4256 results about "Region of interest" patented technology

The invention provides a cross-modal remote sensing image-text retrieval method based on multistage semantic collaborative matching, which comprises the following steps of: extracting a region of interest by using a semantic segmentation algorithm through an image preprocessing module, segmenting a remote sensing image into a plurality of regions and generating image blocks; respectively extracting global features, regional features and pixel-level features of the image, and carrying out fine-grained coding on key regions such as fine-grained ground feature edges and the like; the text multi-level coding module is used for carrying out three-level feature coding of documents, sentences and words on texts based on a pre-training language model to ensure multi-level understanding of the texts; in the multi-level matching and fusion module, the similarity between the remote sensing image and the text description is calculated through a cross attention mechanism, weighted fusion is carried out on features of all levels, and finally a retrieval score is output. The method not only improves the accuracy and robustness of image-text retrieval, but also can be widely applied to the fields of remote sensing monitoring, environment change recognition, geographic information systems and the like.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

Provided herein are methods for capturing an analyte from a first region of interest of a biological sample on a substrate, where the biological sample comprises the first region of interest and a second region, and where the method includes contacting the second region with a sealant in order to create a hydrophobic seal thereby preventing an interaction between an analyte from the second region with a capture domain of a capture probe.

The invention provides a multi-modal document understanding model, a training method, a reasoning method and equipment, global visual features are extracted by using a weight-frozen first visual encoder, the understanding ability of the model to natural scene images is enhanced, a second visual encoder extracts fine-grained features based on high-resolution document images and region-of-interest information, and the understanding ability of the model to natural scene images is enhanced. And the analysis precision of the complex document is improved. And the information interaction module improves the intelligent understanding ability of a specific area in combination with the position of the region of interest input by the user. The feature fusion module splices multi-modal features in a channel dimension, so that visual information from different sources is efficiently integrated. The linear layer converts feature dimensions, so that the visual features are adaptive to the input requirements of the large language model, and the large language model combines visual and text information to generate a text understanding result conforming to semantic logic. According to the model, the capability of analyzing and extracting the fine granularity of the document information is improved by combining the two-way visual encoder with the selection of the region of interest of the user on the document image of the image-text structure.

Systems and methods are provided for targeting one or more features in a region of interest, and efficiently removing the features from an array of features. The systems and methods can remove one or more beads that contain analytes, intermediate agents, or a combination thereof, from the region of interest of a substrate by emitting light toward the beads on the substrate.

Provided herein are methods of characterizing tumors or a region of interest in a biological sample.

The invention discloses a behavior analysis camera monitoring method and system based on deep learning, and the method comprises the following steps: S1, collecting an image sequence and environment parameters of a target object in a scene through a multi-modal sensor, the environment parameters comprising illumination intensity and background noise level; s2, performing priority division on the image sequence by using a dynamic region-of-interest weight calculation module, and generating a region-of-interest mask based on the movement track and posture change of the target object; s4, generating an abnormal behavior index according to an output result of the classifier; and S5, when the comprehensive abnormal index exceeds a preset threshold value, a cross-camera cooperative tracking mechanism is triggered, an alarm signal is generated, and meanwhile, high-risk behavior fragment marks are stored to a priority queue, and accurate perception of human body behaviors in a complex environment is realized through an innovative architecture combining multi-modal data fusion and dynamic weight adjustment.

The invention discloses a casting detection method and system based on machine vision, and relates to the technical field of intersection of machine vision and material science, and the method comprises the steps: extracting the boundary coordinates of a region with a risk value exceeding a risk threshold according to a defect risk weight map, and forming a dynamic region of interest scanning frame; adjusting the incident angle of a polarized light source according to the track of the dynamic attention area scanning frame, collecting a local image, and obtaining a high-contrast image sequence for eliminating the interference of a surface oxide layer; extracting a defect edge feature map from the high-contrast image sequence through a deformable convolution kernel, and obtaining a defect candidate region mask; the defect candidate region mask is mapped to a von Mises stress field, and a crack germination probability is predicted in combination with a material fatigue limit parameter. According to the method, the accuracy and reliability of detection are improved, and the applicability under complex working conditions is remarkably improved, so that the casting quality control level is effectively improved, and the repair and quality control cost is reduced.

The invention discloses an intelligent track foreign matter detection system and method based on image recognition, and particularly relates to the technical field of image recognition. A real-time image is acquired, an area-of-interest is extracted in combination with an image segmentation algorithm, and potential foreign matters are identified; the space-time consistency of the detection result is verified by analyzing the space-time data of the track foreign matter so as to judge the real threat; extracting multi-dimensional features such as shape, texture, color and size from a real threat target, and outputting classification confidence to evaluate model classification stability; the detection accuracy is evaluated by combining the space-time consistency and the classification stability, the detection results are divided into different categories, the abnormal degree of subsequent detection is predicted for the incomplete accuracy detection result, and the image acquisition resolution or frame rate is dynamically adjusted to improve the detection definition. The problems of missing detection and frequent false alarm of small and medium-sized foreign matters in the prior art are solved, the accuracy and reliability of track foreign matter detection are greatly improved, and the running safety of a high-speed train is guaranteed.

The invention discloses an image diagnosis report generation method and device and a storage medium. The method comprises the following steps: determining the matching degree on three different levels, namely, the spatial matching degree between a diagnosis text and a candidate template on an anatomical part level, the semantic matching degree between the diagnosis text and the candidate template and related to pathological information, and the clinical association degree between the diagnosis text and the candidate template; according to the method, the target template matched with the diagnosis text is searched based on the diagnosis text input by the user, and the search is further optimized through the time decay factor, the clinical priority and the conflict degree among the regions of interest, so that the accuracy of searching the required image diagnosis template for the user is improved, and the user experience is improved. Therefore, the technical problems that in the prior art, a traditional keyword matching algorithm is poor in keyword matching effect in a medical scene, and the accuracy of searching out a correct template is reduced are solved.

The invention discloses a spine three-dimensional evaluation method and system based on RGB images and anatomical constraints, and belongs to the field of medical image analysis. The method comprises the following steps: carrying out preprocessing and anatomical symmetry correction on an upright position back RGB image shot by the intelligent mobile equipment for a single time; extracting regions of interest of shoulder blade and iliac crest mark points by using a lightweight segmentation network; generating an initial depth map through a depth estimation model fusing a vertebra attention mechanism and a multi-constraint loss function; carrying out physiological scale calibration on the initial depth map based on height and weight parameters; a cascade GAN is adopted to eliminate clothes interference and reconstruct a back three-dimensional surface; based on biomechanical characteristics, NURBS parameters are adjusted in a self-adaptive mode to fit a spine three-dimensional curve; and outputting a coronal plane Cobb angle, a trunk rotation angle and a sagittal plane curvature parameter. Through monocular depth estimation and dynamic evaluation of anatomical constraint, the problems of radiation risk, subjective error and clothes interference of a traditional method are solved.

The invention discloses an eye movement tracking method, an eye movement tracking device, eye movement tracking equipment, computer equipment and a storage medium. The method comprises the following steps: calculating calibration gaze information and a calibration pupil position according to first event data collected by an event camera; determining a region of interest based on the calibrated pupil position; processing second event data collected by the event camera according to the region of interest to generate region of interest data, the collection time of the second event data being later than the collection time of the first event data; according to the region-of-interest data, calculating a current fixation position offset; according to the calibration gaze information and the current gaze position offset, the current gaze information is determined, the calibrated pupil position is updated based on the current gaze position offset, the gaze direction, the moving direction and the moving distance of the pupil are accurately captured in real time by utilizing the characteristics of high time resolution and the like of the event camera, and the eye movement tracking effect is effectively improved.

The invention discloses a small sample target detection method based on target feature enhancement and semantic fusion perception, and relates to a computer vision technology. A data set is divided into a query set and a support set, after features are extracted through a backbone network, background noise in the support features is inhibited through a dynamic hypergraph construction module, and high-order semantic association of a target area is enhanced; fusing the category name text semantics and the image specific prototype by using a semantic fusion perception module to generate a high-discrimination category prototype; modeling semantic distribution by means of a variational auto-encoder, and extracting variational features; and fusing the region-of-interest features and the variation features through a channel attention mechanism to realize classification and regression. According to the method, the prototype characterization capability is effectively improved, and experiments show that the method remarkably improves the detection precision and is suitable for labeling sample scarce scenes. More accurate small sample target detection is realized by enhancing the feature expression of the support feature map and the semantic meaning of the category prototype, and higher robustness and recognition performance are shown in a complex scene.

The present application relates to the field of image processing, and discloses a method and system for processing an intracranial large vessel image, an electronic device, and a medium. The method comprises: acquiring an intracranial large vessel original image of an object to be identified and an intracranial large vessel original image of a sample subject; on the basis of the intracranial large vessel original images, using a cerebrovascular segmentation model to obtain cerebrovascular mask images; calculating regions of interest and bounding boxes of the cerebrovascular mask images; on the basis of the bounding boxes of the regions of interest, selecting corresponding regions of interest from the intracranial large vessel original images; respectively preprocessing mask image regions of interest and original image regions of interest to obtain images to be processed; marking a target area in the image to be processed of the sample subject; using a training set to train a convolutional neural network model to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of said object into the cerebrovascular occlusion classification model to obtain a target area identification result. The present application can improve the accuracy of target area identification.

The invention belongs to the field of image defect region segmentation, and discloses a power line image defect region segmentation method, system and device and a medium, and the method comprises the steps: obtaining power line image data, and analyzing the power line image data to obtain original image data; generating a gradient response diagram based on the original image data, obtaining a preliminary geometric contour of the power equipment, matching the preliminary geometric contour with a power equipment template, and establishing line structure information; based on the line structure information, selecting pixel coordinates of the power equipment, and delimiting a target attention area according to the pixel coordinates of the power equipment; and extracting local features of the target region of interest to obtain a preliminary defect segmentation map, and generating a defect region mask according to the preliminary defect segmentation map. According to the method, the target attention area is delimited, background interference is reduced, information is synthesized in the attention area to judge the defect mode, the defect area is segmented, the result is analyzed and optimized through the connected area, and the defect detection accuracy, robustness and defect type identification capability can be improved.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention belongs to the field of eye movement data tracking, particularly relates to a pilot eye movement tracking method, system and equipment based on multi-camera fusion and a storage medium, and aims to solve the problems that in the prior art, the visual angle of a traditional eye tracker is limited, and sight estimation errors are caused by head movement. The method comprises the following steps of: constructing a multi-view visual perception network by deploying multiple cameras and eye trackers in a cockpit; multi-source data space-time alignment is achieved through space coordinate system mapping and time synchronization, and head motion errors are compensated by combining dynamic cutting of eye interested areas and three-dimensional head posture estimation. The geometric path generates a physical driving vector based on iris fitting and an eyeball model, and the data path regresses the vector through a deep learning model; and converting to a world coordinate system through a head posture after fusion. High-precision data of the eye tracker is preferentially adopted in the central area, and field-of-view data of the system is expanded in the edge area. According to the invention, the tracking precision under field angle coverage and motion interference is obviously improved.

The invention relates to the field of image processing, in particular to an automobile part processing burr detection method, which comprises the following steps of: positioning an automobile part, acquiring an edge image to define an ROI (Region of Interest), performing sub-pixel extraction on an edge in the ROI, and performing multi-scale decomposition by applying discrete wavelet transform to obtain detail coefficients. The fractal dimension of the edge local contour is calculated, the expected detail energy of the edge local contour under the corresponding wavelet scale is deduced, then the adaptive threshold adjustment amount is obtained through calculation, the final local adaptive threshold is generated in combination with the basic threshold, and the threshold is used for judging the actual detail coefficient to accurately position burrs. And then carrying out feature quantification on the positioned burrs. And finally, comparing the quantized burr characteristics with a preset quality standard, judging whether the part is qualified or not, and outputting a result. According to the method, the wavelet threshold is adaptively adjusted by introducing fractal features, so that the accuracy and robustness of burr detection under different edge conditions are improved.

The invention belongs to the technical field of spacecraft pose estimation, and particularly relates to a cross-domain spacecraft pose estimation method based on mask self-distillation domain adaptation, and the method comprises the steps: 1, inputting a complete image, and employing a Faster R-CNN algorithm to position a spacecraft bounding box; the robustness of the model is improved by applying a track environment data enhancement strategy; and extracting a target ROI region as key point regression network input based on the detection frame. 2, dividing ROI (Region of Interest) data of a source domain and a target domain; and optimizing heat map supervision loss learning key point positioning knowledge. 3, inputting random mask enhanced target domain data into the student model; inputting original target domain data into the teacher model; a learnable shared prototype space is constructed, and self-distillation is guided through heat map consistency loss and semantic consistency loss. And 4, jointly optimizing the loss of the key point regression network 3, and realizing progressive migration of source domain annotation knowledge to a target domain. And 5, solving the 6D pose of the spacecraft relative to the camera through the EPnP. According to the invention, robust six-degree-of-freedom pose estimation of the target spacecraft is realized.

The invention provides a ship target segmentation method and system based on an improved Harris eagle optimization algorithm, and relates to the technical field of image processing and mode recognition. According to the technical key points, the method comprises the following steps: processing an airborne grayscale image including a sky domain, a sea area, a marine ship and a coastline, and obtaining a region-of-interest image; obtaining an optimal segmentation threshold value by using an improved Harris eagle optimization algorithm, and segmenting the airborne grayscale image by using the optimal segmentation threshold value to obtain a segmented image based on the optimal segmentation threshold value; and fusing the region-of-interest image and the segmented image based on the optimal segmentation threshold to obtain a final segmented image. According to the method, the high response area of the ship target is accurately locked through the SIFT features, and adaptive threshold optimization is realized in the target gray band in combination with the improved Harris eagle optimization algorithm, so that the ship target segmentation precision and robustness under the complex sea condition are remarkably improved, and high-reliability technical support is provided for maritime affair monitoring and other scenes.

The invention provides a magnetic resonance image self-supervision super-resolution reconstruction method and system based on a prior guide diffusion model. The method comprises the steps of image preprocessing, pseudo-pairing data set construction, prior condition construction, model training, model testing and three-dimensional isotropic volume reconstruction. According to the method, the dependence on a real high-resolution image is remarkably reduced by constructing a self-supervised pseudo-pairing mechanism, and high-quality reconstruction from anisotropic MRI to an isotropic image is realized; meanwhile, by introducing multiple medical priori including high-frequency residual, edge structure and region-of-interest guidance, detail retention and structural boundary definition of the image are effectively improved, the convergence speed and robustness of the model are improved while anatomical fidelity is guaranteed, and the method is suitable for large-scale popularization and application. And a technically feasible and clinically applicable solution is provided for high-resolution reconstruction of medical images.

A computer program product interacts with machine-readable mediums with instructions for automated target recognition. It captures a video of a specified region using detectors on a vehicle. Raw image frames from the video undergo pre-processing, followed by feeding through long and short-range target detection pipelines. Image frames are downscaled in the short-range pipeline, generating image windows and applying a trained convolutional neural network (CNN). Full resolution and non-redundant image chips are created. Detection region of interest (ROI) proposal lists are generated and analyzed with the CNN, producing frame detection lists with detected targets. These lists are stacked and processed using a multi-target Kalman filter, ultimately creating a track list of targets for monitoring.

A method of identifying road distress on a road based on imagery of the road is disclosed. The method is performed by a processor and comprises the steps of: identifying a region of interest from road regions detected in the imagery of the road; detecting one or more road distresses present in the region of interest by identifying features associated with physical characteristics related to road distress in an original image comprising the region of interest; detecting one or more road distresses present in the region of interest by identifying features associated with physical characteristics related to road distress in a corrected image obtained by applying at least one of optical distortion correction and perspective distortion correction to the original image comprising the region of interest; and identifying one or more road distresses by combining the detection based on the original image and the detection based on the corrected image. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention relates to the field of industrial visual inspection, and provides an automatic test performance benchmark construction method which comprises the following steps: dynamically generating ROI (Region of Interest) parameters; performing pixel cutting on the target area of the production line according to the dynamically generated ROI parameters to generate a high-speed image data stream, and aligning the image acquisition moment with the mechanical motion phase of the production line; selecting a matched visual detection model based on target features in the high-speed image data stream, and outputting a defect detection result by the matched visual detection model; according to defect distribution parameters of a preset test scene, the simulation defect features are embedded into the high-speed image data stream, and a mixed test data stream is generated; performing multi-device time sequence alignment on the mixed test data stream and the defect detection result, and packaging into a standardized data set containing a unified time reference; and analyzing the standardized data set, comparing the simulated defect truth value label with a defect detection result, calculating a multi-dimensional performance index of the visual inspection system, and generating a test report containing parameter optimization suggestions.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

The invention discloses a remote sensing video segmentation method and segmentation system based on text guidance, belongs to the crossing field of remote sensing image processing and computer vision, and relates to a remote sensing video segmentation method and segmentation system. The invention aims to solve the problems that the existing remote sensing video segmentation technology is poor in flexibility, cannot interact with natural languages, is insufficient in generalization ability for new categories or complex targets, and cannot meet the requirement of quickly and accurately extracting semantic information in a dynamic remote sensing scene. The method comprises the following steps: 1, acquiring a video frame sequence, and acquiring a key frame based on the video frame sequence; 2, obtaining an initial segmentation mask; 3, obtaining an optimized mask; 4, calculating a minimum bounding rectangle of the optimized mask, obtaining a bounding box of the minimum bounding rectangle, and obtaining an expanded bounding box; and 5, inputting the expanded bounding box and the video frame sequence obtained in the step 1 into an improved SAM2 video segmentation model, and outputting a frame-by-frame segmentation result of the region of interest by the improved SAM2 video segmentation model.

The invention relates to a glass flaw recognition method, system and equipment based on image enhancement and a medium. The method comprises the following steps: acquiring a multi-view image set of to-be-detected glass; respectively carrying out region-of-interest extraction on the vertical dark field image, the horizontal dark field image, the vertical bright field image and the horizontal bright field image to obtain multiple groups of image blocks; combining a plurality of image sub-blocks with the same position in each group of image blocks to obtain a standard image sub-block group; inputting the standard image sub-block group into a pre-trained glass flaw detection model to obtain a detection result and a classification probability corresponding to each image sub-block at the same position; and based on a preset channel fusion weight, performing weighted fusion on the detection results at the same position according to the corresponding classification probability to obtain a glass flaw recognition result at the corresponding position. According to the method, through region-of-interest extraction, channel separation feature extraction and fusion probability output under an independent view angle, the accuracy of glass flaw automatic identification is improved.

The invention relates to an environment sensing method based on a laser radar and a high-precision map, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring environmental point cloud data in real time through a laser radar; dividing a region of interest based on a high-precision map; performing target identification and classification on the environment point cloud data in the region of interest; dividing the targets in the region of interest to a dynamic barrier layer or a static barrier layer according to the motion attributes, geometric characteristics and point cloud density of the targets; and based on the road attribute information of the high-precision map, target classification and tracking results are optimized, and path planning and decision making are assisted. According to the invention, the environment perception capability in the vehicle driving process can be improved, the calculation burden is effectively reduced, the false detection rate is reduced, and the adaptability and decision reliability of the system in a complex scene are improved.