5163 results about "Image capture" patented technology

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

Autonomous aerial vehicle technology and, more specifically, environmental illumination for autonomous unmanned aerial vehicles involves, in some embodiments, a UAV including upward-facing image capture devices, downward-facing image capture devices, one or more illumination sources, and a computer system. The computer system is configured to direct the one or more illumination sources to selectively emit light into a surrounding physical environment while the UAV is in flight, process images captured by any one or more of the plurality of upward-facing image capture devices or the plurality of downward-facing image capture devices to estimate a position and / or orientation of the aerial vehicle, generate a planned trajectory for the aerial vehicle through a physical environment based on the processing of the images, and control a propulsion system and / or flight surface of the aerial vehicle to cause the aerial vehicle to autonomously maneuver along the planned trajectory.

The invention relates to a method, a device, equipment and a program product for controlling a robot to execute tasks. The method includes acquiring a user input for a robot and an image captured by a camera of the robot. The method includes generating a sequence of actions for the robot based on the user input and the image by a visual language action model trained by reinforcement learning for a world model. In addition, the method further comprises the step of controlling the robot to execute the task corresponding to the user input by executing the action sequence.

An apparatus is configured to classify indicator lights of surrounding vehicles as either active or inactive. The apparatus may use a Siamese network to determine respective feature vectors from respective images captured by a vehicle at different times. The apparatus may also embed speed and time information in the respective feature vectors based on odometry information associated with the vehicle at a time each respective image was captured, and fuse, using a temporal attention mechanism, features and the speed and time information from the respective feature vectors to produce a fused feature vector. The apparatus may further process the fused feature vector using capsule modules to produce an indicator feature vector, calculate a similarity metric from the indicator feature vector, and process the similarity metric with a classifier to output an indicator classification.

The invention provides a focusing method, device and equipment. The method comprises the following steps: determining pixel offset between a red channel image and a green channel image of a tissue slice; the red channel image and the green channel image are a red channel image and a green channel image of a tissue slice in a ghosting image captured by the color image sensor when a red light filter, a green light filter and double optical wedges in the focusing module are modulated at the same time; determining an out-of-focus distance according to the pixel offset and a preset corresponding relation; the preset corresponding relation is the corresponding relation between the pixel offset and the defocus distance; and focusing according to the out-of-focus distance. According to the method provided by the embodiment of the invention, the problem that positive and negative defocusing directions cannot be distinguished through a single-frame image in a phase shift detection automatic focusing technology based on image monochromatic double-hole modulation is solved, and the problem of time waste caused by high-speed switching of an illumination light source in an automatic focusing technology based on red-green double-LED illumination is solved; and meanwhile, the problem that a phase shift detection automatic focusing technology based on pupil segmentation images is relatively small in focusing range is solved.

The invention discloses a multi-degraded image restoration method based on semantic guidance. According to the method, for degraded images captured by a vehicle-mounted camera under severe weather conditions, potential features of the images are extracted by adopting a trunk network based on Transform, multi-modal semantic information is extracted in combination with a CLIP visual language model, and semantic guidance is provided for different degradation types through a dynamic text prompt generation mechanism. A self-adaptive feature fusion module is designed, channel attention and space attention mechanisms are combined to realize effective integration of multi-modal features, and a degradation feature extraction and fusion module is introduced to enhance the generalization ability of the model. According to the semantic guidance multi-type image recovery network SGIRN provided by the invention, the global modeling capability of the Transform and the cross-modal representation capability of the CLIP visual language model are combined, so that high-quality recovery of various weather degradation types is realized.

Provided are a camera-array-based intelligent measurement and control method and system for an ultra-high bridge tower (1). The system is provided with a camera array monitoring module, an image capture and processing module, a monitoring apparatus, a bridge tower (1) status evaluation and prediction module, and a bridge tower (1) intelligent control and early warning module. Targets (3) are vertically arranged on a bridge tower (1), and an array camera monitors the real-time displacement of each target (3) across the full height range of the ultra-high bridge tower (1); the monitoring apparatus acquires environmental parameters and stress data; a bridge tower (1) structure status model is established in the bridge tower (1) status evaluation and prediction module to predict the future alignment change trend of the bridge tower (1); and the bridge tower intelligent control and early warning module performs real-time early warning and control on the bridge tower (1). Thus, all-weather automatic monitoring and intelligent control of the alignment of the structure of a bridge tower (1) are realized, thereby effectively improving the measurement efficiency, reducing the labor costs, ensuring the accuracy and reliability of measurement results, and thus enhancing the construction quality of the bridge tower (1).

A georegistration (a.k.a. georectification) of an image captured by a camera in an aerial vehicle, such as a satellite, is based on identifying multiple features using descriptor sets, and sending to a ground station only the descriptors of the identified features and the associated locations in the captured image, without sending of the captured image itself, thus requiring a low communication bandwidth. Using a database of geosynchronized reference images, the ground station uses the received descriptors sets and the associated image locations to localize the features on a selected geosynchronized reference image from the database, and forms a mapping function that map any locations in the captured image to geographical coordinates on Earth. The mapping may be used to geosynchronize an additional feature identified in the aerial vehicle, or to geo synchronize a region that may be cropped from the captured image and sent to the ground station.

The invention relates to the technical field of oral health monitoring, and discloses a real-time oral health monitoring system based on an intelligent imaging technology. The system comprises an image capture module, a feature analysis module, a health parameter calculation module, an anomaly detection module and a feedback control module. The image capture module captures dynamic image data in the oral cavity in real time and transmits the data to the feature analysis module; the feature analysis module preprocesses the image data, identifies key oral cavity structural features and generates a structured feature data set; the health parameter calculation module quantifies related health parameters based on the data set and integrates the related health parameters into an oral health parameter set; the anomaly detection module identifies the oral cavity area with the parameter value deviating from the normal range and outputs an anomaly detection result set; and the feedback control module generates optimization suggestions according to the optimization suggestions and drives the user interface display module to output a visual report. According to the system, real-time monitoring, automatic analysis and timely feedback of oral health are realized, and the monitoring efficiency and accuracy are improved.

The invention relates to the technical field of image quality optimization, and discloses a hunting camera imaging quality optimization method based on multimode data fusion. The method comprises the following steps: acquiring a visible light image, a thermal infrared image, a laser ranging point cloud and inertial measurement unit data, and carrying out space-time alignment and registration to form synchronous multimode data. A controllable imaging parameter set and scene context description information are separated by performing joint feature extraction on synchronous data. And for each parameter to be optimized, the system retrieves a plurality of candidate strategies from the imaging optimization knowledge base by taking the current value and the scene context as query conditions, selects an optimal strategy through fusion decision calculation, and finally generates and executes a global imaging parameter optimization instruction set to control a camera to complete image capture. According to the method, intelligent optimization of imaging parameters based on depth scene understanding is realized, and the image quality and adaptability of the hunting camera in a complex environment are improved.

The invention relates to the field of heliostat calibration, and discloses a scheduling method for calibrating heliostats through cooperation of artificial light sources and cameras in a large-scale heliostat field, and the method comprises the following steps: determining the heliostat range covered by each camera, determining the heliostat range covered by each artificial light source, and determining the heliostat range covered by each camera; the method comprises the following steps: determining the center and size of a light spot in an image shot by a camera when the heliostats are calibrated, determining the heliostats which can be calibrated in the coverage range of each artificial light source-camera combination, regarding each effective calibration combination as a node to construct an undirected graph, dividing the nodes into a plurality of groups through a graph grouping algorithm, and ensuring that the nodes in the same group are not connected with each other, therefore, all combinations in the same group can execute calibration in parallel. According to the method disclosed by the invention, the problem of affiliation misjudgment caused by light spot interference between heliostats is solved, possible shadow and shielding problems are avoided, dynamic scheduling of the artificial light source and the camera is realized, and invalid operation time is shortened.

The invention belongs to the technical field of image processing, and particularly discloses a low-light environment image adaptive enhancement method based on unmanned aerial vehicle inspection, which comprises the following steps: preprocessing a low-light RGB image shot by an unmanned aerial vehicle; the preprocessed low-light RGB image is converted to an HSV color space, and brightness and color components are obtained through separation; performing dual-channel attention mechanism processing: respectively optimizing brightness and color components by adopting a brightness branch and a color branch; wherein the brightness branch enhances the dark part contrast through a convolutional network, and the color branch suppresses a color channel with significant noise based on a channel attention mechanism; and reconstructing the optimized color and brightness components into an HSV color space, and converting the HSV color space into an RGB image format to realize brightness enhancement and color denoising. The problems of overexposure, color distortion, excessive noisy points and the like existing in the image shot by the unmanned aerial vehicle camera in the low-light environment in the prior art can be effectively solved, and the image quality under the night or weak light condition is improved.

The present disclosure is directed to apparatuses, system, and methods for rotating an operator station relative to another portion of an agricultural machine, such as a frame of the agricultural machine, to facilitate transport of the agricultural machine from one location to another. Transportation of the agricultural machine in this may include moving the agricultural machine in a direction that is conventionally considered to be the reverse direction of the agricultural machine. The operator station may include a cab of the agricultural machine or a portion of the cab, and the operator station may include one or more displays for displaying one or more images captured by an image capture device that is directed rearwardly of the agricultural machine.

A method for training a prediction model to generate a high-resolution image representing defects on a substrate from a low-resolution image of the substrate. The method includes inputting a first image and a reference image of defects on a substrate, which are representative of images captured using different image capture conditions, to a neural network. The neural network is executed to generate a predicted image in response to the first image. A loss function that is indicative of a difference between a defect distribution in the predicted image and a defect distribution in the reference image is calculated and the neural network is modified based on the loss function. The neural network may be trained until the loss function is minimized.

The invention discloses a vehicle-mounted head-up display system, a vehicle and a vehicle display driving method, and relates to the technical field of vehicle-mounted display.The vehicle-mounted head-up display system comprises a first display module and a second display module; the first display module is configured to project an image shot by the streaming media rearview mirror system out of a windshield in front of a driver to form virtual image display, and the second display module is configured to project head-up display content out of the windshield in front of the driver to form virtual image display. Therefore, the head-up display content and the image shot by the streaming media rearview mirror system form an integrated vision field, and a driver can simultaneously watch the front road, the head-up display content and the image of the streaming media rearview mirror system without shifting the sight line and the focal length, so that the driving safety is greatly improved; and a fused and coherent virtual image picture can be constructed, so that the driving experience is improved.

The application provides a three-dimensional profile synchronous scanning measurement method of a lens module, and relates to the technical field of lens detection. The method comprises the following steps: constructing a three-dimensional profile measurement system, and measuring a lens module to be measured based on the three-dimensional profile measurement system; calibrating a line confocal sensor to determine the corresponding relationship between the actual position of a lens in the lens module and the corresponding point in the light strip profile image captured by a high-speed camera assembly; adjusting the distance between the optical measurement module and the lens module to be measured to a focusing position through a Z-axis motion assembly; driving the lens module to be measured to move according to a planned path through an XY motion assembly, and simultaneously acquiring the light strip profile image of the entire lens module through the high-speed camera assembly; and performing data processing on the light strip profile image of the entire lens module to generate a three-dimensional profile image of the lens module. The application effectively solves the requirement of fast and accurate measurement of the three-dimensional profile of the lens module.

The invention belongs to the technical field of pin inserting machines, and particularly relates to a pin inserting machine for a lower cover of an optical filter switcher. The loading part is arranged in the object carrying part and used for loading the inserted cover shell; a transfer member; a material shaking part; and the loading part comprises an air pressure controller. According to the device, the probe can be inserted into the lower cover of the optical filter switcher in a modernized processing mode, however, after the probe is inserted, whether the probe is stably inserted into the lower cover cannot be known, and if the insertion point of the lower cover is damaged or the downward pressing force is not large enough, the inserted probe is loosened, so that the probe cannot be stably inserted into the lower cover. According to the technical scheme of the utility model, after each probe is inserted, the sliding disc automatically shakes for a period of time, and the stability of the probes is judged according to pictures collected by the storage shell, so that the probes which are not firmly inserted are eliminated in time, and the problem that the quality of processed products is reduced due to the reduction of the conductivity of the whole switcher is avoided.

A system and method are disclosed for training a machine learning model configured to determine a traversability of a real-world environment by a mobile robot based on an image of the real-world environment. The method advantageously generates high quality synthetic training data for training a traversability detection model to discriminate between traversable and untraversable regions in images captured of a real-world environment. The synthetic training data is generated through simulation of a virtual robot in a virtual environment. Once the traversability detection model is trained, it can be deployed to the mobile robot for the purpose of predicting traversability of a real-world environment.

An image capturing apparatus that realizes both of efficient heat dissipation and size reduction of an image capturing apparatus body. The image capturing apparatus includes an imaging board having an image sensor mounted thereon, a main circuit board that has heat generating devices mounted thereon and is arranged substantially in parallel to an imaging surface of the image sensor on a rear side of the imaging board, a duct unit disposed on a rear side of the main circuit board, and a cooling fan that draws outside air into the duct unit. The duct unit has a duct base opposed to the main circuit board and disposed substantially in parallel to the main circuit board, and a duct plate disposed obliquely to the duct base at a predetermined angle. The cooling fan is mounted on the duct plate.

An agricultural system includes one or more processors and memory storing instructions executable by the one or more processors. The instructions, when executed by the one or more processors, configure the agricultural system to: obtain an image captured by an imaging device during an agricultural operation performed by an agricultural work machine, the image indicating a characteristic; segment the image to generate a segmented image having a plurality of cells and selectively size each cell of the plurality of cells according to one or more cell size criteria; determine one or more values of the characteristic based on the segmented image; and control an operating parameter of the agricultural work machine based, at least, on the one or more values of the characteristic.

The invention relates to an unmanned aerial vehicle navigation decoy defense method based on visual map matching, and solves the technical problems that a traditional relative positioning mode is easy to cause positioning drift and an unmanned aerial vehicle is easy to decoy and interfere under long-term and large-scene conditions. The absolute positioning method based on the scene coordinate regression network is adopted, positioning drift is avoided in mechanism, and the scene coordinate regression network based on deep learning is used for performing positioning information mapping on the visual image and the scene coordinates. And inputting a visual image shot by the unmanned aerial vehicle into the optimized scene coordinate regression network to obtain a scene coordinate predicted value, and calculating the scene coordinate predicted value by using an RANSAC algorithm to obtain a pose estimation result. And comparing the pose estimation result with global navigation satellite system (GPS) positioning information received by the unmanned aerial vehicle to complete positioning signal decoy recognition, so that the unmanned aerial vehicle can effectively defend against GPS decoy signals.

An image capturing apparatus includes a first circuit board mounted with an image sensor, a second circuit board that is fixedly connected to the first circuit board and supplies power to the first circuit board, a main circuit board that supplies power to the second circuit board, a flexible circuit board connecting the main circuit board and the second circuit board, wherein the main circuit board supplies a first voltage higher than a voltage at which the image sensor operates to the second circuit board via the flexible circuit board, and the second circuit board includes a stepdown circuit that converts the first voltage into a second voltage lower than the first voltage and outputs the second voltage to the image sensor, and the stepdown circuit is a DCDC converter including an inductor.

A head-worn augmented reality (AR) device system includes cameras, display devices, and processors, along with a memory that stores specific instructions. When these instructions are executed by the processors, they enable the device to perform several operations. First, the device accesses a two-dimensional (2D) camera image taken by its camera. The device then generates a first set of three-dimensional (3D) tracked points using the device's odometry system applied to this 2D image. Optionally, a second set of tracked 3D points is created based on one or more images captured by the camera. These 3D points are projected onto the 2D camera image to create a sparse depth image. Finally, this 2D camera image, along with the newly formed depth image, is fed into a first machine learning model to generate a metric depth estimation.

The invention discloses an aircraft skin assembly control method and system, and the method comprises the following steps: segmenting a skin and a frame from an image based on a skin surface image through employing a deep learning segmentation model; registering and splicing the images captured by different spectrum cameras to obtain a complete skin image; based on the complete skin image, using a region expansion method to identify and track a skin surface deformation region; preprocessing the original skin model through a finite element method to obtain a corresponding grid model; estimating stress distribution in the current assembly state based on the force haptic image and a stress transfer model; and based on the deformation area and the stress distribution, generating an assembly path and a pressure adjustment instruction by adopting a self-adaptive control strategy, and controlling the assembly process. Through fusion of a multispectral vision technology and a high-precision force tactile perception technology and combination of a deep learning algorithm and a nonlinear mechanical model, accurate identification of skin surface features and accurate positioning of a deformation area are realized, and the assembly control precision is improved.

Implementations set forth herein relate to triaging of robots at an industrial facility using generated snapshots that can be based on an operating status of various features of each particular robot. The snapshots can be generated from data generated by each robot and / or data observed for a particular robot, such as images captured of a particular robot. Each snapshot can be utilized to estimate a likelihood or probability that each robot can complete the task without first visiting a service station. When a probability for a particular task is satisfied, the robot predicted to complete the task can be assigned to the particular task. Alternatively, or additionally, when certain estimated probabilities do not satisfy a threshold for completing the task, each robot associated with such an estimated probability can be directed to a service station of the industrial facility.

An imaging system according to an embodiment includes an imaging unit configured to image a subject, a viewing angle acquiring unit configured to acquire viewing angle information of the subject using an image captured by the imaging unit, a subject information acquiring unit configured to acquire, using the image, position information and posture information of the subject and position information of an object present in a visual field region of the subject, and a region calculating unit configured to calculate, based on the viewing angle information, the position information, and the posture information of the subject and the position information of the object present in the visual field region, at least one of a dead angle region that cannot be grasped by the subject or a visual recognition region that is a region obtained by excluding the dead angle region from the visual field region.

The present application discloses an image capture method for an image capture system, a handheld gimbal, and an unmanned aerial vehicle. The method comprises: acquiring a tracking activation instruction for instructing activation of multi-target tracking; on the basis of the tracking activation instruction, identifying a plurality of target objects in an image captured by an image capture device, and tracking the plurality of target objects in the captured image; and on the basis of tracking information of the plurality of target objects, automatically adjusting the orientation of the image capture device and / or image capture parameters of the image capture device, such that the plurality of target objects remain within the captured image. Tracking and image capture can be activated on the basis of an acquired tracking activation instruction, and in group tracking scenarios such as group dancing, group photos, or stage performances, the orientation and / or image capture parameters of an image capture device can be automatically adjusted on the basis of tracking information of a plurality of target objects in an image captured by the image capture device, such that the plurality of target objects remain within the captured image, thereby effectively improving multi-target tracking and image capture performance in group tracking scenarios.

The invention discloses inductive control glasses for human body signal recognition and an inductive control method. The inductive control glasses specifically comprise lenses, a glasses frame, an image capturing module, a central module and a battery, the lenses are buckled and connected in the spectacle frame of the spectacle frame; the image capturing module, the central module and the battery are arranged on the spectacle frame according to preset positions; a wire led out from the central module is electrically connected to the two ends of the lens; the image capturing module and the battery are electrically connected to the central module. According to the invention, the image capturing module and the automatic focusing are integrated, and automatic focusing can be realized according to the human body signal, so that the user experience is improved.