18 results about "Hand region" patented technology

This invention discloses a machine vision-based method and system for monitoring medication dispensing. The method involves acquiring a multi-view polarized image sequence and corresponding depth image sequence of the medication dispensing operation area; performing physical-level specular de-illumination on the multi-view polarized images and identifying transparent material regions to generate multimodal fusion data with transparency priors; detecting the operator's hand posture in real time to generate a dynamic occlusion mask; constructing and training a dynamic neural radiation field, incorporating a temporal de-occlusion prior loss during training to utilize unoccluded spatiotemporal information to recover the visual features of the area marked by the dynamic occlusion mask; and based on the trained dynamic neural radiation field, removing the hand region and rendering a de-occluded and specular-de-illumination virtual monitoring view. This invention solves the problems of target loss, inaccurate measurement, and poor interpretability caused by specular reflection, transparent objects, and dynamic occlusion.

The application discloses a method for determining the centroid of a human hand region and a display device. When a user performs 3D gesture interaction, an image collector collects an RGB image and a depth image. Based on the coordinates of the center point of the human hand detection frame in the RGB image, the coordinates of a target point corresponding to the center point of the human hand detection frame in the depth image are calculated. Based on the coordinates of the target point and the depth value of the human hand region where the target point is located, the coordinates of the centroid of the human hand region in the depth image are calculated. The method and the display device are based on non-aligned RGBD images, do not need complex calculation to realize the alignment function of the RGB image and the depth image, but greatly simplify the calculation method of the centroid of the human hand region in the depth image using the epipolar constraint method, reduce the search range of the corresponding feature points from the RGB image to the depth image, can timely determine the centroid of the human hand region, realize real-time interaction of the 3D gesture algorithm in an embedded terminal, and improve the immersive interaction experience of the user.

This invention provides a gesture command recognition method based on an improved contactless data stream network. It utilizes the coordinates of key points in hand data extracted using an improved MediaPipe network algorithm framework based on artificial intelligence. The improvements to the MediaPipe network include increasing the recognition rate of the network model, enhancing the accuracy of key gesture features, and optimizing scene adaptability. The method achieves rapid gesture category determination by "precise hand region cropping and positioning—multi-scale key feature point fusion enhancement—gesture category static and dynamic classification adjustment." This invention addresses the recognition bias caused by excessively small gestures in long-distance photography and detailed gestures in close-up photography by adding a multi-scale feature enhancement module; it also reduces the misjudgment rate of gesture recognition by adding limb nodes and fusing the association between limbs such as the arm and torso; it enhances robustness in complex environments, reducing background interference and occlusion; and it strengthens the motion features between image frames, distinguishing between static and dynamic gestures and improving the dynamic gesture recognition rate.

The present disclosure relates to an information processing device and an information processing method that make it possible to improve the accuracy of estimating the shape of a person's hand estimated from a captured image of the person who is a subject. In the present invention, a mesh is estimated from an image of the person who is the subject, the mesh is used to set a hand joint parameter to 0 and determine the size from the wrist to the tip of the middle finger when the palm is opened as the size of a hand region, the skeleton of the person is estimated from the image, a center position of the hand region is determined from the joint group corresponding to the skeleton of the person, and the hand shape is estimated on the basis of a hand image obtained by cutting out, with the center position of the hand region as a reference, the size range obtained by multiplying the size of the hand region by a prescribed coefficient. The present disclosure can be applied to virtual viewpoint image reproduction apparatuses.

The application relates to the field of video applications, and provides a video desensitization method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring video data, identifying a human hand region based on the video data, obtaining N gesture recognition results and M hand contact article recognition results, determining a sensitive region and a target sensitive level according to the N gesture recognition results and the M hand contact article recognition results, and performing desensitization processing on the video data according to the sensitive region and the target sensitive level. The video desensitization method provided in the embodiment of the application can solve the technical problem that a hand contact article or hand operation of a photographed person cannot be desensitized, personal information of the photographed person is easily leaked, and the personal information safety of the photographed person is improved.

This invention relates to the field of wearable device technology, specifically disclosing a wearable hand device, a physiological monitoring component, a signal processing method, and a system. The device includes an elastic wrapper with a palm region and a back of hand region. The palm region has a conductive coupling portion; in the wearing state, both sides of the conductive coupling portion are electrically connected to the user's palm skin and electrodes of an external device, respectively. The back of hand region has a physiological monitoring component, including a physiological signal acquisition module, a signal processing module, and a status indication module. The signal processing module is electrically connected to the physiological signal acquisition module and the status indication module, and controls the status indication module to indicate the physiological state based on the physiological signals acquired by the physiological signal acquisition module. At least one of the elastic modulus, hardness, and thickness of the palm region is lower than the corresponding parameter of the back of hand region. This solution enables physical interaction between the wearable hand device and external devices, as well as real-time physiological state perception under high-load training scenarios.

This application discloses a method, apparatus, and method for training a dynamic gesture detection network. The network training method includes: acquiring multiple gesture videos, each corresponding to a dynamic gesture; performing image processing on each gesture video to determine a first feature map and a second feature map corresponding to each gesture video; wherein the first feature map corresponding to any gesture video is used to represent: motion information between two adjacent frames in any gesture video, or hand contour information in any gesture video; the second feature map corresponding to any gesture video is used to represent: the position information of the hand region in any gesture video; and iteratively training the dynamic gesture detection network based on the first and second feature maps corresponding to each gesture video to obtain a target detection network. This application solves the technical problem in related technologies that training a dynamic gesture detection network requires a large amount of training data and involves a complex training process.

This application provides a computer vision-based gesture recognition system, method, device, and medium. For each occluded keypoint, based on a pre-defined spatiotemporal graph model of the target object's hand keypoints, a graph convolutional network is used to extract spatial features of keypoints with high detection confidence in the spatial neighborhood of the occluded keypoint and temporal series features with high detection confidence in historical frames before occlusion. This yields a spatiotemporal collaborative attention map for guiding information completion. Based on the spatiotemporal collaborative attention map, feature propagation and aggregation are performed from the keypoints with high detection confidence to the occluded keypoints to obtain the completed feature representation of the occluded keypoints. The completed coordinates of the occluded keypoints are then output, generating an occlusion-resistant keypoint sequence for the target object's hand region. Based on this occlusion-resistant keypoint sequence, the gesture category of the target object is identified. Using the scheme of this application, robust gesture recognition against occlusion can be achieved.

This application discloses a virtual reality teaching gesture recognition method based on a dual-camera multi-branch network, comprising: acquiring 3D point cloud images and depth images of the gesture changes of the object to be recognized at different times; performing hand region detection on the 3D point cloud images and depth images respectively, and segmenting the hand region images from them; inputting the hand region images at different times into a trained gesture recognition model to generate gesture recognition results of the object to be recognized at different times; and forming operation instructions of the object to be recognized at different times based on the gesture recognition results and the preset mapping relationship between standard gestures and instructions. This invention integrates data from two modalities, depth images and 3D point cloud images, for hand recognition. The data from the two modalities are complementary, resulting in more robust recognition capabilities and improved recognition accuracy. Based on the recognition results, virtual scene teaching can be implemented, allowing students to focus more on learning in the classroom.

The application discloses a morning inspection machine hand wound discrimination method and device based on meta-learning enhancement, equipment and medium. The method comprises the following steps: acquiring an initial hand image collected by a morning inspection machine, and determining a hand region mask in the initial hand image; preprocessing the initial hand image based on the hand region mask, extracting features from the preprocessed image based on a pre-constructed lightweight meta-learning model, and strengthening the features through a wound-specific attention module to obtain wound strengthened features; performing few-sample feature matching on the wound strengthened features and wound prototype features in the lightweight meta-learning model, and determining a hand wound discrimination result according to a matching result. The application achieves the technical effects of few sample requirements, strong anti-interference capability, high recognition accuracy and adaptive morning inspection machine embedded operation.

The application provides a vehicle cabin interaction method and device, a storage medium and equipment. In the method, multi-modal data of a driver's hand region is collected, gesture features are extracted from the multi-modal data, the gesture features are processed based on a gesture recognition model to obtain a gesture category, a cabin interaction instruction is generated according to the recognized gesture category, the cabin interaction instruction is sent to a cabin subsystem to drive it to perform corresponding operations, and vehicle cabin interaction based on gesture control is realized. In addition to image data, the multi-modal data also includes radar data and / or depth data, so that gesture features can be effectively captured even in extreme light and when the hands are blocked. In this way, the gesture recognition accuracy and interaction reliability can be improved.

A parameter determination method for a low-voltage insulating glove, comprising: on the basis of operation behavior parameters and operation environment parameters of a target operation scenario, obtaining operation parameters; inputting the operation parameters into a trained friction analysis model to obtain friction analysis results outputted by the friction analysis model and corresponding to hand regions in the target operation scenario; on the basis of the friction analysis results of the hand regions, obtaining glove reinforcement region parameters corresponding to the target operation scenario; collecting hand dimension data of operators in the target operation scenario, and on the basis of a data distribution of the hand dimension data, obtaining glove size category parameters; and on the basis of the glove reinforcement region parameters and the glove size category parameters, obtaining a target parameter set of a low-voltage insulating glove applied in the target operation scenario.

A handheld ultrasonic probe detection and attribute recognition method based on multi-scale attention and hand guidance, collects process images of an operator holding an ultrasonic probe to examine a patient, and pre-processes the process images as input images; a handheld target detection model detects the input images, outputs probe categories, boundary box coordinates and detection confidence; and an attribute recognition model recognizes attributes of the input image probes, and outputs direction attributes and position attributes of the probes. The hand detection module locks the hand region in the handheld target detection model, and the position attention module establishes a spatial correlation between the hand features and the probe image features, significantly improving the probe category recognition accuracy, the boundary box positioning accuracy and the detection confidence. The attribute recognition model captures the interaction between the probe image features and the target region features through a cross-attention mechanism, dynamically adjusts the channel weight of the fused features, enhances the key channel information, and accurately identifies the direction attributes and the position attributes of the probes.

This disclosure provides a method, apparatus, system, device, medium, and program product for hand pose reconstruction. The method includes: acquiring an image sequence simultaneously captured from at least two angles; determining the hand region in each frame of the image sequence; wherein the hand region of at least a portion of the images in the image sequence is determined based on the hand regions of adjacent images; identifying hand pose parameters in the hand regions; and constructing a three-dimensional hand model based on the hand pose parameters. This disclosure, based on multiple views, avoids keypoint drift and shape loss caused by occlusion of the real hand during the three-dimensional hand model construction process, and has the advantages of low computational load and short inference time. Furthermore, since the hand region of some images in the image sequence is determined based on the hand regions of their adjacent images, target recognition is not required for each frame, thereby reducing the frequency of target recognition and further improving the speed and efficiency of three-dimensional hand model construction.

This invention discloses a method for restoring deformed hands in generated images, comprising the following steps: acquiring a generated image containing a deformed hand; detecting the deformed hand in the generated image and reconstructing a structurally correct 3D hand mesh; generating a contrast-normalized depth map through depth rendering, and generating a refined hand mask based on the depth map; inputting the generated image, the refined hand mask, and the depth map into a finely tuned stable diffusion restoration model; injecting the depth map as a geometric prior using ControlNet to guide the model to restore only the area indicated by the refined hand mask, generating a restored hand with correct anatomical structure and consistent global style with the original image. This invention can accurately locate and restore only the deformed hand region, significantly improving the correctness of the hand structure while effectively protecting the global composition, texture details, and artistic style of the image.

The application discloses a kind of multi-modal fusion hand image enhancement method and system, including the two-way gradient channel between the differentiable physical simulation module of constructing parameterized three-dimensional hand model and two-dimensional image processing module;Iterative optimization is executed in channel, each iteration includes: initialize three-dimensional hand model posture parameter, generate two-dimensional projection mask by differentiable rendering, update segmentation network parameter, region analysis network parameter and three-dimensional hand model posture parameter by minimizing joint loss function;Sampling obtains intermediate segmentation mask sequence and posture parameter sequence, generates region evolution feature vector;Extract static morphological feature vector and structural feature vector, generate multi-dimensional region morphological control vector by fusing three;The vector is converted into image enhancement control instruction, selects and combines image processing operator to carry out pixel-level processing, and outputs enhanced hand region image;Difference, high-fidelity pixel-level enhancement to target hand region is realized.

The application provides a dynamic gesture recognition method, device and equipment. The method comprises the following steps: acquiring a hand image sequence; the hand image sequence comprises N frames of to-be-detected hand images; wherein N>1, and N is an integer; performing hand region detection on the to-be-detected hand images to obtain N frames of detection images corresponding to the to-be-detected hand images; performing optical flow analysis processing on adjacent two frames of to-be-detected hand images to obtain N-1 frames of optical flow grayscale images; performing fusion image processing on the to-be-detected hand images, the detection images and the optical flow grayscale images to obtain a fusion image sequence; inputting the fusion image sequence into a three-dimensional residual network to obtain a dynamic gesture classification and a dynamic gesture category recognition result; the scheme of the application has strong generalization ability, strong expression ability and good expansibility; in the case of ensuring the accuracy, the model uses as few parameters as possible, ensures the recognition speed of the model, and has better classification effect.

The application provides a juicer human-computer interaction control method, system, device and medium. The hand region point cloud of a target user is separated from the hand image data of the target user when the target user controls the juicer by gestures. The gesture features of the target user when interacting with the juicer are determined based on the hand region point cloud. The context features of the historical interaction tasks of the target user and the juicer are determined. Then, adaptive gesture recognition is performed based on the gesture features and the context features to obtain the interaction intention vector of the target user and the juicer. The compound operation instruction of the target user to the juicer is determined according to the interaction intention vector and the current working state of the juicer. The working mode of the juicer is dynamically adjusted by converting the compound operation instruction into the machine control instruction of the juicer. The scheme of the application can realize dynamic context perception compound operation in the juicer gesture interaction control in a complex kitchen environment.