56 results about "Image Quantification" patented technology

The invention discloses a single-bit image recovery method and device, a terminal and a medium, and relates to the field of image processing. The method comprises the following steps: performing single-bit quantization on a to-be-recovered image by adopting a non-zero quantization threshold, and determining an initial image quantization model; replacing a sign function in the initial image quantization model with a smooth hyperbolic tangent function, performing low-rank matrix decomposition on the to-be-recovered image, and determining a target image quantization model; determining an initial optimization model according to the truncated least square function and the target image quantization model; a semi-quadratic minimization method is adopted to convert the initial optimization model, and a target optimization model is determined; solving the target optimization model by adopting a near-end block coordinate descent algorithm to obtain each low-dimension sub-matrix and a noise matrix; and determining a reconstructed image by using each low-dimensional sub-matrix. The problem that in the prior art, quantization noise cannot be effectively restrained, and therefore image restoration precision is affected is effectively solved.

A surface performance evaluation device including memory and a processor coupled to the memory. The processor being configured to: acquire a captured image, which is a moving image of a test object on which a liquid is dispersed, and which is captured by a camera; quantify, based on the captured image that is acquired by the processor, a degree of diffusion of the liquid that is dispersed on the test object and diffuses; and evaluate a surface performance of the test object based on an index quantified by the processor.

The present application provides an image sensor, a working method thereof, and an electronic device. A bit line module is provided, and in a first state, a low-gain reset signal is sampled into a first equivalent capacitor; in a second state, a high-gain reset signal and a high-gain image signal are sampled and outputted in sequence through a second gain output bit line; when the second state returns to the first state, the low-gain reset signal sampled in the first equivalent capacitor is outputted through the first gain output bit line, and then the low-gain image signal is sampled and outputted through the first gain output bit line; at the same time, a gain output selection circuit is connected to a matching readout circuit to achieve effective image quantization; while achieving a high dynamic range of the image sensor, the cost and size of the image sensor are reduced; because there is no need to sample and hold the output of the pixel circuit to reduce the KT / C noise of the circuit, the circuit noise level is lower, further improving the performance of the image sensor.

The invention relates to the technical field of image recognition, in particular to an automatic classification system for predicting cardiovascular and cerebrovascular events, which comprises the following steps of: segmenting carotid plaques and tracking the carotid plaques frame by frame to obtain preliminary plaque segmentation results; fusing and predicting to obtain a fusion segmentation result; extracting quantitative features of the carotid plaque; and performing prediction according to the fusion segmentation result and the quantitative features, and outputting a classification probability result corresponding to the cardiovascular and cerebrovascular event. According to the method, the problem of poor single-modal recognition effect is effectively solved, the accuracy of cardiovascular and cerebrovascular event prediction is remarkably improved through technical means such as multi-modal data fusion and feature guide attention, and powerful support is provided for clinical diagnosis and risk assessment. A carotid artery plaque part in common ultrasonic data is tracked and enhanced frame by frame through a plaque tracking module in advance, so that a subsequent network can capture plaque related semantic features more easily. And image quantitative features are extracted and input into a classification module, so that more accurate classification prediction is realized.

The present invention discloses a single-bit image restoration method, device, terminal, and medium, relating to the field of image processing. The method employs a non-zero quantization threshold to single-bit quantize an image to be restored, determining an initial image quantization model; employs a smooth hyperbolic tangent function to replace the sign function in the initial image quantization model, and performs low-rank matrix decomposition on the image to be restored, determining a target image quantization model; determines an initial optimization model based on a truncated least squares function and the target image quantization model; employs a semi-quadratic minimization method to transform the initial optimization model and determine a target optimization model; employs a proximal block coordinate descent algorithm to solve the target optimization model to obtain low-dimensional submatrices and a noise matrix; and utilizes the low-dimensional submatrices to determine a reconstructed image. This method effectively addresses the problem that prior art cannot effectively suppress quantization noise, thereby affecting image restoration accuracy.

An adaptable deep learning method is provided that delivers sound hepatic lesion identification in NETs, while significantly reducing human effort for data annotation and improving model generalizability for PET image quantification. A region-guided GAN (RGGAN) model conducts image-to-image translation between list-mode simulated PET images and real-world clinical data, while preserving semantic content of interest, e.g., lesions. The RG-GAN model is integrated with a lesion detection model into an end-to-end, unified framework for joint-task learning, such that the two models can benefit from each other. The RG-GAN translates the list-mode simulated data into real world-style images, which appear to be drawn from the real clinical PET image dataset, and feeds the translated images into the lesion detection model for training. In order to deal with the limited diversity of list mode-simulated PET image data, a specific data augmentation module is incorporated into the unified framework to improve model training.

The invention belongs to the technical field of postoperative care guidance, and discloses an oral implant postoperative care guidance system based on edge calculation. The system comprises an original image acquisition and preprocessing module, a real-time image analysis and quantification module, a data fusion feature construction module, a personalized reply dynamic modeling module, an intelligent risk assessment and decision module and a self-adaptive nursing guidance generation module. The method comprises the steps of obtaining an image quantitative index data set, obtaining a multi-modal time sequence feature vector, obtaining a personalized recovery prediction report, carrying out risk assessment based on the personalized recovery prediction report, obtaining a risk assessment decision report, and generating a personalized nursing guidance report based on the risk assessment decision report. The method has the remarkable advantages of being high in nursing evaluation authenticity, large in risk active prediction effect and good in personalized nursing guidance effect.

The present invention discloses a deep learning system and method for video action migration, including a person video data preprocessing unit, a video feature quantization unit, a video feature recombination unit, and an action migration video generation unit, which are respectively connected to a system control unit. The person video data preprocessing unit preprocesses the source image and the reference image, and extracts the source key point information in the source image and the reference key point information in the reference image. The video feature quantization unit respectively extracts the depth features of the source image and the reference image, performs feature quantization, and obtains the source image quantization feature and the reference image quantization feature. The video feature recombination unit predicts the quantization feature of the target image according to the source key points, the source image quantization feature, and the reference image quantization feature. The action migration video generation unit outputs the target image according to the quantization feature of the target image. The system control unit stores the program and conducts control. This system is used to achieve action migration and ensure the clarity and resolution of the target image.

This invention provides a clinical trial matching system and method for tumor patients based on a large language model and OCR technology, applicable to the field of medical data processing. This application parses clinical data and trial information, processes unstructured text, and generates structured clinical feature data through contextual association analysis; extracts key data and performs double verification and correction to generate supplementary structured data information; enhances the structured clinical feature data and supplementary information based on a multimodal processing pipeline module, extracts image quantification indicators, analyzes immunohistochemical results, and generates a comprehensive matching score; compares patient features with inclusion and exclusion conditions item by item through a rule engine and semantic similarity calculation to generate preliminary matching results; corrects misjudgments of marginal cases through context-aware multi-round reasoning, and adjusts the ranking based on clinical trial priority weights to generate an optimized clinical trial matching list; and generates a clinical trial matching report based on the above data.

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The invention relates to the technical field of synthetic aperture radar image processing, and discloses an SAR image quantization and enhancement method based on histogram statistics, and the method comprises the steps: obtaining a minimum value and a maximum value of an SAR amplitude diagram; setting parameters required for SAR image quantization and enhancement, performing normalization processing on the SAR amplitude diagram according to a histogram statistical order to obtain a first normalized amplitude diagram, and extracting sample points from the first normalized amplitude diagram; determining an amplitude quantization upper limit according to the histogram result and the image brightness adjustment factor; according to the amplitude quantization upper limit, performing amplitude limiting processing on the first normalized amplitude diagram, and performing second normalization to obtain a second normalized amplitude diagram; performing contrast enhancement on the second normalized amplitude diagram to obtain an enhanced amplitude diagram; the SAR image enhancement method and the SAR image enhancement device solve the technical problems that according to an existing enhancement method, noise is prone to being amplified, brightness distortion is caused, or contrast ratio improvement is insufficient, and the overall processing efficiency is low.

The invention relates to the technical field of immunohistochemical staining image quantitative analysis, in particular to an immunohistochemical staining image quantitative analysis method and system, and the method comprises the following steps: S1, obtaining a digital image of an immunohistochemical staining section; and S2, carrying out color deconvolution processing on the digital image, and decomposing the digital image into an exclusive channel image corresponding to at least one target coloring agent and a background channel image. According to the method, color deconvolution is realized through a non-negative matrix factorization algorithm, target coloring agent signals and background and other dyeing signals in the composite dyeing image are accurately split, and the problem that dyeing components of an existing system are not thoroughly separated is solved; through combination of a Mask R-CNN deep learning network and multi-mode adaptive parameters, positive and negative cells in different dyeing modes can be accurately identified, and multiple scenes such as nuclear dyeing, membrane dyeing and slurry dyeing can be adapted.

This invention discloses a low-power chip, its data processing method, and a storage medium. The method includes: evaluating whether the bias will overflow after quantization based on a first quantization factor of a block image, a second quantization factor of the weight matrix of the block image, and the bias corresponding to the block image; if it is determined that the bias will overflow after quantization, then at least one of the first quantization factor and the second quantization factor is corrected; quantizing the block image, the weight matrix, and the bias using the corrected quantization factor and the remaining uncorrected quantization factors; performing fixed-point operations on the quantized block image, the quantized weight matrix, and the quantized bias using a fixed-point arithmetic unit for the corresponding network layer to obtain the fixed-point operation result; and performing inverse quantization on the fixed-point operation result to obtain the block features of the block image.

A method for quantifying liquid leakage from a barrier coating includes: a) providing a system for acquiring and analyzing images of a wet substrate; b) loading the wet substrate into a holder; c) illuminating the wet substrate using an illumination system; d) acquiring at least one image of the wet substrate using an imaging system; e) using an analysis unit to transform at least one image of the wet substrate to provide at least one transformed image, and quantifying liquid leakage from the barrier coating based on the at least one transformed image; and f) providing an output including a value that identifies the amount or percentage of liquid on the wet substrate, and / or a generated image showing the amount or percentage of liquid on the wet substrate.

The invention provides a knee osteoarthritis treatment decision-making tree system and device, and relates to the technical field of knee osteoarthritis treatment decision-making trees, and the system comprises a data receiving module, a data processing module and a result generation module. The data receiving module is used for receiving natural language text data and iconography image data of the patient and respectively preprocessing the natural language text data and the iconography image data; the data processing module comprises a text feature unit, an image feature unit and a decision unit; the decision-making unit is used for fusing the text features and the image quantization features to obtain comprehensive features; based on the comprehensive features, through a decision tree model, a treatment scheme for knee osteoarthritis is output; and the result generation module is used for sending out the treatment scheme. According to the scheme, information such as medical records of patients can be automatically analyzed, personalized treatment schemes are given, interference of human factors is reduced, real-time and objective decision support is provided for doctors, and therefore the diagnosis and treatment efficiency and effect are improved.

The invention relates to the technical field of medical health, in particular to an intelligent evaluation and intervention system for muscle quality of a patient with advanced solid tumors. The multi-dimensional dynamic evaluation unit is used for forming the extracted somatic cell mass, the score of the anorexia-cachexia syndrome, the standardized skeletal muscle index and the average muscle radiation attenuation value into multi-dimensional feature data, and the multi-dimensional dynamic evaluation unit is used for evaluating the quality of the somatic cells on the basis of the multi-dimensional feature data and fusing tumor treatment scheme data. Generating a multi-dimensional muscle health index and a malignant fluid risk probability through a decision tree model, and dividing patients into different risk levels; and the intervention strategy generation unit is used for outputting a comprehensive intervention scheme based on the multi-dimensional muscle health index and the malignant fluid risk probability in combination with the real-time tolerance data and the risk level of the patient. According to the system, the L3 level skeletal muscle index and the muscle radiation attenuation value can be extracted more accurately and stably, manual annotation and manual variation are reduced, and the reliability of image quantification is improved.

The present invention relates to the technical field of laser grinding of diamond composite sheets in superhard material processing, and in particular to a laser grinding control system for diamond composite sheets, wherein a partitioning module is provided to divide a processing surface into concentric ring-shaped high, low and slope grinding zones based on data from a height measuring probe; a monitoring module synchronizes laser pulses to capture spark images, quantifies the projected area ratio, the maximum spark cluster diameter and the discreteness, and binds the polar coordinate position and the grinding zone number; a control module performs the first grinding with an initial removal amount, and dynamically adjusts the subsequent removal amount of each ring zone according to the abnormal distribution of sparks; a data feedback module determines that the abnormal points are concentrated in a single grinding zone and exceed a threshold value and provides feedback, which can effectively improve the grinding quality and provide effective data feedback to relevant technical personnel to facilitate timely tracing of quality problems, and can adapt to the processing control of flat or special-shaped diamond composite sheets.

Disclosed herein is a medical system (100, 300) comprising a memory storing machine executable instructions and a plurality of neural networks. The plurality of neural networks comprises a noise estimation neural network and at least one image quantification neural network (124). The noise estimation neural network is configured to output a noise estimate (128) of the medical image (126, 126') in response to receiving the medical image as an input. The at least one image quantification neural network is configured to output image attributes of the medical image in response to receiving the medical image as an input. The medical system further comprises a computing system.

The invention relates to the technical field of geotechnical engineering, in particular to an analysis method and device for the plugging effect of a grouting material, and the method comprises the steps: collecting a whole grouting process image of a cement-based material in a micron-sized crack or pipeline; carrying out error elimination and binarization processing on the image; extracting edge features from the processed image to segment a plurality of particles which have adhesion and are not agglomerated, and determining the particles; determining particles of which the areas are greater than a preset agglomerated particle threshold value, and determining and marking the agglomerated particles to generate an image quantitative index of the plugging process; and generating an analysis result of the plugging effect of the grouting material according to the evolution curve corresponding to the image quantification index in the plugging process. Therefore, the problems that the micro change process of the particles is difficult to record in real time and the spatial distribution and change rule of the particles cannot be comprehensively revealed due to the fact that related technologies are mainly based on macroscopic model tests or analysis of final-state slurry consolidated bodies, and consequently grouting material plugging effect prediction and construction reliability are affected are solved.

The invention discloses a quantitative analysis method for a jointed rock mass blasting cracking mode, which comprises the following steps: establishing a jointed rock mass blasting stress wave propagation theoretical model, deducing a propagation equation of stress waves in a jointed rock mass, and determining stress wave reflection, transmission and superposition rules; a jointed rock mass blasting numerical model is constructed based on ANSYS / LS-DYNA software, material parameters and state equations of rocks, joints, explosives and air are determined, an RHT damage model is adopted for the rocks and the joints, an MATHIGHEXPLOSIONBURN model is adopted for the explosives and combined with an EOSJWL state equation, and an EOSLINEARPOLYNOMIAL state equation is adopted for the air. According to the method, theoretical derivation, numerical simulation and image quantitative analysis are combined, and quantitative analysis of the blasting cracking mode of the jointed rock mass is achieved. The engineering cost can be effectively reduced, and the blasting effect and the construction safety are improved.

The application discloses a method for multi-modal large model inference acceleration, and belongs to the technical field of artificial intelligence, aiming to solve the technical problems of long time consumption in the pre-filling stage caused by large image quantization loss and too many image coding tokens during the inference of a multi-modal large model. The method first extracts the overall features of an image through a mask auto-encoding network, then acquires the candidate regions of the image by using a detection network and completes the alignment and extraction of the regional features, and obtains the image sequence feature vector after the overall features and the local features are fused; meanwhile, the input text is segmented and embedded to obtain the text word vector, and the two types of vectors are spliced and fused according to the sequence dimension to input a large language decoding model to complete decoding. The application can greatly reduce the image token calculation amount, significantly reduce the time consumption in the inference pre-filling stage under the premise of ensuring the inference effect, and improve the overall inference efficiency of the multi-modal large model.

The invention relates to a lubricating oil emulsification detection system and detection method based on microscopic video analysis. The system comprises an image acquisition unit used for realizing microscopic video stream acquisition by adopting a camera or a camera; the calculation processing unit is used for executing an image processing and analysis algorithm; the result storage unit is used for storing images with labels, quantized data and report files; the detection method comprises the following steps: S1, collecting a microscopic video stream, initializing an analyzer and a storage environment, and establishing a corresponding directory; s2, reading video frames according to a set interval and carrying out basic preprocessing; s3, passing the read video frame through a core detection assembly line: highlight interference elimination-candidate emulsified block mass extraction-background modeling verification-morphological optimization processing; s4, performing quantitative analysis on the detected candidate area; and S5, generating a final report. The method is sensitive in detection precision and high in efficiency, realizes quantification and standardization, and shows remarkable advantages in the aspects of economy, reliability and environmental safety.

The invention discloses a method for observing the shape and number of synapses in a dentate gyrus region based on image analysis, and relates to the technical field of neurobiology. The method comprises the following steps: S1, tissue treatment: after an experimental animal is anesthetized, pouring a buffer solution and a stationary solution into the heart in stages, separating hippocampus tissue, trimming and fixing, pre-cooling, slicing and preserving at low temperature; s2, dyeing and marking: performing targeted marking on the dentate gyrus area of the sea horse by adopting DiI particles through a fine tungsten steel through needle, and preserving at low temperature after incubation; s3, fixing and mounting: fixing for the second time, and mounting with an anti-fluorescence quenching agent after DAPI nuclear dyeing; s4, confocal imaging is carried out, exclusive parameter layer scanning is set, and qualified dendritic spine images are screened; and image quantitative analysis can be further carried out, and accurate quantification is realized according to classification standards. The method is standard in operation, greatly improves sample quality and experimental result credibility, optimizes experimental efficiency and controllability and reduces technical threshold by means of targeted marking and standardized processes, and is suitable for relevant basis and application research of the hippocampal synapses.

The application belongs to the field of display module residual image quantitative test, and relates to a display module residual image quantitative test device, which comprises a lower jig, an upper jig b provided above the lower jig, and an upper jig a fixedly connected to one side of the upper jig b; a shielding assembly provided between the lower jig and the upper jig b, the shielding assembly comprising a through hole b, a partition grid a and an optical test probe; and a switching assembly provided on the top of the lower jig. Through the arrangement of the shielding assembly, the upper jig a or the upper jig b can cover the entire display module main screen area, and only the through hole a or the through hole b position is reserved for brightness measurement, external light interference is effectively shielded, and it is ensured that the measurement data reflects the true brightness value of the display module main body itself. Through the arrangement of the switching assembly, the switching of the upper jig a and the upper jig b can facilitate the test of the black-white junction center position and improve the detection comprehensiveness.

The application discloses a kind of oral prosthetic body abutment relationship image quantitative evaluation systems, belong to oral prosthetic body image processing technical field, including image acquisition and pretreatment module, feature atlas construction module, topological analysis calculation module and quantitative evaluation mapping module.Image acquisition and pretreatment module obtains the three-dimensional scanning image of prosthetic body in situ state, obtains two-dimensional development image by gray mapping and two-dimensional development, separates out highlight contact zone by multi-scale morphological opening and closing reconstruction operation;Feature atlas construction module constructs morphological feature atlas according to the morphological gradient distribution characteristics of highlight contact zone under different scales;Topological analysis calculation module tracks the continuous boundary of abutment contact zone in morphological feature atlas, and calculates the length, width and area quantitative value of contact zone;Quantitative evaluation mapping module maps quantitative value to prosthetic three-dimensional space frequency domain, generates quantitative index for evaluating the closeness of abutment relationship.

The present application discloses an image recognition method, device, electronic device, and storage medium, which obtain an image to be recognized; perform linear processing on the image to be recognized to obtain linear image data; wherein the data type of the linear image data is a floating-point type; perform preprocessing on the linear image data to obtain preprocessed image data; wherein the preprocessed image data is within a preprocessing threshold range; perform a first quantization processing on the preprocessed image data according to an image quantization coefficient to obtain first image data; wherein the data type of the first image data is an integer type; perform a second quantization processing on the first image data to obtain second image data; wherein the data type of the second image data is an integer type; perform recognition processing on the second image data to obtain a recognition result of the image to be recognized. In this way, during the image recognition process, the image data to be processed is converted into integer data, which reduces the processing difficulty and the amount of calculation, thereby effectively improving the processing efficiency of the neural network model and promoting the further application of neural network technology in the field of computer vision.

A method for quantifying surface defects on a coated substrate includes: a) providing a system for acquiring and analyzing images; b) loading a coated substrate, wherein the coated substrate includes a coating formed on the surface of the substrate, onto a holder; c) illuminating the coated substrate using an illumination system, wherein illuminating the coated substrate includes illuminating the coated substrate at an incident angle of 15° to 85° with respect to a plane parallel to the surface of the coated substrate to generate shadows of any defects on the surface of the coated substrate; d) acquiring one or more images of the coated substrate using an imaging system; e) using an analysis unit to transform one or more images of the coated substrate and provide one or more transformed images, and quantifying surface defects on the coated substrate based on one or more transformed images; and f) providing an output, wherein the output includes a value that identifies the amount or percentage of defects on the coated substrate, and / or a generated image showing the amount or percentage of surface defects on the coated substrate.

The invention relates to a shooting parameter adjusting method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a plurality of second shot images of a current shot scene based on a first shooting parameter of a first shot image; each second shooting parameter corresponding to the second shooting image is a shooting parameter after the first shooting parameter is adjusted; obtaining a target evaluation model; the target evaluation model is obtained by training the initial evaluation model according to the target shot image and a second image quantized value of the target shot image; the target shot image is an image meeting the aesthetic preference of the user in the plurality of second shot images; and obtaining a third image quantized value of a third shot image shot by using the current shooting parameter according to the target evaluation model, and adjusting the current shooting parameter according to the third image quantized value. By adopting the method, the adaptability and generalization performance of the target evaluation model to diversified scenes and personalized aesthetic appreciation can be improved, so that the accuracy of the obtained shooting parameters is guaranteed.

The invention discloses a method for evaluating the recovery benefit of a giant panda national park greenway on a refined patient. The method comprises the following steps: acquiring an image of the giant panda national park greenway; preprocessing the giant panda national park greenway image; selecting a sperm fraction patient subject; obtaining the recovery benefit of the perceptual classification patient subject on the greenway image of the giant panda national park by adopting a perceptual recovery scale; screening environmental factors in the giant panda national park greenway image; quantifying environmental factors in the giant panda national park greenway image; and constructing a correlation prediction model between the environment characteristic factors and the scores of the perception recovery scale. According to the method, environment feature factors are quantified through images, greenway environment features are systematically analyzed, the association prediction model is constructed, dynamic association between environment elements and rehabilitation indexes of the precisely-classified patient is revealed, and a new data-driven normal form is provided for scientific design of the rehabilitation-oriented greenway.

The invention discloses a method for cooperatively distinguishing pores and bubbles in ultra-stable foam based on FC and sphericity analysis, and belongs to the field of solid-liquid separation of flotation clean coal, and the method comprises the following steps: obtaining a three-dimensional grayscale image of the foam through Micro-CT, performing three-dimensional reconstruction according to a filtered back projection algorithm, and extracting a gap structure; determining a representative sub-volume based on the change in porosity to ensure statistical reliability; adopting a Feret Caliper method to calculate geometrical characteristic parameters of the gap and performing morphological classification; determining a distinguishing threshold TH by combining the relationship between the sphericity and the bubble volume fraction; and automatically identifying and distinguishing the bubbles and the pores according to the threshold value. And finally verifying by using an optical microscope result. According to the method, non-destructive recognition and quantitative analysis of the internal structure of the ultra-stable foam are achieved, high precision and repeatability are achieved, and a reliable image quantification means is provided for optimization of a coal dehydration process.