102 results about "Visual Disparity" patented technology

The invention discloses a vehicle-mounted HUD optical performance comprehensive detection method, and aims to solve the problems that a traditional detection method depends on manual operation and is low in detection efficiency and large in error. Project loading and test starting, picture height position calibration, multi-parameter test, brightness and chromaticity test, ghosting test, static distortion test and binocular parallax test items are automatically implemented through detection equipment, and accurate control (such as application of camera pixel-millimeter ratio) of advanced image recognition algorithm and equipment in the test process is carried out. The vehicle machine controls the step number of the curved mirror motor, and the industrial personal computer controls the motion module, so that high-precision measurement of optical parameters is realized, and the test efficiency and the test accuracy are improved.

The present invention relates to a vehicle control method, a binocular stereoscopic vision-based road unevenness feature detection method, system and device, and a computer readable storage medium. The binocular stereoscopic vision-based road unevenness feature detection method comprises the steps: S1, road surface unevenness feature detection; S2, generating a binocular disparity point cloud; S3, feature region point cloud projection; and S4, region feature calculation: calculating unevenness information of a corresponding region point cloud. The vehicle control method comprises the steps: T1, performing ego-vehicle trajectory prediction on the basis of the motion state of the vehicle; T2, on the basis of the predicted ego-vehicle trajectory, calculating a degree of correlation to a corresponding region point cloud; and T3, sending to an ADAS controller the concavity and convexity and height position of the corresponding region point cloud and the acquired degree of correlation as summarized information of each road unevenness feature. The present invention can effectively identify road unevenness information, reduce calculation resource consumption, and thus improve the comfort of intelligent driving.

A method for calibrating a binocular camera calibration parameter, an apparatus, a computer device, and a medium. The method comprises: acquiring a binocular disparity map and / or a binocular depth map; performing epipolar alignment detection on the binocular disparity map and / or the binocular depth map to obtain an epipolar alignment error; and on the basis of the epipolar alignment error and a first preset threshold, determining a target calibration parameter to be calibrated of a binocular camera. In the solution, in the process of calibrating the binocular camera calibration parameter, the epipolar alignment error is obtained by performing epipolar alignment detection on the binocular disparity map and / or the binocular depth map, and the target calibration parameter of the binocular camera is determined on the basis of the epipolar alignment error, thereby not only simplifying the calculation of the epipolar alignment error but also improving the precision of the epipolar alignment error, thus enhancing the accuracy of calibration of the calibration parameter.

The invention discloses an anti-jitter data acquisition and obstacle recognition method for a rail transit vehicle, and relates to the technical field of rail transit vehicle data acquisition and processing, continuous four frames of original images are acquired through a binocular camera by adopting synchronous and staggered shooting, and global motion estimation is performed in combination with acquired sensor data; local pixel level alignment is realized by using a preset image layer and pixel grid division, a stable image is generated through weighted fusion, local features of the image are optimized based on a multi-head attention mechanism, the stable image is mapped to a high-dimensional vector space by using a depth encoder, motion correction information is fused, and finally, the motion correction information is obtained. A deep reconstruction network is combined with binocular parallax to generate a three-dimensional point cloud, three-dimensional coordinate changes are monitored in real time, obstacle recognition and early warning are achieved, high-quality images can be obtained in the violent vibration environment of a vehicle, the data acquisition precision and the real-time performance and accuracy of obstacle detection are improved, and the operation safety of rail traffic is effectively guaranteed.

The invention discloses an integrated three-dimensional space modeling method based on Gaussian splashing and a three-dimensional point cloud. The method comprises the following steps: step 1, converting a scene multi-view image and depth data into the three-dimensional point cloud; 2, constructing a point sequence input set; step 3, inputting the point sequence input set into a PointMamba model; 4, performing Gaussian parameter initialization, performing scale expansion on a local sparse region, and performing direction constraint on a global key region; 5, obtaining a prediction depth map through a binocular parallax matching algorithm, calculating a rendering depth map of the initial Gaussian point set, and adjusting the optimization intensity; and step 6, executing an adaptive encryption operation, and outputting a three-dimensional space model. According to the method, high-precision three-dimensional reconstruction of a complex scene is realized, and the method is suitable for scenes needing high-density point cloud modeling and semantic understanding, such as building scanning, industrial detection and virtual reality.

The embodiment of the invention provides an audio and video alignment method, an audio and video understanding method, electronic equipment, a storage medium and a computer program product. The alignment method comprises the following steps: performing feature extraction and coding processing on a target audio to obtain a first audio feature sequence, and performing block feature extraction and coding processing on a target video to obtain a first video feature sequence; performing visual dynamic enhancement coding processing on the first video feature sequence based on the visual difference of the cross-frame image blocks to obtain a second video feature sequence; performing time sequence information injection on the second video feature sequence and the first audio feature sequence to obtain a third video feature sequence and a second audio feature sequence; and performing cross-modal alignment fusion processing on the third video feature sequence and the second audio feature sequence to obtain an aligned video feature sequence and an aligned audio feature sequence. According to the method, the visual dynamic change corresponding to the audio event can be accurately captured, and the alignment accuracy of the audio and the video is improved.

The invention discloses an image processing method and device and a storage medium, and belongs to the technical field of shooting. The method is applied to the electronic equipment comprising a plurality of cameras, the plurality of cameras comprise a reference camera, and the method comprises the following steps: under the condition that a first camera is called for shooting, determining a first white point of a current frame shot by the first camera and a first environment color temperature of a shooting environment corresponding to the current frame; according to an environment color temperature difference between the first environment color temperature and a second environment color temperature of a shooting environment corresponding to a memory frame of a reference camera, carrying out fusion processing on white point information of the first white point and white point information of a second white point of the memory frame to obtain white point information of a third white point; and performing white balance correction on the current frame according to the white point information of the third white point. Therefore, the color difference of the images shot by other cameras and the reference camera in the same scene and the visual difference caused by the color difference can be reduced, and the color consistency of multi-camera shooting in the same scene is improved.

The invention discloses a binocular disparity map obtaining method, device and system for a small target, and is used for improving the disparity optimization effect of the small target, and the method comprises the steps: carrying out the hierarchical feature extraction of a left view and a right view, obtaining a feature map, compressing the feature map to 32 channels through the convolution operation integrated with the structure information, and obtaining a binocular disparity map; therefore, an initial splicing body is constructed; obtaining attention weights of the stereo images corresponding to the left view and the right view, screening the initial splicing bodies by using the attention weights, and constructing a cost body according to a screening result and initial parallax loss; fusing the convolutional context information with the preliminarily aggregated intermediate features to perform cost aggregation on the cost body to obtain an aggregation result; and adopting a self-adaptive multi-modal cross entropy loss function to supervise an aggregation result, and performing multi-modal output on the supervised aggregation result through a multi-modal parallax estimator to obtain a binocular parallax graph of the left view and the right view.

The invention belongs to the technical field of underwater target space positioning, and discloses an underwater benthos rapid positioning method and system based on binocular vision, and the system comprises an underwater environment data binocular camera collection device which carries out the image enhancement processing of collected image data; inputting the enhanced image into a lightweight Slim-RT-DETR target detection network, and carrying out the detection and recognition of a fishing object; cutting target areas of left and right views of the binocular camera based on a target detection and recognition result, and calculating a parallax value by adopting an anti-noise optimization method of random sampling and neighborhood interpolation; and calculating three-dimensional space coordinates of the fishing object according to the binocular parallax and the re-projection matrix, and positioning the underwater benthos. According to the invention, a rapid image enhancement algorithm and a target detection model suitable for underwater fishing are designed, a normal form method suitable for underwater biological positioning is provided, and high-precision and high-real-time underwater fishing object positioning can be realized.

The invention belongs to the field of computer graphics, and discloses a self-adaptive LOD subdivision method and system oriented to virtual reality and based on a perceptual model. The method comprises the following steps: firstly, researching factors influencing visual perception, establishing a perception model BD-castleCSF fused with stereoscopic vision by combining binocular parallax, and dynamically dividing a rendering region; then, on the basis of a discrete LOD initial order processing grid model, introducing a mosaic technology to execute grid subdivision, calculating a downsampling factor according to a perception model, dynamically adjusting the subdivision degree, and proposing a perception adaptive subdivision LOD algorithm; experimental results show that compared with a traditional static rendering method, the LOD switching frequency of the method is reduced by 67%, the effective subdivision triggering rate is improved by 19.9%, the redundant triangular surface compression rate is improved by 11.9%, and the frame time standard deviation is reduced. Therefore, the method can dynamically and smoothly adjust the model details under different watching conditions, guarantees the consistency of visual effects, and remarkably improves the rendering efficiency. According to the method, an efficient and adaptive rendering solution is provided for the field of virtual reality.

Embodiments of the present application relate to a method and system for three-dimensional modeling of a train based on a binocular disparity prediction model, the method comprising: setting installation, acquisition and splicing rules of double parallel linear array cameras; setting a binocular disparity prediction model; acquiring first data set based on the installation, acquisition and splicing rules; training the binocular disparity prediction model based on the first data set; installing the double parallel linear array cameras after the training; when a train passes through the two installed cameras on the current train track, acquiring and splicing images to obtain images I1 and I2, inputting the images I1 and I2 into the binocular disparity prediction model for prediction to obtain a disparity map D 1‑2 , and based on I1, I2 and D 1‑2 , the present application can improve prediction accuracy in high-speed scenes and complex lighting environments, effectively overcome the limitations of a single perspective, and improve the integrity of three-dimensional reconstruction of edge and occluded areas.

The application discloses a binocular disparity map acquisition method, device and system for small targets, which is used for improving disparity optimization effect of small targets. The method comprises the following steps: performing hierarchical feature extraction on left and right views to obtain a feature map, compressing the feature map to 32 channels through a convolution operation with structure information, and constructing an initial splicing body; obtaining attention weights of corresponding stereo images of the left and right views, screening the initial splicing body by using the attention weights, constructing a cost volume according to a screening result and an initial disparity loss; fusing convolution context information and intermediate features after preliminary aggregation to aggregate the cost volume, and obtaining an aggregation result; supervising the aggregation result by using an adaptive multi-modal cross-entropy loss function, and performing multi-modal output on the supervised aggregation result by using a multi-modal disparity estimator, so as to obtain binocular disparity maps of the left and right views.

The invention belongs to the technical field of grain quality detection, and particularly discloses a grain quality detection device which comprises a detection vehicle, an oblique insertion flow guide sampling barrel, an alternate lifting rolling bearing type dyeing mechanism, a quality highlighting device and a paving observation device. According to the difference between surface structures and internal pore structures of wizened grains and full grains, the difference can cause different adsorption capacities of the wizened grains and the full grains when the wizened grains and the full grains are in contact with dyes, and the difference can be presented in a visual color difference form by dyeing the grains, so that the quality of the grains can be conveniently distinguished; the method for distinguishing the grain quality based on the visual difference after dyeing has the advantage of being high in intuition, and the grain quality can be rapidly judged under the condition that complex instrument analysis is not needed.

The application discloses a feature extraction method and system based on difficult sample mining and multi-granularity division, which is used for a cross-view geographical image retrieval task. The method first preprocesses cross-view street view images and satellite images, and uses a generative model to generate cross-view images, reducing the visual difference between different view images. Then a two-stage difficult sample mining model is constructed, including a sampling strategy based on geographical location and visual similarity, mining difficult negative samples in different ranges, and enhancing the inter-class discrimination ability. Then a multi-granularity feature division module is introduced, the image features are extracted through a ResNet50 backbone network, and the features are divided and fused according to different granularities to obtain rich and robust view-invariant feature representation. Finally, the satellite image to be retrieved is input into the trained feature extraction model, the features are extracted, and similarity matching is performed with the street view image library to obtain the cross-view retrieval result.

The invention relates to the technical field of robot sensors, in particular to a visual tactile sensor based on color block mark array optimization and a three-dimensional reconstruction method, and the method comprises the steps: enabling four colors with remarkable visual difference to correspond to two patterns with different sizes to form eight digital identifiers, a binocular vision camera is used for collecting a color block array on the surface of an elastomer, on the basis of completing three-dimensional calibration and image preprocessing, the color block characteristics of mark points and neighborhood distribution of the mark points are used as constraints, displacement distance screening, line positioning and inter-frame movement continuity are combined, rapid matching and dynamic tracking of the mark points are achieved, and the accuracy and the accuracy of three-dimensional calibration are improved. According to the method, the spiral search traversal frequency is remarkably reduced, the system processing time is shortened to about 270 ms from about 320 ms through array structure design, matching strategy optimization and sensor structure miniaturization cooperation, high-precision reconstruction is guaranteed, meanwhile, the real-time performance and robustness are improved, and the method is suitable for complex operation scenes such as industrial robot precision assembly and flexible grabbing.

This application belongs to the field of virtual art restoration technology, specifically providing a deep learning-based virtual art restoration system. The system primarily analyzes historical images and environmental data of the artifact and restoration materials to train an aging prediction model and simulate long-term appearance changes. By quantitatively comparing the differences and stability of their aging trajectories, it calculates the long-term compatibility score for each restoration material and automatically selects the optimal material to generate the final virtual restoration image. This application effectively solves the problem in existing technologies where it is difficult to predict significant visual differences that may appear between restoration materials and the artifact itself after long-term natural aging, ultimately leading to insufficient durability of the restoration results. It achieves scientific prediction and optimized selection of the long-term visual compatibility of restoration schemes, significantly improving the reliability and stability of restoration results and reducing the risk of restoration failure due to material aging incompatibility.

This application relates to a method for identifying wildfire hazards based on a spatial-temporal correlation operator using visual language priors. The aim is to address the problems of high false alarm rates and difficulty in early identification of concealed fires in vision-based power transmission line wildfire monitoring methods under complex backgrounds. The method constructs a multimodal fusion tensor for the current frame based on visible light and infrared images of the target area. It then uses a visual language prior module to extract features from the multimodal fusion tensor and meteorological data to obtain semantic feature vectors and semantic credibility. Finally, it uses a spatial-temporal correlation module to obtain a spatial-temporal evolution feature vector based on the multimodal fusion tensor, historical time-series cache queue, binocular disparity map, and the semantic credibility. Finally, it uses a spatial-temporal correlation operator to fuse the semantic feature vector, the semantic credibility, and the spatial-temporal evolution feature vector to obtain the probability of wildfire hazard risk.

The embodiment of the invention provides a task execution method and device, electronic equipment and a storage medium, and relates to the technical field of robotics.The method comprises the steps that a view pair collected through a binocular camera is obtained, and a first monocular geometric feature of a left view of the view pair and a second monocular geometric feature of a right view of the view pair are extracted; determining similarity characterization values of the first monocular geometric feature and the second monocular geometric feature under the plurality of different parallax characterization values, and obtaining binocular parallax features including the plurality of different parallax characterization values and the similarity characterization values; taking at least one view in the view pair as a target image, and extracting visual semantic features from the target image; performing feature fusion based on the binocular parallax features and the visual semantic features to obtain fused features; and controlling the robot to execute the task for the task object based on the fusion feature and the task description instruction. According to the scheme provided by the embodiment of the invention, the task execution accuracy can be improved.

The invention belongs to the technical field of energy intelligence, and provides a regional integrated energy system multi-level agent construction method, which comprises the following steps: carrying out space-time alignment processing on real-time operation data to obtain a synchronous data stream; respectively mapping parameters in the synchronous data stream into specific visual attribute primitives to obtain a multi-level state semantic image; performing visual semantic segmentation on the multi-level state semantic image to obtain visual semantic units, and constructing a spatial correlation map between the visual semantic units; performing visual difference comparison on the spatial correlation map and a historical same-period spatial correlation map to obtain an abnormal visual semantic unit; performing multi-frame tracking verification on the abnormal visual semantic unit, and integrating an abnormal unit identifier and an abnormal type into an abnormal event report; and carrying out hierarchical decision making on the abnormal event report and the spatial correlation graph to obtain a collaborative regulation and control instruction. According to the invention, the efficiency of cooperative management of the regional integrated energy system can be improved.

The invention provides an intelligent nuclear power embedded part detection and acceptance method based on a binocular camera, belongs to the technical field of detection and acceptance, is an intelligent detection algorithm based on binocular vision and a deformable attention mechanism, and aims to break through the technical bottleneck of three-dimensional positioning and multi-scale feature fusion of embedded parts in a complex scene. A three-dimensional pyramid network with reserved features is designed, multi-scale features are fused in a layered mode, binocular geometric consistency is kept, and the problem of information loss in cross-resolution feature fusion is effectively solved; parallax perception position coding is innovatively introduced, binocular parallax information and two-dimensional position coding are deeply fused, and the three-dimensional space perception capability is enhanced; a non-parametric anchor-based target query mechanism is provided, query embedding covered by dense space is generated by multiplexing low-resolution three-dimensional features, the model convergence efficiency and the detection precision are improved, the detection cost is reduced, the detection efficiency is improved, and the detection safety is guaranteed.

A vision-enabled fluid flow rate detection system is disclosed. The system includes an image sensing device that images an infusion container supplying a fluid to an infusion pump and a pattern of markings associated with a surface of the container, and identifies a visual difference in the pattern from a default state of the pattern to determine a volume of fluid infused from the infusion container, and used to calculate a volume of fluid in the container and a flow rate of the fluid. When the amount of fluid, as determined by the vision system, differs from the amount reported by the pump, the pump's motor may be adjusted to correct the expected volume or flow rate, as needed. When a severe infusion inaccuracy is detected, an alarm or other indication may be provided, or the infusion terminated.

The application discloses a kind of calibration method and device of vehicle-mounted surround view camera, storage medium and vehicle.The method comprises: in response to calibration trigger information, the original external parameter of the vehicle-mounted surround view camera is acquired;Determine the homonymy point on the original image corresponding to adjacent two cameras in the vehicle-mounted surround view camera respectively;Calibration processing is carried out based on the original external parameter and the homonymy point on the original image, and the external parameter matrix of the camera in the vehicle-mounted surround view camera is obtained;Based on the external parameter matrix, the original image collected in the vehicle-mounted surround view camera is spliced, so that the change of surround view fisheye camera external parameter caused by load, tire pressure change can be effectively solved, and the visual difference of splicing misplacement can be solved positively.

The invention belongs to the crossing field of information hiding and digital image processing, and particularly relates to an image steganography method and system based on frequency domain transformation and a reversible neural network, and the system comprises a wavelet transformation module which is used for executing frequency domain decomposition of a carrier image, a secret image and a steganography image, the inverse wavelet transform module is used for executing reconstruction from frequency domain features to pixel domain images, the reversible network is used for realizing reversible conversion of 24-channel fusion features, the reversible network is formed by sequentially connecting 16 GlowBlocks in series, and each GlowBlock comprises an activation standardization layer, a reversible 1 * 1 convolution layer and an affine coupling layer which are cascaded. According to the method, through the cooperation of the frequency domain feature hierarchical modeling and the reversible network, while the steganography reversibility is ensured, the visual difference between the steganography and the original carrier is remarkably reduced, the recovery precision of secret information is improved, and the method is suitable for image steganography scenes with high requirements for reversibility, concealment and recovery quality.

This application discloses a disparity correction method, apparatus, electronic device, storage medium, and product. The method includes: determining an initial disparity map between a left and right image acquired by a binocular camera based on a binocular matching disparity network; determining matching pixel pairs between the left and right images based on the initial disparity map; for each matching pixel pair, traversing all disparities within the disparity neighborhood centered on the initial disparity corresponding to the matching pixel pair, and calculating the aggregation cost of the target pixel within a preset-sized pixel window in its target image based on the current disparity; determining the target disparity corresponding to the target pixel based on the aggregation cost corresponding to each disparity in the disparity neighborhood, and correcting the corresponding initial disparity in the initial disparity map based on the target disparity to generate a target disparity map. This solution not only effectively ensures the robustness of deep learning-based matching disparity but also improves the accuracy of binocular visual disparity.

In various examples, visual differences between video data and an encoded version of the video data are used to determine updates to quantization parameters (QPs) used to encode the video data to store or upload video clips that corresponds to notable events associated with a machine. The video data may correspond to images applied to a machine learning model to perform control operations for the machine. A metric may be used to quantify the visual differences. To evaluate the visual differences, samples of the video data and encoded video data may be determined and analyzed, rather than entire images or frames. To selectively enable updates to QPs, the system may detect that a deviation between a bitrate corresponding to the encoded data and a reference bitrate has exceeded a threshold.

A dynamic bitrate allocation method and system based on AI multi-token prediction, which relates to the field of image communication. In this method, the light and dark alternating regions are determined; the light and dark alternating regions are divided into multiple coding units; the visual saliency of each coding unit is calculated to construct an attention propagation map; the visual association strength between coding units is determined; the bitrate sharing groups are clustered; the dominant units are selected within each bitrate sharing group, and the visual difference degree is determined; the bitrate sharing coefficient is established according to the visual difference degree; the transition regions are identified; the spatial redundant bitrate of the transition regions is transferred to the visual key regions with visual saliency greater than the preset saliency to obtain an optimized bitrate allocation scheme; and the high dynamic range video frames are encoded according to the optimized bitrate allocation scheme. This application is used to improve the accuracy of allocating bitrates that adapt to the human eye perception characteristics in scenes with drastic light and dark alternations, thereby improving the subjective quality of the encoded video.

The invention discloses a quantitative evaluation method, system and device of a stereoscopic visual function and a storage medium, and belongs to the technical field of visual function detection and evaluation, the method comprises the steps that interactive response of a user to a visual stimulation image is continuously acquired, and the image comprises a target stimulation object and at least one interference stimulation object; according to the response result, adopting an adaptive algorithm to iteratively update the binocular parallax parameter until the change trend meets a preset convergence condition, and outputting a first binocular parallax parameter; and calculating and outputting a stereoscopic vision sharpness threshold value according to the parameters, so that by implementing the method and the device, real binocular parallax perception and cognitive judgment based on monocular clues can be effectively distinguished, the stereoscopic vision sharpness can be accurately determined, and the accuracy, objectivity and reliability of stereoscopic vision detection are improved.

The invention provides a video key frame extraction method and device, equipment and a storage medium, and the method comprises the steps: obtaining an original frame sequence of an input video; performing content analysis on the original frame sequence, and identifying whether a user-defined attention target exists or not; if the concerned target is not recognized, selecting a key frame from the original frame sequence according to a preset extraction strategy; and if the attention target is identified, constructing a candidate frame set based on the frames containing the attention target and calculating visual difference measurement among the frames in the candidate frame set, and selecting a key frame from the candidate frame set based on the visual difference measurement and outputting the key frame. By adopting the method, the key frame extraction efficiency, pertinence and information density can be improved, and the accuracy of subsequent video analysis is further improved.

A vision-enabled fluid flow rate detection system is disclosed. The system includes an image sensing device that images an infusion container supplying a fluid to an infusion pump and a pattern of markings associated with a surface of the container, and identifies a visual difference in the pattern from a default state of the pattern to determine a volume of fluid infused from the infusion container, and used to calculate a volume of fluid in the container and a flow rate of the fluid. When the amount of fluid, as determined by the vision system, differs from the amount reported by the pump, the pump's motor may be adjusted to correct the expected volume or flow rate, as needed. When a severe infusion inaccuracy is detected, an alarm or other indication may be provided, or the infusion terminated.

The application discloses a kind of based on vision's blade spraying uniformity identification method, belong to image processing technical field.The application is under two kinds of light intensity acquisition blade image, obtain medium light image and high light image;Extract brightness and carry out pixel level difference, obtain two brightness difference graphs;Luminance difference graph is handled using filter core, and structural uneven intensity graph is generated according to maximum and minimum response value difference;Pixel classification is carried out to structural uneven intensity graph, distinguish smooth point and structural mutation point, and carry out binary and OR operation, obtain structure co-display graph, and structural extraction graph is obtained by morphological operation;According to structural uneven intensity graph, generate unevenness weight graph;Extract structural extraction graph and unevenness weight graph feature, carry out fusion splicing, and generate spraying uniformity score.The method utilizes the visual difference under different illumination conditions, realizes the high-precision identification of blade spraying uniformity.