19 results about "Point localization" patented technology

The invention discloses a personnel state monitoring method and system based on a YOLO algorithm, and belongs to the field of image or video recognition. According to the method, a real-time image or video stream is acquired through a camera, human body detection and key point positioning are performed by using an optimized YOLO algorithm, and a motion model is established in combination with human body posture estimation so as to judge the state of a person. According to the optimization algorithm, a CBAM attention mechanism and a multi-scale feature fusion technology are introduced, and the detection precision and robustness in a complex scene are improved. The system supports correction of personnel key point information, and noise is eliminated through historical frame data and a prediction model. For abnormal behaviors, such as falling or illegal intrusion, the system can generate an alarm signal by comparing an action model with a normal behavior sample library and notify a management terminal. According to the invention, efficient and real-time monitoring of the state of the personnel in the natural gas station is realized, and the safety management level is effectively improved.

Example aspects of the present disclosure are directed to computing systems and methods for hand tracking using a machine-learned system for palm detection and key-point localization of hand landmarks. In particular, example aspects of the present disclosure are directed to a multi-model hand tracking system that performs both palm detection and hand landmark detection. Given a sequence of image frames, for example, the hand tracking system can detect one or more palms depicted in each image frame. For each palm detected within an image frame, the machine-learned system can determine a plurality of hand landmark positions of a hand associated with the palm. The system can perform key-point localization to determine precise three-dimensional coordinates for the hand landmark positions. In this manner, the machine-learned system can accurately track a hand depicted in the sequence of images using the precise three-dimensional coordinates for the hand landmark positions.

The invention provides a starting point positioning method and system for coding mapping relation identification. The method belongs to the field of coding mapping, and comprises the following steps: acquiring Coquelet-80 audio data; the method comprises the following steps of: demodulating the Coquelet-80 audio data to obtain bit stream data; verifying the mapping relationship in the bit stream data through a preset algorithm, marking the data matched with the mapping relationship as the data conforming to the mapping relationship, adjusting the initial position of the data not matched with the mapping relationship, and re-verifying the mapping relationship through the preset algorithm until the mapping relationship is matched; and extracting part of data conforming to the mapping relation from the two groups of data conforming to the mapping relation, and decoding according to the code table. The system comprises an acquisition module, a demodulation module, a verification mapping module and a decoding module, and can quickly and reversely deduce an original number or confirm whether a mapping relation is established or not.

This invention relates to the field of measurement data analysis technology, specifically to an engineering measurement data analysis method and system based on image recognition. The method includes the following steps: using an industrial camera to identify structural edges, measurement markers, and calibration rulers to form a reference set of measurement point coordinates; establishing corresponding quantities of directional scales based on the calibration ruler direction; combining the displacement relationship of the measurement points to form a measurement point interval characterization set; determining the edge deviation reference rate; and finally forming a boundary positioning association result. This invention uses grayscale abrupt changes to define edges and construct measurement point positioning references; calculates the lateral and longitudinal displacements of the calibration ruler to extract corresponding directional scales; combines measurement point displacement and edge direction to form interval characterizations; analyzes the normal span and surface deviation state to form edge references; and combines the boundary extension orientation to define the associated segments of the arrangement trajectory. This reduces interference from manual interpretation, maintains consistency of multi-directional conversion benchmarks, controls the error accumulation process, and improves the coherence and stability of engineering monitoring data processing.

This invention belongs to the field of intelligent medical image recognition technology, specifically relating to a method and system for locating key points in hand bone X-ray medical images. The method includes the following steps: S1, collecting and preprocessing hand bone X-ray images and corresponding key point annotation data; S2, constructing a key point localization model for hand bone X-ray medical images; S3, training and validating the model using an auxiliary loss function; S4, performing end-to-end prediction using the validated model. This invention features high localization accuracy and the ability to locate a sufficient number of hand bone key points at once.

The present invention relates to the field of biometric recognition technology, and particularly to a real-time iris localization and segmentation system and method. The system includes: a backbone network for extracting image features from an original image to obtain an image feature map; a semantic segmentation subnet for performing iris segmentation on the image feature map; a center point localization subnet for locating the inner and outer circle positions of the iris; and a size regression subnet for predicting the radii and offsets of the inner and outer circles of the iris. The RISNet of the present invention performs inner and outer circle localization in a manner of double center point confidence prediction and double-branch regression, shares the same feature map with the segmentation network, outputs the localization result and the iris mask simultaneously, and does not require post-processing; optimizes the loss function and proposes PairLoss; the experimental results show that the method of the present invention has strong robustness, high accuracy, improved localization accuracy and segmentation accuracy, and realizes speed improvement.

Example aspects of the present disclosure are directed to computing systems and methods for hand tracking using a machine-learned system for palm detection and key-point localization of hand landmarks. In particular, example aspects of the present disclosure are directed to a multi-model hand tracking system that performs both palm detection and hand landmark detection. Given a sequence of image frames, for example, the hand tracking system can detect one or more palms depicted in each image frame. For each palm detected within an image frame, the machine-learned system can determine a plurality of hand landmark positions of a hand associated with the palm. The system can perform key-point localization to determine precise three-dimensional coordinates for the hand landmark positions. In this manner, the machine-learned system can accurately track a hand depicted in the sequence of images using the precise three-dimensional coordinates for the hand landmark positions.

This invention relates to the field of image feature recognition technology, specifically to a method and system for locating key surgical points in orthopedic surgery based on anatomical structure feature recognition. The method includes the following steps: constructing a prior skeletal topology map representing biomechanical connectivity based on human anatomical standards; utilizing a graph attention mechanism to perform semantic mapping and feature aggregation between the multi-scale anatomical feature map and the prior skeletal topology map to generate node-enhanced feature vectors; and performing spatial coordinate regression calculations based on these node-enhanced feature vectors to output the coordinates of the surgical key points. In this invention, by introducing prior anatomical topological knowledge as a global constraint for feature extraction, the information transmission mechanism of the graph structure effectively captures the geometric dependencies between key skeletal points. This solves the problem of local feature ambiguity caused by traditional methods in complex pathological scenarios such as fracture displacement, bone hyperplasia, or tissue occlusion, achieving high-precision and robust surgical key point localization.

The invention provides a human body key point detection method and device, electronic equipment and a storage medium, and relates to the technical field of computer vision, key point features of different granularities in a to-be-detected human body image are extracted through a plurality of key point decoding layers, the performance and speed of human body key point regression can be optimized in a coarse-to-fine mode, and the accuracy of human body key point regression is improved. And the human body key point positioning accuracy is improved. Each key point decoding layer applies the bottom-layer semantic features and the position coding features of the bottom-layer semantic features, and a large amount of position guidance information can be provided for human body key point positioning, so that the regression difficulty of the human body key points is reduced. The key point detection model introduced through the method not only can improve the positioning accuracy and positioning precision of the human body key points, but also can avoid the problems of high calculation cost and low detection speed caused by a thermodynamic diagram method.

The invention relates to the technical field of medical image processing, in particular to a target point positioning method and device based on a medical image, equipment and a medium. According to the method, a target point is manually marked on a three-dimensional cross-sectional image, then the probability that each voxel coordinate on the three-dimensional cross-sectional image is possibly the target point is predicted on the three-dimensional cross-sectional image by adopting an identification positioning algorithm, and the target point is identified according to the marked target point coordinate and the prediction probability corresponding to each voxel coordinate. And screening the final position of the target point from each voxel coordinate. According to the method, manual labeling and an automatic identification positioning algorithm are combined, so that the positioning instability caused by image quality in automatic identification positioning can be made up, and the positioning stability and accuracy of the target point are improved.

This invention provides an intelligent broadcast switching method based on multi-person pose estimation and motion amplitude detection. First, the video signal is framed into images. These images are then input into a pre-built and trained multi-person pose estimation model to detect keypoint coordinates and related information. The motion amplitude detection module takes into account the differences in image depth and human scale. Based on the keypoint coordinates and related information, a motion amplitude detection algorithm is used to normalize and calculate pose feature values. Based on these values, the motion amplitude is judged. A threshold method is used to determine whether a motion is large or small. If a pose feature value exceeds a system-set threshold, the motion is considered large; otherwise, it is considered small. Finally, the intelligent broadcast system switches shots based on the detection results. This invention focuses on improving the accuracy of human keypoint localization and motion amplitude detection, achieving automated or assisted program production.

This application relates to the field of computer vision technology and discloses a method and system for detecting pain expressions. The method includes: acquiring a facial video stream, segmenting muscle regions and extracting texture features after facial key point localization and inter-frame alignment; calculating motion energy based on these features, extracting enhanced micro-expression temporal segments and multi-scale spatiotemporal features, constructing a muscle dynamics model and completing state estimation; generating an adaptive candidate spatiotemporal window set through spatiotemporal attention weighted fusion; performing temporal modeling for dynamic feature fusion, and selecting the optimal window by fusing micro-expression features; and outputting the final pain level after double consistency verification and loop closure optimization. This application overcomes the limitations of static images, accurately captures facial dynamics and temporal changes, strengthens feature correlation, and significantly improves detection accuracy.

The present invention discloses a global-to-local key point localization method and device based on fused attention, comprising the following steps: Step 1: Collect images with a camera and annotate their key points, and preprocess the image data to construct a key point localization data set; Step 2: Construct a global regression model and conduct supervised training; Step 3: Based on the global regression model, construct a local regression model and conduct supervised training on it through a joint loss; Step 4: Input the preprocessed image into the global regression model to obtain the predicted coordinates of the key points. The global regression model proposed by the present invention extracts deep features of the image, fuses and maps the image features related to the key points into the heat map space, realizing high-precision localization of the key points on the entire image. In addition, the fused attention module can effectively capture the relationship between global features and local features, guide the global features of the image to approach the local features, and realize the gradual accurate localization of the key point coordinates within the local block.

The invention discloses a touch anti-interference algorithm scheme, which comprises the following steps of: designing and arranging M driving channels and N sensing channels, executing a scanning period, obtaining original signal intensity of all nodes, converting original signal data into differential data, traversing lines by combining a threshold value t, calculating and recording a line touch area of each line, and calculating the line touch area of each line according to the line touch area. The node signals with the same scanning time and similar scanning time are differentiated to form whole-screen differential signals, interference bottom noise is eliminated accordingly, and effective signals are reserved; the invention provides a method for calculating a point area according to a line touch area and a method for calculating point coordinates and point width and height information through a circumscribed rectangle, and provides a method for calculating point coordinates and point width and height information according to the point coordinates and the point width and height information. Therefore, various interferences are eliminated, the touch point positioning and contour information obtaining accuracy is remarkably improved, and the user control and writing experience is improved.

This invention discloses a method, system, and device for multi-person pose estimation, comprising: inputting a feature map F in As input to the pre-constructed occluder branch, the occluder feature map F is obtained. out1 And perform key point localization to obtain the key point heatmap of the occluder; input feature map F in Feature map F of the occluder out1 The input feature map F is obtained by fusion. in_occludee ; Input feature map F in_occludee As input to the pre-constructed occluded branch, obtain the occluded feature map F. out2 And perform key point localization to obtain the key point heatmap H of the occluded subject. occludee The output is the pose estimation result of multiple people in the image to be estimated. This invention realizes the display modeling of complex scenes with crowds or occlusions and decouples the occlusion relationship, and establishes different information spaces for occluders and occluded people. It avoids missed or false detection of key points and effectively improves the estimation accuracy.

1. The name of the design product: positioning of the task approval information of the graphical user interface of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the part of the graphical user interface of the electronic device that requests local protection. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The electronic devices of design 1-design 3 are common designs, and other views of design 1-design 3 are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: the product interface of design 1-design 3 is an interactive interface for multi-dimensional data display of energy equipment; it is used for users to quickly locate the required auxiliary information and provides a convenient view mode; the interface of design 1 front view is an interactive interface for approval operation; in the interface of design 1 front view, after clicking the auxiliary information tab, design 1 interface change state diagram 1 is displayed; in the interface of design 1 interface change state diagram 1, after selecting the anchor point to the corresponding auxiliary information module (such as selecting the approval record anchor point), design 1 interface change state diagram 2 is displayed; the interface of design 2 front view is an interactive interface for approval operation; in the interface of design 2 front view, after clicking the to-be-confirmed X item button, the to-be-confirmed artificial inspection item is viewed, and design 2 interface change state diagram is displayed; the interface of design 3 front view is an interactive interface for approval operation; in the interface of design 3 front view, after clicking the permanent mode, design 3 interface change state diagram is displayed; the red coated part is not the part of the design that requests protection.

The invention relates to an AIOT multi-point positioning method. An apparatus comprises means for measuring a reception time of an activation signal triggering a response; means for determining the transmission time of the response; and means for determining the received transmission time difference as the difference between the transmission time of the response and the reception time of the activation signal triggering the response.

The application aims at the field of target counting and target positioning in remote sensing images, and discloses a remote sensing image multi-class target counting method based on point positioning. First, a feature extractor is used to extract features in the remote sensing image, then a scale adaptive feature enhancement module is used to complete rough prediction of the target scale in the remote sensing image, and the image features are fused and enhanced, next, the enhanced image features are used to predict the position, category, confidence and fine scale of the target in the image by using a prediction head, and the corresponding candidate target of the remote sensing image is generated. The candidate target contains repeated prediction of the same target, so it cannot be directly used as the prediction result of multi-class target counting and positioning. By using a non-maximum suppression post-processing means for the candidate target, the false prediction target and the repeated prediction target in the candidate target can be suppressed. The application improves the accuracy of network multi-class target counting and positioning.

An embodiment of the present application provides a training method and device for a face key point detection model. In this method, for each training sample in the training sample set of the face key point detection model, the face key point detection model is used to perform face key point localization on the training sample to obtain the predicted positions of at least one face key point in the training sample. For each face key point, the position error between the predicted position and the true position of the face key point is calculated, and the loss function value corresponding to the face key point is determined according to the position error; when the loss function value of the face key point detection model is lower than the set threshold, it is determined that the face key point detection model meets the training end condition. Thereby, the performance of the trained face key point detection model can be effectively improved.