178 results about "Digital image data" patented technology

The invention relates to the technical field of non-electrical variable control, in particular to a digital visual control system and method for a glass bottle production line. The method comprises the following steps: acquiring digital image data of molten glass; molten glass bubble distribution is identified based on the molten glass digital image data, the quality state of the molten glass is evaluated, and micro-defect distribution map data is generated; collecting the internal temperature of the smelting furnace through a temperature sensor array, and constructing a three-dimensional temperature field distribution model of the smelting furnace; and predictively optimizing and adjusting the heating control parameters of the smelting furnace based on the three-dimensional temperature field distribution model of the smelting furnace, and generating a regional heating power distribution strategy. According to the invention, through fusion of a plurality of advanced technologies, accurate sensing and whole-process linkage control of the molten glass state are realized, intelligence, quality uniformity and process visualization level of glass bottle production are significantly improved, and glass manufacturing is promoted to develop towards the intelligent and efficient direction.

The invention relates to an RAG-based voucher classification method, a medium and equipment, and the method comprises the steps: receiving digital image data of a to-be-classified voucher, carrying out the multi-modal optical character recognition processing to generate a structured OCR result, extracting a text semantic feature vector and a visual layout feature vector based on the structured OCR result, carrying out the fusion of the text semantic feature vector and the visual layout feature vector to generate a multi-modal query vector, and carrying out the classification of the to-be-classified voucher. Similar samples and semantic similarity scores and category metadata thereof are obtained through approximate nearest neighbor retrieval, after an initial candidate category list is generated, key field values are extracted for each candidate category, evidence credibility scores are calculated, comprehensive confidence scores are generated by fusing the semantic similarity scores and the evidence credibility scores, reordering is conducted, and a candidate category list is obtained. And finally, selecting a classification decision path according to the score distribution, and outputting a classification result and an interpretability report. The accuracy and robustness of voucher classification are effectively improved, and complex voucher scenes with changeable formats and fuzzy semantics can be processed; and the interpretability and reliability of the classification decision are enhanced.

Methods, systems, and non-transitory computer readable storage media are disclosed for modifying digital images via a generative neural network with local refinement. The disclosed system generates, utilizing an encoder neural network, a latent feature vector of a digital image by encoding global context information of the digital image into the latent feature vector. The disclosed system also determines a modified latent feature vector by trimming the latent feature vector to a feature subset corresponding to a masked portion of the digital image. Additionally, the disclosed system generates, utilizing a generative decoder neural network on the modified latent feature vector, digital image data corresponding to the masked portion of the digital image. The disclosed system also generates a modified digital image including the digital image data corresponding to the masked portion combined with additional portions of the digital image.

Retexturing items depicted in digital image data is described. An image retexturing system receives image data that depicts an item featuring a pattern. The image retexturing system identifies coarse correspondences between regions in the image data and a two-dimensional image of the pattern. Using the coarse correspondences, the image retexturing system establishes, for each pixel in the image data depicting the item, a pair of coordinates for a surface of the item featuring the pattern. The coordinate pairs are then used to generate a mesh that represents the surface of the item. The image retexturing system then applies a new texture to a surface of the item by mapping the new texture to a surface of the mesh. A shading layer and item mask are generated for the image data, which are combined with the retextured mask to generate a synthesized image that depicts the retextured item.

The systems and methods for improving pose estimation models are disclosed herein. Digital image data of an environment can be obtained and provided to a first machine learning model. A first confidence metric can be computed for the image. The first confidence metric can be compared with a threshold value and provided to a second machine learning model. A second confidence metric can be generated for training of machine learning models for pose estimation. A generic machine learning model can be updated using model parameters from trained local machine learning models.

Systems, devices, and methods are disclosed to correct aimpoint of a system of a weapon and affixed digital weapon sight. The method employs the components of a digital weapon sight configured together for a process of sensing of a ballistic event, storing of digital imagery data before and after a shot or series of shots, and then the performing of an image registration algorithm, via a cross-correlation type function. The image registration determines the misalignment between the images as a Cartesian shift, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing. Preprocessing methods for enhancing image registration algorithm accuracy are presented.

The invention relates to the technical field of image processing, and discloses a pathological image classification method and system based on graph neural feature fusion, a terminal and a storage medium, and the method comprises the steps: obtaining multi-scale digital image data, carrying out the extraction of the multi-scale digital image data, obtaining a plurality of image features, fusing the plurality of image features through a pyramid-shaped multi-scale feature fusion model to obtain a multi-resolution feature; obtaining a slide level label, constructing an initial graph neural network model based on a dynamic graph construction mechanism, a self-attention mechanism, a channel reduction mechanism and multi-resolution features, and training the graph neural network model according to the slide level label to obtain a target graph neural network model; and obtaining to-be-detected pathological image features, inputting the to-be-detected pathological image features into the target image neural network model for classification, and outputting a classification result. According to the method, features of the to-be-detected pathological image are classified according to the target image neural network model, and efficient and accurate classification is realized.

The invention relates to a method, a system, equipment and a medium for identifying a broken generation of ancient ceramics. The method comprises the following steps: acquiring multi-dimensional digital image data of ancient ceramics and carrying out classification processing to generate an original image set; extracting and fusing macroscopic features and microscopic features of the original image set to generate a multi-modal feature matrix; on the basis of the multi-modal feature matrix, a sample set with the similarity higher than a preset threshold value in a historical database is matched through a deep hash algorithm, and a first cutoff interval is determined in combination with sample age information; obtaining a glaze component analysis result of the ancient ceramic, and confirming a second cutoff interval according to the glaze component analysis result based on the first cutoff interval; and obtaining an artificial generation interruption result, and generating a generation interruption identification result according to a preset comprehensive decision rule in combination with the artificial generation interruption result and the second generation interruption interval. Through fusion of image multi-dimensional features, science and technology detection data and expert experience, efficient, objective and accurate determination of the ancient ceramic age interval is realized.

A computer system obtains digital image data of hair of a live subject; provides the digital image data to a style recommendation engine of the computer system; determines, by the style recommendation engine of the computer system, color of the hair of the live subject based on the digital image data; and executes a machine learning model of the style recommendation engine using the color of the hair of the live subject as input to generate a style profile as output. The style profile comprises parameters configured to control discharge of one or more components of a hair coloring formulation by a formulation dispensing system of a hair coloring device.

The invention relates to the technical field of image acquisition, and discloses an image acquisition method and device based on a multi-channel image acquisition card. The method comprises the following steps: inputting multiple paths of image signals into a multi-channel image acquisition card for signal conditioning to obtain digital image data; performing adaptive gain control on the digital image data to obtain gain-adjusted image data; carrying out digital filtering, image enhancement and feature extraction on the image data of which the gain is adjusted to obtain processed image data; performing definition evaluation, noise evaluation and dynamic range evaluation on the processed image data to obtain an image quality evaluation status word; and performing hierarchical storage on the processed image data according to the image quality evaluation status word to obtain distributed storage image data, thereby improving the real-time processing capability and data management efficiency of the multi-channel image acquisition card.

The invention relates to the technical field of industrial automation, in particular to a weftless tape machine process parameter self-adaptive acquisition system fused with machine vision. The system comprises a data acquisition module which is arranged on a production path of the weftless tape machine and is used for capturing a digital image of a weftless tape flowing through a view field of the weftless tape machine in real time; the data processing and parameter extraction module is used for receiving and processing the digital image and extracting process parameters from the digital image; the control module is used for receiving the process parameters, comparing the process parameters with preset target parameter values to generate deviation signals, generating control instructions according to the deviation signals and sending the control instructions to a controllable execution part of the weftless tape machine so as to drive the controllable execution part to carry out adjustment. By constructing an intelligent system from high-precision visual perception to real-time closed-loop control, the product quality consistency is improved, the rejection rate is reduced, and high automation and intelligence of the production process are achieved.

Embodiments of the disclosure relate to a display device and a data driving circuit. Specifically, there may be provided a display device comprising a display panel where a plurality of gate lines, a plurality of data lines, and a plurality of subpixels are disposed, a gate driving circuit configured to supply scan signals to the plurality of gate lines, a data driving circuit configured to convert digital image data into an analog data voltage and supply the analog data voltage to the plurality of data lines, a timing controller configured to control the gate driving circuit and the data driving circuit, a first gamma circuit generating a first gamma voltage corresponding to a first pixel during a period corresponding to a first color selection signal, and a second gamma circuit generating a second gamma voltage corresponding to a second pixel during a period corresponding to a second color selection signal different from the first color selection signal.

Advanced processing is performed in a chip. A stacked light-receiving sensor according to an embodiment includes a first substrate (100, 200, 300), a second substrate (120, 320) bonded to the first substrate, and connection wiring (402) bonded to the second substrate. The first substrate includes a pixel array (101) in which a plurality of unit pixels are arranged in a two-dimensional matrix. The second substrate includes a converter (17A) configured to convert an analog pixel signal output from the pixel array to digital image data and a processing unit (14) configured to perform a process for data based on the image data. At least a part of the converter is disposed on a first side in the second substrate. The processing unit is disposed on a second side opposite to the first side in the second substrate. The connection wiring is attached to a side other than the second side in the second substrate.

Systems and methods are disclosed to correct aimpoint of digital weapon sight. The method employs the components of a digital weapon sight configured together for a process of sensing of a ballistic event, storing of orientation data and digital imagery data before and after a shot or series of shots, and then performing an image registration algorithm using the imagery data stored. The image registration determines the translational offset between the images, and aimpoint of subsequent shooting is corrected through application of adjusted reticle symbology or adjusted image sensor windowing.

The invention discloses a novel on-chip coding calculation type hyperspectral imager, and relates to the technical field of spectral imaging. The system specifically comprises an optical imaging assembly which is used for carrying out space focusing on light of a target scene to form a two-dimensional light field; the spectrum coding module is used for modulating the spectrum information of the two-dimensional light field through a periodic micro-nano structure array to generate a coded light signal; the photoelectric sensor is used for converting the coded optical signal into digital image data; the spectrum reconstruction module is used for decoding the digital image data based on a deep learning algorithm and generating a three-dimensional data cube containing space-spectrum information; wherein the periodic distribution parameter of the micro-nano structure array is matched with the point spread function characteristic of the optical imaging assembly. The invention aims to reduce the manufacturing cost of a spectrum encoder, improve the production efficiency of the spectrum encoder and solve the matching problem of the spectrum encoder and an imaging system.

The invention discloses a moving target identification and tracking system based on an analog micromirror array (AMA), belongs to the technical field of photoelectric tracking and machine vision, and aims to solve the technical problems of slow response, large inertia and complex structure of a traditional mechanical tracking system. According to the technical scheme, an AMA composed of a plurality of MEMS micromirror units which can be independently controlled and have the static or quasi-static two-dimensional deflection capacity is adopted as a non-mechanical light beam deflection actuator, the AMA can be composed of micromirror arrays in various forms, digital image data are analyzed in real time to solve the target offset, and the non-mechanical light beam deflection actuator is obtained. And an instruction is generated to drive the AMA to deflect at a high speed so as to compensate the deflection, so that continuous and stable locking and tracking imaging of the moving target are realized. The system has the advantages of being high in response speed, high in tracking precision, free of mechanical inertia and compact in structure, supports flexible switching of various working modes such as wide-area scanning and long-distance tracking, and is high in application flexibility.

A method includes capturing a plurality of digital images of a physical object. While capturing the plurality of digital images, for each of the captured digital images: whether the digital image is of sufficient quality to determine an object type for the physical object is determined in real-time; an object type for the object is determined in real-time; and whether the digital image is of sufficient quality to generate a digital fingerprint for the physical object from digital image data of the digital image is determined in real-time. The method also includes: extracting features from one or more authentication regions of a digital image of the plurality of digital images, which has been determined to be of sufficient quality to generate a digital fingerprint for the physical object from digital image data of the digital image; and generating a digital fingerprint for the object based on the extracted features.

The invention relates to a method for detecting aircraft icing of an aircraft, the method comprising the following steps: - Recording at least a part of an outer flow surface of the aircraft by means of at least one optical image sensor of a detection device and generating digital image data containing the recorded part of the outer flow surface of the aircraft, - inputting the digital image data into a machine learning system of the detection device as input data, which has learned a correlation between digital image data as input data and various types of aircraft icing as output data by means of at least one machine-learned decision algorithm in order to obtain output data, and - Detecting aircraft icing depending on the output data received and, if aircraft icing has been detected, classifying the aircraft icing with regard to at least one icing type depending on the output data received.

Mechanisms are provided for performing an artificial intelligence (AI) based drive-through transaction. A digital image capturing device, in response to a vehicle entering a drive-through, captures a digital image of the vehicle. A computer vision operation is executed on the digital image to analyze data patterns and identify an identity of individual(s) within the vehicle. User profile(s) are retrieved that correspond to the individual(s) within the vehicle. A customized menu of products and / or services is generated based on user profile information and one or more menu items are pre-selected from the customized menu based on contextual information derived from at least one of audio or digital image data received during the drive-through transaction. A menu presentation computing device, located in the drive-through, is controlled to present the pre-selected menu item(s) and the customized menu.

The invention relates to a visual language model training method and system, and the method comprises the steps: providing a multi-modal geometric training data set which comprises a plurality of data units, each data unit comprises digital image data for encoding a geometric figure, a section of text data for describing a question to the geometric figure and a standardized answer encoded in a markup language format supporting machine analysis; generating one or more candidate answers according to the text data and the digital image data in the geometric training data set by applying a reinforcement learning framework and taking the visual language model as a strategy; for each generated candidate answer, a reward function module is applied to allocate a reward value. According to the method, generalized space intelligent improvement is realized, the effectiveness and robustness of training are improved, and the model can be encouraged to explore different problem solving paths and find a correct reasoning chain.

System and methods for extracting data from and authenticating digital images of documents. The system may receive first digital image data. The system may determine a first image type of the first digital image data. The system may determine a first version identifier based on the first digital image data. The system may determine a first ruleset. The system may determine a first set of pixel coordinate ranges for authenticating digital images. The system may determine a first authentication status for the first digital image. The system may determine a second portion of the first digital image at a second set of pixel coordinate ranges. The system may compare a first portion to the second portion. The system may determine a second ruleset. The system may update the image ruleset database to include the second ruleset.

Systems, devices, and methods are disclosed for determining provenance (e.g., origin, authenticity) for an object using digital image data of one or more objects. A system receives, via a network, first digital image data of an object. The system determines from the first digital image data, a first set of feature variables, each corresponding to a characteristic of the object. The system determines, via an artificial intelligence model and with input including the first set of feature variables and a comparison dataset, an origin for the object. The artificial intelligence model generates, based on the portion of first feature variables that match corresponding portions of the comparison dataset, an output indicative of an origin. The system communicates, via the network, an indication of the provenance of the object.

1. The name of the design product: digital camera (X05). 2. The use of the design product: for image shooting and storing digital image data. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo best indicating the design points: perspective view 1.

The invention relates to an optical measurement equipment digital image exchange system based on a VPX architecture, belongs to the technical field of image processing, and solves the problem of realizing optical measurement equipment digital image data distribution by utilizing the VPX system architecture. The system comprises a digital image switching board meeting the VPX standard, the digital image switching board takes an FPGA (Field Programmable Gate Array) chip as a core, is integrated with an ARM (Advanced RISC Machines) chip and is provided with a panel front output interface and a VPX connector rear output interface connected with a VPX backboard bus, the FPGA chip changes the current working mode of the system according to external control information sent by the ARM chip, and the FPGA chip is connected with the VPX backboard bus. Free switching between a real-time data stream mode and a post injection data stream mode is realized, so that different scene requirements are met. According to the invention, the VPX backplane bus is used for replacing a traditional optical fiber beam splitting or Camera Link cable, so that the number of external cables is reduced, the wiring complexity is reduced, and efficient and reliable work such as exchange and distribution of digital images of the optical measurement equipment is realized.

A device and a computer implemented method for explainable clustering of a scene. The method includes determining a first relation that relates a first object class to a second object class, wherein determining the first relation includes determining, depending on the first object class and the second object class, a pair of entities in a first knowledge graph, in particular a commonsense knowledge graph, that represents information about a domain, wherein the pair of entities is related with the first relation in the first knowledge graph, determining a cluster in that the scene belongs depending on the scene and depending on other scenes, determining a second relation that relates the scene with the cluster depending on at least one feature of digital image data representing the scene, determining a rule that maps the first relation to the second relation.

The invention belongs to the technical field of digital image transmission, and particularly relates to a digital image transmission system based on ultra-low time delay, which comprises the following steps: firstly, acquiring digital image data in a scene in real time by using a high-frame-rate and low-noise image sensor, and transmitting the acquired original image data; then, carrying out preprocessing operation on the collected original image data; then, a deep neural network model is utilized to adaptively adjust coding parameters and a coding mode according to the content characteristics of the image and the real-time transmission bandwidth condition; on the basis, the adaptive lightweight coding module is arranged, a complex inter-frame prediction algorithm in a traditional coding standard is abandoned, a block-level differential coding mode is adopted, and coding parameters and modes are adaptively adjusted according to image content characteristics and real-time transmission bandwidth; the method has the advantages that the coding delay is effectively reduced, and the high-real-time scene coding requirement is met.

The invention relates to a street view image recognition method and system based on quantum transfer learning. The method comprises the following steps: acquiring a streetscape image data set, and preprocessing the streetscape image data set; pre-training a DenseNet classical neural network based on the preprocessed streetscape digital image data to obtain pre-training parameters; removing the last layer of full-connection neural network of the DenseNet classical neural network, combining the last layer of full-connection neural network with a quantum convolutional neural network to form a mixed quantum classical neural network, fixing parameters of the DenseNet classical neural network based on pre-training parameters, and training the mixed quantum classical neural network by adopting a quantum machine learning framework based on an ISQ quantum compiler; and street scene image recognition is carried out by using the trained hybrid quantum classical neural network. According to the method, strong parallelism and non-local characteristics of quantum computing are applied, hidden information in data is potentially learned by using new computational logic, data training is accelerated, computing resources are saved, and street view digital image recognition can be realized.

This device reads radiographic images stored on dental imaging plates and converts them into digital image data. To use it, the imaging plate is placed in the patient's oral cavity for radiographic imaging. After imaging, the imaging plate is removed from the patient's mouth and inserted into this device for reading the radiographic images.

The invention relates to the technical field of medical instruments, and discloses an optical-mechanical-electrical integrated imaging system of a minimally invasive surgical instrument. The system comprises an illumination light path regulation and control device which is used for receiving ambient light parameters and dynamically adjusting spectral distribution and illumination intensity of emergent light; the image acquisition device comprises a miniature image sensor and an optical assembly associated with the miniature image sensor, and the miniature image sensor is used for acquiring optical signals of an operation area and converting the optical signals into digital image data; and the image processing device is used for receiving the digital image data output by the image acquisition device and performing multi-stage image enhancement operation, noise suppression operation and contrast optimization processing operation on the digital image data. Through collaborative optimization of the illumination light path regulation and control device and the image processing device, the technical problem that a traditional endoscope is poor in imaging quality in a low-light environment is effectively solved. Meanwhile, the modular design of the system is combined with the advanced manufacturing process, so that the system is endowed with high functional flexibility and scene adaptability.

The embodiment of the application provides a kind of digital image wavelet base extraction method, system and storage medium, belong to digital image data processing technical field.The method comprises: obtaining the PSF field, image tilt field and amplitude sampling sequence covering target image area, and establishing the space corresponding relationship of the three;For each to-be-solved position in target image area, extract PSF signal from PSF field, extract image slope value, and construct sampling path along slope direction in PSF signal according to slope value;Amplitude superposition processing is carried out to PSF signal along slope direction, and the wavelet base function corresponding to the to-be-solved position is generated;One-dimensional convolution operation is carried out to wavelet base function and amplitude sampling sequence along vertical direction, and the single-channel composite image corresponding to the to-be-solved position is generated, and all single-channel composite images are arranged according to spatial position to form the final image composite profile.The present application scheme realizes an accurate digital image wavelet base extraction and digital image synthesis method.