26 results about "Vision Disparity" patented technology

The application discloses a kind of environment simulation method and system based on binocular stereo vision measurement, it is related to scene simulation technical field, and its technical solution points are: the method includes by constructing the physical imaging model in left and right optical elements in binocular stereo vision camera, and left and right view is obtained based on the physical imaging model;Determine the disparity map between the left and right view, determine scene depth image based on the disparity map;Three and more scene depth images generated by three and more different binocular stereo vision cameras corresponding to the scene are acquired, and the three and more scene depth images are fused, to construct the three-dimensional simulation model corresponding to the scene based on the depth image after fusion.The application can accurately reflect the depth level and spatial relationship of scene, and the three-dimensional simulation model finally constructed is more realistic, and can strongly support virtual reality, three-dimensional measurement and other applications.

The application discloses a kind of coal mine underground image stereo matching methods based on threshold and weight Census transformation, comprising the following steps: S1, image information is collected by two monocular camera modules binocular holder;S2, the gray value of all pixels in support window is thresholded;S3, improved center point pixel calculation method obtains matching generation value;S4, improved dynamic cross-domain obtains cost aggregation value;S5, disparity value is obtained using WTA strategy, S6, the overall process is verified on the visual system of underground unmanned auxiliary transport vehicle above-mentioned.The application applies threshold and weight Census transformation method to coal mine underground perception, realizes the autonomous obstacle avoidance and visual reconnaissance function of coal mine underground unmanned auxiliary transport vehicle, reduces the influence of factors such as dust, unstable illumination conditions on stereo matching, improves the accuracy of stereo matching.

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.

This application relates to a virtual binocular speckle stereo matching method based on local gray-level plane binary segmentation. It pertains to the fields of computer vision, 3D measurement, and stereo vision, and includes the following steps: S1: image loading and parameter settings; S2: local gray-level plane binary segmentation; S3: disparity calculation and sub-pixel optimization based on Hamming distance; S4: disparity map post-processing; sub-pixel interpolation is performed based on the matching cost curve to obtain disparity values ​​with sub-pixel accuracy, resulting in an initial sub-pixel disparity map; S5: depth map calculation and effective value filtering: the initial sub-pixel disparity map is filtered, consistency checked, and outlier removed to obtain an optimized dense disparity map; finally, combined with the calibration parameters of the virtual binocular system, the dense disparity map is converted into a depth map, and the result is visualized. This method has the advantages of high robustness, high accuracy and efficiency, and strong versatility.

This invention discloses a visibility estimation method and apparatus based on binocular vision. The method includes: simultaneously acquiring left and right views using a calibrated binocular camera; inputting the image pairs into a stereo matching deep neural network to obtain a disparity map and converting it into a depth map; inputting the left view and depth map into a dual-branch deep convolutional neural network to extract multi-scale features; fusing RGB features and depth features through a cross-modal feature fusion module; and finally outputting a visibility estimate through a feature aggregation and regression module. The apparatus includes a binocular image acquisition unit, a data processing and visibility estimation unit, and a result output unit. This invention solves the depth ambiguity problem of monocular vision by fusing binocular depth information and image appearance information, achieving high-precision, non-contact visibility surface measurement. It has the advantages of low cost and flexible deployment, and is suitable for visibility monitoring in traffic scenarios such as highways.

The application discloses a wave image matching method based on fusion matching cost, and relates to the field of stereo matching, and comprises the following steps: S1, left and right eye images of waves are collected by using binocular cameras to determine a disparity range; S2, the left and right eye images of original waves collected in the step S1 are preprocessed; S3, a matching cost value of the wave images is obtained by using an improved Census algorithm; S4, improved Census cost, AD cost and gradient cost are fused to obtain a cost space; S5, a cross-domain is used for cost aggregation; S6, a winner-takes-all (WTA) strategy is used to calculate the disparity of the wave images; S7, the disparity map in the step S6 is subjected to left-right consistency checking, and is subjected to hole filling and sub-pixel optimization processing. The application adopts the above method, solves the problem that the disparity map obtained from the wave images is prone to a large number of invalid points and mismatching points, improves the correctness of wave stereo matching, and guarantees the matching precision of the depth discontinuous region.

A method of determining binocular alignment includes measuring an uncoupling phoria of a patient at a first simulated distance by: presenting a fusible image including a target having a first disparity corresponding to the first simulated distance using a stereoscopic display of a head-mounted device at a screen distance; presenting the target having the first disparity for a first eye and presenting an uncoupling null target image for a second eye to present a non-fusible image, and measuring an uncoupling phoria in response to the presentation of the non-fusible image using an eye tracker of the head-mounted device; and including determining a vergence of the patient at the first simulated distance by: presenting a fusible image having the first disparity corrected for the measured uncoupling phoria for the first and second eyes; measuring a coupling phoria in response to the presentation of the fusible image; and determining the vergence as a combination of the uncoupling phoria and the coupling phoria.

This invention discloses a computer vision-based method for detecting the rehabilitation status of facial nerve diseases, belonging to the field of computer vision technology. The method includes: synchronously acquiring facial action sequences with binoculars under terminal guidance, performing epipolar correction and temporal labeling to obtain segmented binocular sequences; performing face localization, pose correction, and scale normalization on the segmented binocular sequences, and achieving semantic unification of the affected side, generating keypoint trajectories and functional partition semantic masks; and performing three-dimensional initialization of disparity values ​​on the segmented binocular sequences based on the keypoint trajectories and functional partition semantic masks, and constructing an anisotropic Gaussian set with partition attributes. This invention employs a three-dimensional representation and mirror comparison mechanism with functional partition attributes, which can finely quantify the differences between the affected and healthy sides at local levels such as the eye area, mouth area, and eyebrow area, improving the sensitivity and interpretability of slight functional recovery and compensatory movements.

The application discloses a binocular vision naked-eye 3D image processing method, device and system, and particularly relates to the naked-eye 3D technical field; the application defines foreground and background areas, constructs a parameter extraction and optimal value quantization calculation system covering parallax, scene and splicing dimensions, and combines preset threshold values and weight factors in multiple scenes to realize comprehensive and accurate evaluation of naked-eye 3D image quality, solve the problem of one-sidedness of traditional single-index evaluation, and accurately locate the core crux affecting the stereoscopic effect.

The application provides a face recognition and living body detection fusion method and device based on a binocular camera, and the method comprises the following steps: after collecting original left and right face images, performing binocular image preprocessing to obtain a standard binocular image pair; performing multi-scale stereo feature extraction on the standard binocular image pair to obtain a depth-texture joint feature representation and a disparity map; combining the disparity map, performing adaptive 3D face reconstruction and time sequence dynamic feature modeling, and through multi-level living body detection fusion decision and face feature extraction and matching, completing end-to-end fusion verification of face living body detection and face recognition. Through deep fusion of binocular stereo vision and deep learning, multi-dimensional living body discrimination features are constructed, and various known and unknown attacks can be effectively resisted. An adaptive stereo matching and 3D face reconstruction mechanism is designed, the unstable recognition problem caused by illumination change and posture change is overcome, and the system robustness is improved.

The application provides a binocular vision-based joint semantic segmentation and depth estimation method, which effectively alleviates the feature conflict between semantic segmentation and depth estimation through a double-branch feature extraction structure, and improves the adaptability of the model in complex scenes; based on a quality perception adaptive fusion mechanism, dynamic interaction is realized according to feature confidence, negative transfer between tasks is avoided, and the overall robustness of the model is improved; a feature-guided disparity refinement strategy is adopted to realize high-precision recovery of the boundary area, and the method has the advantages of light weight, strong real-time performance and easy deployment, and is suitable for popularization and application in embedded vision systems.

The application provides a motion target trajectory tracking and physical quantity measurement method based on binocular vision, and belongs to the technical field of computer vision and image processing, which comprises the following steps: acquiring a stereoscopic image sequence of a motion target collected by a binocular camera; in the starting frame of the sequence, identifying the motion target and determining an initial position through a target detection model; generating a tracking query vector based on the position, tracking the motion target in subsequent frames according to the vector, and outputting a continuous target region; based on pre-calibrated camera parameters, performing epipolar rectification on each frame of image in the stereoscopic image sequence to obtain left and right rectified images; based on the images, calculating the disparity in the continuous target region to obtain disparity data; according to the disparity data and the calibration parameters, obtaining the motion trajectory of the motion target; and calculating the motion physical quantity according to the motion trajectory. Through optimization of the whole process of detection tracking, binocular vision processing and physical quantity measurement, the application realizes real-time measurement of target trajectory tracking and physical quantity in a motion scene.

The application provides a road surface reconstruction method based on a direction perception pseudo binocular network, comprising a direction perception feature enhancement module: by combining road geometric features with view angle changes, multiple feature enhancement mechanisms such as a spatial saliency perception unit and an internal direction decoupling module are adopted, so that the network can effectively enhance the perception ability of the road surface geometry; a pseudo binocular cost volume construction operator is proposed: by introducing feature difference modeling and a nonlinear gating mechanism, the parallax effect in binocular vision is simulated, and a pseudo binocular disparity volume is efficiently constructed under the condition of monocular input. Therefore, the application first realizes high-precision and high-robustness three-dimensional road reconstruction under the condition of monocular input, and the innovation lies in the introduction of direction perception and pseudo binocular disparity modeling technology, which is especially suitable for complex and variable road scenes. In practical applications, the technology can be widely used in the fields of automatic driving, high-precision map construction and the like, and has wide market prospects and technical value.

The application discloses a three-dimensional reconstruction method of a secondary arc based on binocular stereo vision, which comprises the following steps: firstly, a binocular stereo vision system is built, and two high-speed cameras are used to acquire secondary arc images; secondly, an atmospheric scattering model-based defogging algorithm is used to defog and enhance the arc images, and a double-threshold value repair algorithm based on the gray scale of the secondary arc images is used to connect the discontinuous arcs; then, a semi-global stereo matching method is used to calculate the disparity of the corresponding points in a pair of images, and a weighted least square method is used to optimize the disparity map; finally, the three-dimensional coordinates of the arcs are calculated according to the triangulation principle. The application can obtain clear and continuous three-dimensional images of the secondary arcs and accurate real physical parameters of the secondary arcs, and has important significance for the subsequent research on the motion characteristics and discharge evolution process of the secondary arcs.

The application provides a kind of based on binocular vision and deep learning's side slope half-hole rate calculation method and system, belong to the field of blasting technology, this method includes: through binocular camera obtains the left and right image data of slope surface after blasting, and through sensor collects environmental data;Left and right image data are input into stereo matching algorithm, and disparity map is obtained, and based on the parameter configuration of binocular camera, point cloud data is calculated by triangulation principle;Point cloud data is fused with pre-stored geological data and feature extraction is carried out, and multi-feature point cloud data is obtained;Multi-feature point cloud data is input into pre-trained blast hole recognition model, and blast hole area distribution is obtained;Euclidean clustering algorithm is used to segment blast hole area distribution, and blast hole point cloud is obtained;Based on the geometric features of blast hole point cloud and the preset blasting design parameters, the half-hole rate is calculated, and the half-hole rate is obtained, and the half-hole rate evaluation result is generated and output based on the half-hole rate. The application improves the accuracy of half-hole rate calculation.

This invention discloses a method and system for binocular image super-resolution reconstruction based on cross-scale disparity prior, comprising: S1: acquiring the original left feature map and the original right feature map corresponding to the low-resolution left and right binocular images; S2: using a binocular attention module to perform cross-view interaction on the original left feature map and the original right feature map respectively, to obtain the interacted left feature map and the interacted right feature map; S3: inputting the interacted left feature map and the interacted right feature map into the cross-scale disparity attention module, and fusing them to obtain an aggregated feature map; S4: inputting the aggregated feature map into a cascaded dynamic upsampling reconstruction network to obtain the reconstructed super-resolution binocular image. This invention can fully utilize disparity information and obtain more realistic and higher-quality super-resolution images in a shorter running time.

Methods, apparatuses, system, devices, and computer program products for volumetric sensing using a container monitoring system are disclosed. In a particular embodiment, a cargo monitoring system captures a first set of images of a dock scene through the stereo vision system of the dock-mounted monitoring device. The cargo monitoring system calibrates the stereo vision system of the dock-mounted monitoring device based on the first set of images. The container monitoring system determines, based on the first set of images, localization parameters for the dock-mounted monitoring device with respect to a container in the dock scene. The container monitoring system generates a disparity map for the dock scene based on the first set of images. The container monitoring system generates a depth map of an interior space of the container based on the disparity map and the localization parameters.

This invention discloses a multi-resolution end-to-end and online parameter update method for depth perception. It includes: (1) constructing a multi-view vision acquisition system to obtain left and right view images and depth information and completing calibration and alignment; (2) constructing a progressive multi-resolution end-to-end disparity inference network to achieve staged disparity prediction and support dynamic early stopping output; (3) converting depth information into disparity form to construct sparse supervision signals; (4) constructing an asynchronous online adaptive mechanism decoupled from the inference process and the model update process to complete model parameter optimization; (5) introducing a supervised quality assessment strategy to control update triggering and improve online learning stability; and (6) converting the disparity results into a depth map as the final output. This invention, through decoupling inference and learning, progressive computation, and a quality-gated update mechanism, achieves continuous adaptive optimization of the model while ensuring real-time performance, significantly improving perception accuracy and robustness in complex environments.

This invention belongs to the field of image processing technology and specifically discloses a generalized stereo matching method and device based on structure and motion cues. It includes: aligning image features based on disparity information to generate confidence information; fusing binocular disparity information and monocular depth information according to the confidence information to obtain an initial fused disparity map; aligning binocular image features based on the initial fused disparity map; and initializing the hidden state during the iteration process. During the iteration process, structural cues are constructed based on the geometric consistency information between the current disparity and monocular depth, and motion cues are constructed by combining the cost information obtained during stereo matching. The hidden state is recursively updated accordingly, and the disparity is progressively corrected, outputting the final disparity map. This invention effectively improves the zero-shot generalization ability of stereo matching methods under unknown scenes and cross-dataset conditions, and reduces the dependence on large-scale labeled data and scene-specific training.

A binocular vision-based method and system for coaxially online monitoring of a three-dimensional morphology of a molten pool. The method comprises: acquiring dual-view images of a molten pool; inputting the dual-view images of the molten pool into an unsupervised adaptive loss neural network to obtain disparity information of the molten pool; using a checkerboard to calibrate a binocular vision monitoring system to obtain system parameter information; and obtaining depth information of the dual-view images of the molten pool on the basis of the disparity information of the molten pool and calibrated parameter information, and reconstructing the three-dimensional morphology of the molten pool on the basis of the depth information of the dual-view images of the molten pool.

The application provides an optical flow iterative stereo matching method based on an attention mechanism and multi-layer context aggregation, and relates to the technical field of computer vision three-dimensional reconstruction. A left eye image and a right eye image of a target piece collected by a binocular camera are acquired and input into a trained algorithm model; similarity feature maps of different disparity levels of left and right images are generated through a feature encoder, and context features of the left image are obtained only through a context encoder; a 3D cost correlation volume is constructed and a correlation pyramid is generated, multi-semantics aggregation is performed on the correlation pyramid and the context features, searching, interpolation, and re-calibration are performed based on current disparity estimation, a disparity field is iteratively updated through a three-level gated recurrent unit, a raw resolution disparity map is obtained through convex upsampling, and a depth map is converted. The application can improve the stereo matching stability of low-texture and low-reflective target pieces.

This invention discloses a robust stereo matching method based on feature pyramids and attention perception, comprising: setting a feature extraction pyramid to process the input binocular stereo image, and obtaining multi-scale feature maps of the left and right view input images respectively; constructing a multi-scale cost volume pyramid using the multi-scale feature maps, and implementing feedback interaction and cross-scale cost aggregation between cost volumes of different scales; converting the cost volume pyramid into a probability value pyramid using a softmax function, and processing the probability value pyramid using a soft-argmin function to generate an initial disparity map; designing a saliency attention perception module to extract and generate attention feature maps in the shallow layers of the stereo matching network, and fusing the feature maps with the initial disparity map to obtain a refined disparity map. This invention can effectively correct erroneous matching points, restore lost image details and sharp object edges, improve the robustness and precision of disparity prediction, and generate a fine and accurate disparity map.

This invention discloses a method and apparatus for detecting the tilt of finished grain stacks based on computer vision, belonging to the field of grain depot management technology. The method includes: using a binocular vision camera as the measuring device and performing stereo calibration; acquiring left and right images of the stack at fixed points and times; using deep learning-based stereo matching technology to obtain disparity maps of the left and right images, and acquiring image depth information and point cloud information; comparing the point cloud information of the current stack image with the point cloud information of the reference image, and determining whether the stack is tilted based on the comparison result. This invention employs a domain adaptive technique based on color distribution transfer, enabling the model to maintain efficient feature extraction capabilities even under complex lighting conditions in grain depots. It can be deployed without manual annotation, reducing application costs. The invention introduces a residual network to extract multi-scale features and combines it with an adaptive aggregation module, resulting in clearer object contours in the output disparity map and improved detection accuracy.

The application relates to a rehabilitation detection method and device based on depth learning stereo image matching, and belongs to the technical field of rehabilitation detection. The application further optimizes a predicted disparity map by introducing a domain discriminator, and outputs an optimized predicted view, so that the optimized predicted view is converted into a three-dimensional point cloud, human body joint points are detected in the three-dimensional point cloud space, the human body joint points are modeled into a space-time graph, time sequence features are acquired, finally, key kinematic parameters required for rehabilitation evaluation are extracted on the basis of the output time sequence features, multiple rehabilitation evaluation indexes are output, final evaluation is carried out according to the multiple rehabilitation evaluation indexes, and rehabilitation suggestions are output. Through cross-attention matching and adversarial training of the domain discriminator, the application can transfer stereo matching knowledge to the actual home rehabilitation environment on unlabeled rehabilitation binocular images under the condition that rehabilitation scene annotation data is scarce, the end point error is reduced compared with a pure source domain model, and the disparity estimation error of a shielding area is significantly reduced.

The application belongs to the technical field of computer vision, and proposes a binocular stereo matching method based on a light-weight cost volume. The method comprises the following steps: inputting left and right image data; inputting the left and right images into a feature extraction network, selecting features of at least three scales at the tail for splicing, and respectively forming upper branch features and lower branch features; respectively constructing an upper branch cost volume and a lower branch cost volume; inputting the upper branch cost volume and the lower branch cost volume into a multi-scale coupled aggregation network to obtain coupled upper branch fused geometric features and lower branch fused geometric features; performing disparity regression to obtain a low-resolution initial disparity map; and according to superpixel neighborhood weighted upsampling, restoring the full-resolution disparity map as the final matching result. The application improves the information expression ability of the cost volume, enhances the collaborative utilization effect of shallow and deep features, and improves the disparity estimation accuracy and robustness in complex regions while controlling the computational complexity and storage overhead.

The application provides a depth estimation method based on structured light phase guidance, comprising: image acquisition of a to-be-measured object by a binocular imaging device to obtain a left-eye image and a right-eye image, wherein the left-eye image and the right-eye image contain a structured light pattern formed by a grating structure; phase feature coding of the left-eye image and the right-eye image based on stripe information in the structured light pattern to obtain a left-eye phase feature map and a right-eye phase feature map; feature fusion of the left-eye image and the left-eye phase feature map and the right-eye image and the right-eye phase feature map based on a phase attention mechanism to correspondingly obtain left-eye depth features and right-eye depth features; feature fusion of the left-eye depth features and the right-eye depth features based on matching confidence between the left-eye depth features and the right-eye depth features to obtain fused depth features; generation of a disparity map based on the fused depth features; and obtaining depth information of the to-be-measured object based on the disparity map.