72 results about "Appearance based" patented technology

In some embodiments, a computer system receives data representing a pose of at least a first portion of a user and causes presentation of an avatar that includes a respective avatar feature corresponding to the first portion of the user and presented having a variable display characteristic that is indicative of a certainty of the pose of the first portion of the user. In some embodiments, a computer system receives data indicating current activity of one or more users is activity of a first type and, in response, updates a representation of a user having a first appearance based on a first appearance template. The system receives second data indicating current activity of the one or more users and, in response, updates the appearance of the representation of the first user based on the current activity of the one or more users using the first or a second appearance template.

One example provides a computing device comprising a display device, a logic subsystem, and a memory subsystem. The memory subsystem comprises instructions that are executable by the logic subsystem to display a graphical airspace representing an airspace on the display device, and obtain one or more notice to air missions (NOTAMs) related to the airspace. The instructions are further executable to, for each NOTAM of the one or more NOTAMs, display a graphic overlay on the graphical airspace. The graphic overlay has an appearance based at least upon a determined status of the NOTAM.

The embodiment of the invention provides a video processing method, a video processing device, computer equipment and a computer storage medium, and relates to the technical field of video processing. The method comprises the following steps: acquiring a to-be-processed video, and performing target detection on the to-be-processed video based on a pre-configured neural network model to obtain a to-be-tracked object; based on the to-be-tracked object, a Kalman filtering prediction model is constructed in advance, so that the Kalman filtering prediction model predicts a target state vector of a next video frame according to an initial state vector of a historical video frame corresponding to a current video frame, and a motion track of the to-be-tracked object is obtained; and for a target to-be-tracked object in the to-be-tracked objects in the current video frame, according to a pre-trained deep learning appearance model, determining the appearance feature similarity between the target to-be-tracked object and other to-be-tracked objects, so as to perform matching tracking on the target to-be-tracked object based on the appearance feature similarity.

The invention discloses a product appearance flaw detection system and method based on image semanteme, and belongs to the technical field of flaw detection, and the system comprises an image collection module which is used for collecting a product appearance real-time image; the image processing module is used for processing the product appearance real-time image; the image semantic module is used for performing feature extraction and semantic segmentation on the product appearance real-time image to generate a product appearance semantic image; and the flaw detection module is used for performing flaw detection on the appearance of the product, judging whether the appearance of the product has flaws or not, and performing early warning, alarming and visual display on the appearance flaws of the product. The problem that the product appearance defect detection effect is poor due to the fact that effective defect detection cannot be carried out on the product appearance based on image semantics in the prior art is solved. According to the invention, effective flaw detection can be carried out on the product appearance based on image semantics, timely early warning can be carried out according to the product appearance flaw detection condition, and the product appearance flaw detection effect can be improved.

A system and method is provided that displays cursors for street level images, where the cursor changes appearance based on the objects in the image, such as the geographic distance between the objects and the camera position and the surface of the objects. For example, the cursor may appear to lie flat against the objects in the image change size based on the distance between the camera and object's surface.

An image generation system may receive a source image including a source object having appearance features. The appearance features may include unique visual characteristics indicative of a visual identity of the source object. The image generation system may receive a target image including a target object having a target motion. The image generation system may generate, based on the appearance features and the target motion, a three-dimensional (3D) flow field indicative of spatial transformations between the appearance features and the target motion. The image generation system may warp, using the 3D flow field, the appearance features to generate a warped image including a warped object having warped appearance features that mimic the target motion and preserve the visual identity of the source object. The image generation system may provide the warped image as an output image.

Methods, systems, and apparatuses, among other things, may label and classify objects via appearance-based clustering for computer vision automatic target recognition (ATR) systems, including automated anomaly detection for objects appearing in an area of interest (AOI).

The present disclosure generally relates to methods and user interfaces for establishing communications. A computer system displays a user interface object corresponding to a communication with different appearances based on a respective state of a process for sending the communication.

A computer system displays a user interface object in a three-dimensional environment with background content behind the user interface object in the three-dimensional environment. A first portion of the user interface object has an appearance based on an appearance of background content behind the first portion of the first user interface object, and has content between the first portion and a front of the first user interface object. When moving the content within the first user interface object in response to a user request, the content remains between the first portion of the first user interface object and the front of the first user interface object, and is displayed with a visual effect that is applied to the content based on a simulated thickness of the first user interface object.

The invention belongs to the technical field of image data processing and analysis, relates to a satellite video single target tracking method based on appearance and motion feature fusion, and solves the problem of missing detection or false detection in small target tracking. The method comprises the steps of generating tracking sequence data of a current frame based on template image data, current frame image data, appearance prompt data, confidence mark data, historical position data and trajectory change data; performing tracking processing on the tracking sequence data by an encoder to obtain current frame position data, current frame appearance data and current frame confidence data of the single target; and tracking the next frame of image data based on the current frame position data, the current frame appearance data, the current frame confidence data and the encoder. According to the invention, based on the historical position data and the trajectory change data, the position information and the motion information of the small target are accurately extracted through the encoder and target tracking is carried out, so that the accuracy of target tracking is improved.

The present invention provides a method and system for extracting salient regions of community images. The method includes preparing a training image set, and the testing process includes the following steps: calculating appearance-based saliency for the images in the training image set; calculating semantic features of corresponding object labels for each image in the training image set; solving the fusion coefficients of salient features; extracting the salient features of the test image and calculating the saliency map. The method and system for extracting salient regions of community images proposed by the present invention calculate, in the training stage, the salient features based on the image appearance, fuse them with the calculated semantic features of object labels, and solve the fusion parameters. In the testing stage, the fusion parameters obtained through training are used to fuse the salient features of the image appearance and the label semantic features to obtain the final saliency map.

Disclosed is a method, system and device for estimating the three-dimensional gaze direction of a human eye based on appearance. The method comprises: acquiring an image or video containing a face; performing face detection in the image or video frame, introducing a face key point detection algorithm to detect and locate the face key points; cutting out the face image and left and right eye images according to the face detection result and the positions of the key points; and estimating the three-dimensional gaze of the human eye in the image using a gaze estimation model, wherein the training process of the gaze estimation model comprises: using a Base-CNNs network to extract features of the face image to obtain a full-face image feature vector, using two asymmetric branch structures of an EE-Net network to extract features of the left and right eye images to obtain double-eye image feature vectors, estimating a head posture vector in combination with the face key point detection algorithm, and finally using a full connection layer to regress the full-face image feature vector, the double-eye image feature vector and the head posture vector for the final gaze estimation task.

The present invention discloses a scene-level fine-grained video retrieval method and system based on sketches, which belongs to the field of computer vision. The method extracts sketch features to construct a sketch space structure diagram based on appearance features and category features; extracts video features to construct a video spatiotemporal structure diagram based on video timing information, appearance features and category features, and uses an adaptive frame sampling strategy to sample video frames, first sparsely sample video frames, and then use a sketch-video association model to screen video frames, and use a sketch-video retrieval model to complete video fine-grained retrieval. The present invention compresses objects that appear in different time periods into the same sketch, performs scene-level video content summarization, and retrieves videos that are consistent with the sketch background elements, object appearance features and action types.

The appearance of an object is evaluated using at least one of a single type of data and multiple types of data. [Solution] The information processing system inputs appearance data representing the appearance of an object into a first base model, which is a trained model capable of processing at least one of a single type of data and multiple types of data, thereby obtaining appearance base data output from the first base model. Based on the appearance base data, the information processing system obtains related data associated with the appearance base data from multiple base data stored in a database. The information processing system inputs the appearance base data and related data into a second base model, which is a trained model, thereby obtaining evaluation data representing the evaluation of the object's appearance, and outputs the evaluation data.

The present invention discloses a real-time medicine box recognition method and system based on deep learning, belonging to the field of medicine management technology. The method comprises the following steps: setting up a camera above the medicine box to collect image information of the medicine box's appearance; using a pre-trained medicine box recognition model based on Yolov5 to obtain medicine box edge and appearance-based medicine box recognition results; performing text detection using a pre-trained DBNet-based text detection model to obtain the area of the medicine box text; performing text recognition using a pre-trained DenseNet and CTC-based text recognition model to identify the text information of the medicine box; and performing a low-threshold fuzzy search and matching on each word or phrase in the text recognition result in turn in a medicine database. The present invention can effectively solve the problem of manual verification required when dispensing medicines in pharmacies. Moreover, the present method greatly ensures the accuracy of recognition and reduces the workload of pharmacy workers.

The invention provides a notebook appearance defect identification method and device based on a convolutional neural network, and the method comprises the steps: obtaining the appearance feature data of a notebook based on a defect identification signal, and the appearance feature data at least comprise the large surface data, end surface data and bottom surface data of the notebook; appearance data features are obtained and input into a convolutional neural network detection model to obtain detection data, wherein the convolutional neural network detection model is obtained based on appearance defect sample data training; and determining a detection result of the notebook based on the detection data. According to the invention, the method achieves the detection of the appearance defects of various types of notebook computers through the trained convolutional neural network detection model, is wide in notebook computer appearance model coverage, does not need to independently design specific rules and algorithms for each type in the appearance defect detection process of various types of notebook computers, and improves the detection efficiency. The system development and maintenance difficulty is reduced, and the defect detection efficiency is improved.

The invention provides an intelligent quality inspection system and method for a precast beam field, electronic equipment and a storage medium. The intelligent quality inspection system comprises a conveying and positioning subsystem, a detection subsystem and a control subsystem, wherein the conveying and positioning subsystem is used for conveying a to-be-detected prefabricated part to a detection station; the central processing unit is used for sending a detection task instruction, performing multi-dimensional comprehensive analysis processing based on an appearance quality identification result, a geometric dimension identification result and an internal defect identification result, and determining a comprehensive quality evaluation result; the appearance quality detection subsystem is used for carrying out appearance defect detection processing on the surface image information based on an artificial intelligence visual identification algorithm and determining an appearance quality identification result; the geometric dimension detection subsystem is used for performing dimension analysis on the actual geometric parameters to determine a geometric dimension identification result; and the internal defect detection subsystem is used for processing the internal structure information based on an internal defect analysis algorithm and determining an internal defect identification result. The quality inspection efficiency is greatly improved, and the efficiency bottleneck problem of traditional manual detection is solved.

The invention provides a navigation positioning method and device based on personalized digital human anatomical organ reconstruction, and the method comprises the steps: directly obtaining human body features from a human body image and / or a human body video stream through a deep learning network, and outputting target parameters of a skin multi-person linear model, and rapid and objective parameterization of the human body model based on the appearance data is realized. The first position of the target vertex in the first human body internal anatomical model can be quickly positioned based on the target parameter in combination with the first human body internal anatomical model, and the position is mapped to the second position of the human body image through spatial transformation of the virtual camera parameter, so that the target navigation positioning position is determined. In the process, the model parameters do not need to be manually and subjectively modified, the in-vivo organ structure is efficiently predicted according to the human body appearance image and / or the human body video stream, and the technical problems that in personalized human body three-dimensional anatomical structure reconstruction, model parameter adjustment depends on manpower, and prediction of the in-vivo structure based on appearance data is lacked are effectively solved.

The application relates to the technical field of robots, in particular to a lightweight topological map construction method for scene recognition, which comprises the following steps: collecting data and processing the data to obtain accumulated point cloud; extracting scene descriptors; calculating the similarity between the descriptors to generate topological nodes; and calculating an optimal topological map. The application describes a scene according to multi-frame point cloud data accumulated according to a robot running track, extracts a descriptor of the point cloud, and judges whether to generate a topological node according to the similarity of the descriptor; the application can guarantee effective construction of a lightweight topological map, is particularly suitable for the demand of large-range scene topological map construction, and compared with an appearance-based topological map construction method, the number of topological map nodes constructed by the application is greatly reduced, but the reliable scene recognition performance can still be guaranteed.

The present invention relates to the technical field of integrated circuit chip processing monitoring, and more specifically, to an intelligent processing monitoring system for integrated circuit chips. The system comprises: acquiring integrated circuit chip image data within a target area and processing data collected by sensors on a processing line; calculating a defect feature index for each sub-image; segmenting the defective areas of the integrated circuit chips in the sub-images based on the defect feature index; extracting features of the defective areas of the integrated circuit chips; identifying and analyzing the features of the defective areas to generate appearance monitoring analysis results; analyzing the processing data to generate non-appearance monitoring analysis results; and marking the integrated circuit chips within the target area based on the appearance monitoring analysis results and the non-appearance monitoring analysis results. The system is capable of rapidly identifying surface defects of the integrated circuit chips and promptly detecting abnormal changes in processing parameters, thereby improving the accuracy and efficiency of processing monitoring.

The disclosure provides an image repairing method and system combining edge information and appearance flow operation, the method comprising: preprocessing an obtained image to be repaired to obtain a smooth image; performing edge detection and prediction to obtain a target edge map; performing smooth structure reconstruction according to the smooth image and the target edge map to obtain reconstructed smooth structure; extracting the correlation between each region of the image to be repaired according to the reconstructed smooth structure, sampling features from the relevant regions based on the appearance flow operation to generate lively texture, and filling the features containing lively texture information to the damaged region of the image to be repaired to obtain a repaired complete image. The disclosure realizes the repair of the damaged image through the repair process from the edge to the smooth structure and then to the whole, combines the edge information and the appearance flow operation to perform three-stage image repair, can generate reasonable structure and real lively texture, and thus achieves good repair effect.

The invention relates to the technical field of image acquisition, in particular to a railway vehicle appearance image acquisition system and method and device.The system comprises a movable support, an image acquisition module and a control module, and the image acquisition module is arranged on the movable support and used for acquiring an appearance image of a target component of a railway vehicle; the movable bracket is in communication connection with the control module and is used for driving the image acquisition module to move to a position corresponding to a position moving instruction based on the position moving instruction issued by the control module; the control module is used for receiving an appearance image acquisition instruction, issued by a user, of the target component of the railway vehicle, and forming a position moving instruction based on the appearance image acquisition instruction, so that the image acquisition module can acquire an appearance image of the target component of the railway vehicle after the movable bracket moves to a position corresponding to the position moving instruction; the appearance image of the railway vehicle can be dynamically collected according to user requirements, and personalized and refined detection requirements are met.

The invention relates to the technical field of integrated circuit chip processing monitoring, in particular to an intelligent processing monitoring system of an integrated circuit chip. The method comprises the following steps: acquiring image data of an integrated circuit chip in a target area and processing data acquired by a sensor on a processing production line, calculating a defect characteristic index of each sub-image, segmenting an integrated circuit chip defect part in the sub-image based on the defect characteristic index, extracting the characteristic of the integrated circuit chip defect part, and determining the defect part of the integrated circuit chip according to the characteristic of the integrated circuit chip defect part. Identifying and analyzing the features of the defect part, generating an appearance monitoring analysis result, analyzing the processing data, generating a non-appearance monitoring analysis result, and marking the integrated circuit chip in the target area based on the appearance monitoring analysis result and the non-appearance monitoring analysis result. The surface defect of the integrated circuit chip can be quickly identified, the abnormal change of the processing technology parameters can be found in time, and the processing monitoring accuracy and the processing monitoring efficiency are improved.

The present application relates to the technical field of image processing, and discloses a gait recognition method, which captures appearance contour information from different angle positions by polar coordinate sampling at R different sampling angles, explicitly models contour structure in a polar coordinate system, makes up for the deficiency of traditional appearance-based methods in capturing structural features, and thus improves the robustness of gait recognition to appearance changes. Furthermore, the present application also performs multi-scale fusion on structural features of different sampling angles to suppress redundant information and enhance the stability and distinguishability of structural representation. In addition, the present application combines gait features with global information and local information on the final structural features obtained by polar coordinate sampling, maintains the robustness to appearance disturbance, and improves the sensitivity of the model to shape changes. The present application effectively alleviates the influence of view angle change, clothing change and carrying articles, and greatly improves the robustness of appearance contour modeling.

The invention relates to the technical field of PCB (Printed Circuit Board) detection, in particular to a method and a system for detecting the edge deviation of the appearance of a PCB based on a welding component. The polarization characteristic pattern is generated by acquiring the multi-spectral original image and the polarization state parameter of the to-be-detected area of the PCB, the reflection exposure abnormal area and the reflection exposure normal area can be effectively positioned, the fusion weight is automatically adjusted according to the spectral response characteristic of the signal area through the self-adaptive spectrum fusion enhancement technology, and the detection accuracy is improved. The problem that in a traditional method, image enhancement is not fine enough is solved, the adaptive fusion enhancement technology enables the multispectral image to better conform to the actual situation during enhancement, more detail information is reserved, key features can be accurately extracted by generating a semantic segmentation map and combining three-dimensional standard coordinate information, and the image enhancement accuracy is improved. The detection precision and reliability of the welding component and the appearance edge of the PCB are effectively improved, the deviation can be accurately positioned and quantified, and the quality control level in the production process of the PCB is improved.

A method for controlling production of color products includes receiving a reference image for a color product; producing a plurality of color products corresponding to the color product in the reference image; acquiring a plurality of images corresponding to the plurality of color products; determining whether an appearance defect exists for each of the plurality of color products by comparing each image of the plurality of images corresponding to the plurality of color products to the reference image; for each detected appearance defect, determining a severity of the appearance defect; generating a visualization of appearance defects on an image of the color product based on the severity of the appearance defect and a frequency of occurrence of the appearance defect; and adjusting production of the color product in response to the visualization of appearance defects to reduce the severity or frequency of occurrence of the appearance defect.

In some embodiments, a computer system receives data representing a pose of at least a first portion of a user and causes presentation of an avatar that includes a respective avatar feature corresponding to the first portion of the user and presented having a variable display characteristic that is indicative of a certainty of the pose of the first portion of the user. In some embodiments, a computer system receives data indicating current activity of one or more users is activity of a first type and, in response, updates a representation of a user having a first appearance based on a first appearance template. The system receives second data indicating current activity of the one or more users and, in response, updates the appearance of the representation of the first user based on the current activity of the one or more users using the first or a second appearance template.

The present application relates to electronic product appearance defect detection technical field, specifically, relate to a kind of appearance defect detection system and method.The present application also provides a kind of appearance defect detection system, it is based on appearance defect detection equipment to carry out and be suitable for the appearance defect detection method described above;Appearance defect detection equipment includes equipment main body, equipment main body includes feeding end and discharging end, from feeding end to discharging end is sequentially arranged with feeding system, dust removal system, CG face array detection system, 2D and 3D combined CG face detection system, macro detection system, 2D and 3D combined BG face detection system and BG face array detection system.The equipment main body in the present application can preferably cover each area of detection object by single equipment, and different detection sites are detected by targeted system, so that the defective product detection rate can be improved, so as to ensure the quality of product.

A method for recognizing hand gestures when the hand is held in front of the user's upper body. The method detects primary features associated with the upper body, including features on the face (eyes and mouth, etc.) and the shoulder tips. The geometric relationships between these features are used to determine if the face / head has turned to the side relative to the shoulders. If not, the face-shoulder features are used to establish a face-shoulder coordinate system, and the hand image is transformed into this new coordinate system. This effectively defines the hand gestures relative to the user's face and shoulders, significantly reducing the size of the feature space for the hand gestures. This face-shoulder coordinate transformation can be combined with traditional appearance-based gesture recognition methods, or with traditional or hybrid deep neural networks for RGB or RGB-D images.

Disclosed herein are a deep neural network (DNN) learning method for generalizing appearance-based gaze estimation and an apparatus for the same. The deep neural network (DNN) learning method includes creating multiple augmented images based on an original image, inputting the multiple augmented images to a DNN to output a gaze estimation value, calculating a total loss between a gaze ground truth of the original image and the gaze estimation value through gaze consistency regularization (GCR) using a spherical gaze distance (SGD), and updating parameters of the DNN by backpropagation of the total loss.