for the coding mode using the prefetched reference samples.

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.

A method for decoding includes: reference information of a current block is determined; a filtering process is performed on at least partial information in the reference information of the current block, to determine target information of the current block; a respective one of weighting coefficients is determined according to each of reference sample values of a first color component in the target information; a prediction block of a second color component of the current block is determined according to the weighting coefficients and reference sample values of a second color component in the target information; and reconstructed values of second color component samples of the current block are determined according to the prediction block.

The invention relates to the technical field of state identification of high-end equipment, and discloses a health state prediction method for high-end equipment, which comprises the following steps: S1, collecting operation parameters and state parameters of a wind driven generator; s2, preprocessing operation parameters and state parameters of the wind driven generator; s3, constructing a reference sample set for the wind driven generator based on the preprocessed state information; s4, judging the state grade of the wind driven generator based on the reference sample set and the state parameters; and S5, providing a maintenance suggestion for the wind driven generator according to the state grade. According to the method, the accuracy and efficiency of state recognition and prediction of the wind driven generator are remarkably improved, and powerful technical support is provided for safe and stable operation of the wind driven generator.

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.