2371results about "Television systems" patented technology

The invention provides a microscopic light splitting type event camera and traditional camera alignment system. According to the system, a microscopic light path module guides the same light beam to an event camera and a traditional camera at the same time according to a set splitting ratio; the hardware trigger control module respectively accesses external trigger pulses to the event camera and the traditional camera; the time synchronization management module performs link delay compensation on the event camera and the traditional camera; the data acquisition module is used for acquiring an event graph of the event camera and an image frame of the traditional camera in parallel; the space alignment module processes the image frame to enable the image frame to be aligned with an event graph of an event camera, and the aligned image frame and event graph are obtained; and the fusion detection module is used for sending the aligned image frame and event graph into a fusion detection network for fusion detection to obtain a detection result. According to the system, modal mismatching and training noise caused by soft synchronization and rough alignment are avoided, so that fusion detection has stable, reviewable and engineered deployment capabilities.

The invention belongs to the technical field of video live broadcast monitoring management, and particularly relates to a live broadcast content real-time compliance detection method and system based on deep learning, and the method breaks through the limitation that traditional text detection only depends on dominant keywords by constructing a text semantic analysis chain matched with original words and mapping sensitive words through constructing metaphor words. The identification of deep semantic violation contents such as metaphor and private obscure expression is realized; the method comprises the following steps: performing linkage verification on text violation clues and image behavior characteristics through behavior characteristic analysis of image frames before and after time positioning association of text violation based on a time positioning cross-modal collaborative detection mechanism, and forming a multi-modal judgment logic for text triggering image confirmation; a progressive detection strategy of hierarchical thresholds is designed, a first threshold is set as violation confirmation and a second threshold is set as suspected violation early warning, and real-time preliminary detection and precise secondary verification are combined, so that stepped processing from suspected violation to violation confirmation is realized, and the real-time performance and accuracy of detection are balanced.

An image sensor includes a pixel array including pixels and a controller configured to control the pixels to produce a plurality of images. The image sensor also includes a defect detector configured to determine a target image based on an average green pixel value and a gain value related to a light exposure time among the images, and detect an adaptive static defective pixel, which is a defective pixel with a fixed location based on pixel values included in the target image.

The invention discloses a method and device for detecting color reducibility of a mobile phone camera and a medium, and relates to the technical field of image quality detection.The method comprises the steps that after geometric alignment is conducted on a color card image set, color block areas are divided, and a cross-light-source color block observation matrix is obtained through color block feature extraction; converting the cross-light-source color block observation matrix into a target color space coordinate, and performing illumination correction to obtain a cross-light-source color coordinate matrix; inputting the cross-light-source color coordinate matrix into a deep learning model, outputting a color block level high-dimensional color embedding matrix, and obtaining a high-dimensional color embedding vector set corresponding to the tested mobile phone camera in combination with an attention weight; calculating a color reducibility error index based on the high-dimensional color embedding vector set in combination with the standard reference library; and performing qualification judgment according to the color reducibility error index to generate a qualification detection conclusion. According to the invention, a stable and reliable technical basis is provided for camera quality detection under a multi-light-source imaging condition.

The invention relates to the technical field of computer vision, and discloses a Monte Carlo tree search-based camera lens assembly focusing calibration algorithm and system, and the algorithm comprises the steps: constructing a three-dimensional search space comprising a focus position, a horizontal inclination angle and a vertical inclination angle, and combining a high-contrast cross wire test pattern to generate a local definition matrix; calculating an initial inclination angle based on the matrix to construct a root node of the Monte Carlo tree; according to the algorithm, an optimal search path is dynamically selected by applying an upper confidence bound strategy, child nodes are expanded in a three-dimensional space, physical adjustment is executed, and optimal three-dimensional configuration parameters are finally determined based on node scores through back propagation iterative optimization. The method breaks through a traditional one-dimensional search framework, effectively avoids a pseudo peak trap caused by inclination of a lens-sensor axis, improves the calibration precision and efficiency of the full-view-field definition of the camera, and achieves the precise positioning of a global optimal solution under a complex assembly tolerance.

The utility model discloses a suction jig, and discloses a detection device and a carrying device with the suction jig, and the suction jig comprises a suction head which is provided with a suction part, the suction part is used for sucking an optical filter, and the suction part is provided with a containing groove; the background piece is arranged in the containing groove, the background piece is provided with a background face, the surface area of the background face is larger than that of the light sensing area of the light filter, and when the light filter is sucked, the background face is opposite to the light filter so as to cover the light sensing area of the light filter. The suction head can suck the optical filter and move the optical filter to the foreign matter inspection position and the carrying position, and the background is used as the inspection background of the optical filter, so that the foreign matter detection rate is increased.

The invention provides an infrared and photoelectric combined hidden camera detection system and method, and the method comprises the steps: obtaining environment temperature and light intensity distribution reference data through an infrared thermal imaging sensor and a photoelectric sensor, and building a feature reference library; exciting light with different wavelengths is emitted, and the light supplementing intensity is adjusted according to the reference library; at each light supplementing time node, collecting a temperature distribution image, calculating a temperature change rate matrix, and identifying a response time constant abnormal region; a reflected light intensity matrix is collected, a reflected light intensity change rate matrix is calculated, and a reflectivity abnormal area is identified; generating a fusion feature matrix according to the temperature and the reflected light intensity change rate matrix; the similarity between thermal response and a photoelectric reflection distribution mode in a neighborhood is evaluated by constructing a feature fusion judgment function and calculating a spatial correlation coefficient, when a preset threshold condition is met, it is judged that a camera hiding suspicion exists at the position, and a candidate position coordinate set P is recorded. The detection precision can be improved, and the false alarm rate can be reduced.

Disclosed is a method and apparatus for performing, by a management platform operatively connected to both a video surveillance platform and a positioning platform via network connectivity, at least one among image processing and video analytics on obtained images or video related to a detected asset tag to provide user awareness related to a location or movement of the asset tag in a defined environment.

Various examples, systems, and methods are disclosed relating to a visual modeling pipeline. A first computing system can receive, by a visual language model (VLM), visual data and a first prompt corresponding to a context of the visual data. The first computing system can determine, using the VLM and based at least in part on the visual data, at least one description of the visual data. The first computing system can determine a second prompt based at least in part on the at least one description. The first computing system can convert, using a large language model (LLM), the at least one description to a file format. The first computing system can use the at least one description in the file format for at least one automated test.

The invention discloses a camera module test platform and test method, and relates to the technical field of camera module test.The camera module test platform comprises a workbench, a rotating table, a driving mechanism, a bearing mechanism, a detection mechanism and an adjusting mechanism, the top end of the workbench is fixedly connected with a fixing column, the rotating table is arranged outside the fixing column, the driving mechanism is arranged in the workbench, and the bearing mechanism is arranged on the rotating table; the bearing mechanism is arranged at the top end of the rotating table, the detection mechanism is arranged at the top end of the fixing column, and the adjusting mechanism is arranged in the rotating table. According to the camera module testing device, the arrangement mode that the rotating table, the driving mechanism, the bearing mechanism, the detection mechanisms and the adjusting mechanism are matched is utilized, rotation of the rotating table can be utilized, the bearing mechanism drives the camera module to pass through different types of detection mechanisms to be detected in sequence, the testing efficiency of the camera module is greatly improved, and the adjusting mechanism is arranged, so that the testing efficiency is improved. Classification can be selected for processing according to the detection result, and the detection efficiency of the camera module is further improved.

The invention relates to the technical field of ship outboard video monitoring, in particular to a marine intelligent self-cleaning camera system and a control method thereof. Through cooperation of the rotary telescoping mechanism, the lens self-cleaning module, the control unit and the image detection algorithm module, intelligent storage of the camera and automatic cleaning of the lens can be realized, and the reliability and environment adaptability of the marine outboard camera can be greatly improved; the lens self-cleaning module integrates a cleaning liquid spraying function and a high-pressure water washing function, and intelligently adjusts a cleaning strategy in combination with the stain level recognized by the image detection algorithm module and parameters such as salt mist concentration collected by the environment sensing module, so that precise cleaning in different pollution scenes is realized, the cleaning effect is improved, and cleaning consumables are saved; the image detection algorithm module is based on a deep learning model and an image gray analysis algorithm, and is combined with a reference image regular updating mechanism, so that interference factors such as ambient light change can be effectively eliminated, and the accuracy of stain detection is improved.

A work machine control system is provided with an acquisition process unit and a limit process unit. The acquisition process unit acquires a detection result of an object around a machine body of a work machine in a state incapable of operating the work machine. The limit process unit creates a limit state capable of limitedly operating the work machine, when the detection result meets a predetermined condition and then a gate lock lever is so operated as to create a state capable of operating the work machine.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention discloses a video data processing method, electronic equipment and a readable storage medium, relates to the technical field of video data transmission, and is applied to a video data processing system which at least comprises an intelligent sensing module and a dynamic transmission module. The video data processing method comprises the following steps: extracting metadata in video data through an intelligent sensing module; collecting network state data, and sending the network state data and the metadata to a dynamic transmission module; target data transmission parameters are determined through the dynamic transmission module based on the network state data and the metadata, and the target data transmission parameters at least comprise one of redundancy, frame priority, code rate, retransmission parameters and quantization parameters; and transmitting the video data based on the target data transmission parameter. According to the technical scheme, the bandwidth utilization rate, the transmission real-time performance and the anti-frame-loss effect in the video data processing process are improved.

The application provides an image quality detection method and device, electronic equipment and a computer readable storage medium. The image quality detection method comprises: acquiring a current image captured by a shooting device; acquiring a shooting quality parameter related to the current image, wherein the shooting quality parameter comprises at least one of a reflection detection result, a brightness detection result, a definition detection result and an angle detection result of the current image; and determining whether the quality of the current image is qualified according to the shooting quality parameter. In the application, the quality of the image being shot is detected, the uploading amount of invalid images is reduced, and the data processing amount of invalid images is reduced.

The invention relates to a camera module intelligent test device based on multi-station cooperation, and relates to the technical field of camera testing, the camera module intelligent test device based on multi-station cooperation comprises a rack, a conveying device and a bearing device are arranged in the rack, the conveying device is used for transferring a test product to the bearing device, and the bearing device is used for bearing the test product. The bearing devices are provided with matching openings for exposing front cameras or rear cameras of the tested products, and the bearing devices are arranged in the conveying direction of the tested products; a rear shooting near-focus testing mechanism, a rear shooting white card testing mechanism, a rear shooting far-focus testing mechanism, a rear shooting black card testing mechanism, a front shooting white card testing mechanism, a front shooting fixed-focus testing mechanism, a front shooting black card testing mechanism and a front shooting white card testing mechanism which correspond to the bearing devices are arranged in the rack. According to the invention, a serial test process is thoroughly converted into parallel assembly line work, so that the test efficiency is greatly improved, and the high precision and high reliability of the test are ensured.

The invention discloses a camera state detection method. The method comprises the following steps: firstly, synchronously acquiring video stream image data and equipment state data of a camera; then, intelligently judging that the camera is in a static state, a translation state or a jitter state by analyzing the motion vectors of the feature points in the video stream; then, according to different motion states, adaptively selecting and executing corresponding image quality feature or motion mode feature extraction strategies, and fusing with equipment state data; and finally, inputting the fused feature vectors into a pre-trained state classification model, and outputting a comprehensive state detection result containing confidence. According to the invention, through multi-modal data fusion and an adaptive analysis strategy, the accuracy and reliability of camera state detection are significantly improved.

Disclosed are embodiments for determining the position and speed of a projectile in three-dimensional world coordinates using a single camera and radar. In some embodiments, a method comprises: determining a zero radial speed time of the projectile based on radar measurements of the projectile; determining a radial speed of the projectile at a particular time based on the radar measurements; determining a radial speed slope at the particular time based on the radial speed measurements; determining a two-dimensional image point of the projectile at the particular time; determining a depth scale coefficient at the particular time based on the zero radial speed time, the radial speed, the radial speed slope, the image point, and a homographic transformation; and determining a position of the projectile in space at the particular time based on a three-dimensional position of the camera, the depth scale coefficient, the image point and the homographic transformation.

An image debugging system relates to the technical field of image processing. The image debugging system comprises a test platform, a proxy server and an image processing unit. The image processing unit comprises an image processing assembly line, the image processing assembly line comprises a plurality of image processing modules, the image processing unit is connected with at least one image sensor, and the image sensor is used for collecting optical signals and outputting image data to the image processing assembly line. Image processing is carried out by the corresponding image processing module; the test platform is used for generating an image interface and generating a control instruction according to a set protocol data packet, the proxy server is used for receiving, analyzing and forwarding the control instruction to the image processing unit, and the image processing unit responds to the control instruction to configure a corresponding image processing module in the image processing assembly line, or sends the control instruction to the image processing assembly line. And the image processing unit responds to the control instruction to obtain image data processed by the corresponding image processing module in the image processing assembly line.

A system and method for estimating object distance and / or angle from an image capture device may obtain an image including a fiducial marker captured by an image capture device; extract a pixel width and / or a pixel height of the fiducial marker in the image space of the image; determine, based on the pixel width and / or the pixel height, a known width and / or a known height, and one or more calibration parameters, a distance of the fiducial marker from the image capture device; estimate, based on the pixel width, the pixel height, the known width, and the known height, a yaw angle of the fiducial marker with respect to the image capture device; determine, based on a slope of a positional vector of the fiducial marker, a roll angle of the fiducial marker with respect to the image capture device; and / or provide the distance, the yaw angle, and / or the roll angle.

A system includes a processor that controls an operable flying device, controls an operable imaging means, performs real-time analysis of generated data using artificial intelligence processing means, calculates an optimal rescue route based on the analysis results, controls means for loading and transporting supplies with the flying device, controls means for deploying equipment in regions where communication is disrupted and collecting local condition data, analyzes the collected data to generate emergency response information, and communicates the emergency response information to external rescue organizations.

The invention discloses a camera module abnormity judgment method. The method comprises the following steps: obtaining an evaluation image shot by a camera module, and obtaining clear intensity based on the evaluation image; obtaining a region of interest of the evaluation image; and dividing the region of interest into a plurality of sub-regions, and determining an anomaly evaluation result of the camera module based on the plurality of sub-regions and the clear intensity. According to the method, the evaluation image is acquired, the region of interest is subjected to sub-region division, and the divided sub-regions and the definition intensity are subjected to correlation analysis, so that the definition difference between the sub-regions can be effectively compared, whether an abnormal region exists in the evaluation image or not can be identified, and the imaging quality of the camera module can be quickly evaluated.

The embodiment of the present application provides an image processing method, comprising: a first device receiving request information of a second device; then, the first device acquiring position information of M key points in a to-be-transmitted image and determining a target display area in the to-be-transmitted image based on the request information and the position information of the M key points; then, the first device sending the target display area information to the second device, so that the second device processes the to-be-transmitted image into a target display image based on the target display area information. The second device receives the target display area information and processes the to-be-transmitted image into the target display image based on the target display area information and presents the target display image on a display area thereof. Through the above method, the to-be-transmitted image is presented on the display area of the second device with the best visual effect, and the problem that two devices cause the image to be not adaptive due to different shapes of display areas, thereby affecting the image presentation effect, is solved.

A calibration method for determining angles between an image acquisition module reference frame and an accelerometer reference frame of a portable electronic device, comprising receiving a set of images of a known pattern from the image acquisition module from different positions, the known pattern being in the same spatial position when acquired, and each image being associated with a gravity data corresponding to the measurement of the gravity by the accelerometer expressed in the accelerometer reference frame, determining the angles between the image acquisition module reference frame and the known pattern reference frame based on the received pictures of the set of pictures, determining the angles between the image acquisition module reference frame and the accelerometer reference frame based on the determined angles between the image acquisition module reference frame and the known pattern reference frame and assuming the gravity measured by the accelerometer is the same for each image.

The invention relates to the technical field of image analysis and mode recognition, in particular to a state monitoring and fault diagnosis method and system based on artificial intelligence. The invention discloses a state monitoring and fault diagnosis method based on artificial intelligence. The method comprises the following steps: S1, acquiring oil sample type data, performing background purification processing, and then executing a high-precision panoramic imaging process; the X-direction shooting number and the Y-direction shooting number are obtained based on the two-axis motion platform; s2, generating a grid coordinate sequence covering the deposition area according to the X-direction shooting number and the Y-direction shooting number, and forming a line-by-line scanning path; and synthesizing a full-focal-plane image based on the scanning path, and outputting a fused panoramic depth panorama according to the synthesized full-focal-plane image. According to the invention, through background purification, panoramic imaging, improved Mask-R-CNN mode recognition model segmentation and multivariate index fusion, precise recognition of wear particles and intelligent early warning and grading of wear states are realized.

A computing system may generate, manage, and verify dynamic digital credentials recorded on a distributed ledger. The system may receive credential data from an issuer, generate a cryptographic hash representing the credential content, and record the hash on a blockchain along with issuer identifiers and version references. When a credential is updated, the system may calculate and store differential data between versions to improve storage efficiency and maintain traceability. Each version may reference the previous hash, forming an immutable version chain. Recipients may store credentials in digital wallets and share verifiable credentials containing issuer signatures and refresh links for automatic updates. Verifiers may authenticate credentials by validating issuer signatures and comparing credential hashes with corresponding blockchain records, ensuring authenticity, integrity, and version transparency across decentralized systems.

A method performed by a surgical system that includes cameras within an operating room. The system receives a first image from a first camera, representing a first FOV that has an object at a first location. The system receives a second image from a tracking camera having the object at the first location, and determines a first pose of the first camera based on the first and second images. The system receives a third image captured by a second camera, representing a second, different FOV, and having the object at a second location. The system receives a fourth image captured by the tracking camera having the object at the second location, and determines a second pose of the second camera based on the third and fourth images. The system determines a relative spatial transformation between the first and second cameras based on the first and second poses.

A building security system includes computer-readable storage media having instructions stored thereon that, when executed by processors, cause the processors to: receive, from cameras communicably coupled to the building security system, video data, wherein the video data includes one or more video recordings, retrieve contextual information associated with the video data, analyze, using one or more artificial intelligence models, the video data, wherein the analysis of the video data includes detecting one or more details, determine, using the one or more AI models, based on the contextual information and the one or more details detected within the video data, a relevance of the one or more video recordings, and, based on the relevance of the one or more video recordings, automatically implement, using the one or more AI models, an action to delete or reduce a storage size of at least one of the one or more video recordings.

A network entity for processing a data stream into which media content is encoded, the data stream comprising packets, each packet comprising a packet type identifier identifying a packet type associated with the respective packet from a plurality of packet types, wherein each packet having a packet type associated therewith from a first set of packet types from the plurality of packet types comprises an operation point identifier identifying an operation point associated with the respective packet from a plurality of operation points within a scalability space spanned by n scalability axes, wherein each packet having a packet type associated therewith from a second set of packet types from the first set of packet types additionally carries data. The network entity is configured to read a scalability axes descriptor from packets having a predetermined packet type associated therewith that is disjoint from the second set and to interpret the operation point identifier in accordance with the scalability axes descriptor.