490 results about "Image selection" patented technology

The invention relates to the technical field of lung perfusion, in particular to an AI-based lung perfusion evaluation system, which comprises an image quality screening module, a blood vessel positioning analysis module, a blood flow velocity measurement module, a blood vessel anomaly analysis module and an anomaly response module, and is characterized in that the image quality screening module monitors the pixel density of an image and the variation amplitude of color gradient. According to the method, through real-time image definition evaluation and screening, the image selection process is optimized, it is ensured that all the images used for analysis reach the high-quality standard, the gray level change and the edge contour of the blood vessel are automatically calculated, the potential lesion area is accurately positioned and marked, more detailed blood vessel structure analysis is provided, and the accuracy of blood vessel analysis is improved. In addition, the system can rapidly analyze the speed deviation of the blood vessel segment, timely identify the abnormal blood flow, effectively improve the judgment speed of diseases such as pulmonary embolism or pulmonary hypertension, enhance the efficiency of coping with emergency medical conditions through automatic abnormal detection and recording, improve the accuracy and speed of judgment, and support more effective clinical decisions.

The invention discloses a visual large model Token adaptive optimization method, system and device based on differential evolution and a medium, and the method comprises the steps: carrying out the data processing of an image classification data set, an instance segmentation data set and a saliency target detection data set, and obtaining all Tokens corresponding to each image through a Patch Embedding and position coding method; obtaining a plurality of groups of Tokens corresponding to each image through a random selection mode, and performing data processing to output all Tokens corresponding to each image and the plurality of groups of Tokens selected from each image; constructing a Token adaptive selection module, a self-attention optimization module and a downstream task output module; a complete Token adaptive optimization visual large model is constructed; training a reconstruction model and a complete Token self-adaptive optimized visual large model; performing model reasoning to obtain an image classification result, an instance segmentation result image and a saliency target detection result image; the system, the equipment and the medium are used for implementing the method. The method can be widely applied to various visual tasks such as image classification, instance segmentation and saliency target detection.

The invention discloses an underground comprehensive pipe gallery electric power cabin plugging selection method based on a fire situation, and relates to the technical field of urban underground management. According to the method, FDS fire dynamics simulation software is utilized to construct a blocked underground utility tunnel electric power cabin full-size model and a non-blocked underground utility tunnel electric power cabin full-size model, fire spreading conditions under the two models are simulated, extinguishing time and fire spreading images are recorded, and fire evolution under different conditions is determined; based on the extinguishing time and the fire spreading image, whether plugging is carried out or not is selected, the safety and fireproof performance of the underground comprehensive pipe gallery power cabin are improved, further spreading of the fire is stopped in time, the resident safety is improved, social resources are guaranteed, follow-up reconstruction is facilitated, and resource waste is reduced.

The invention discloses a to-be-detected object surface image acquisition device, an image selection method and a defect detection method. The to-be-detected object surface image acquisition device comprises a projector, a diffusion plate, a reflector and a camera. The projector is suitable for projecting stripes; the diffusion plate is suitable for diffusing fringes projected by the projector to the surface of the to-be-measured object; the reflector moves between a first position and a second position relative to the to-be-measured object, and at the first position, the reflector shields a light path from the projector to the diffusion plate. And at the second position, the reflecting mirror avoids a light path from the projector to the diffusion plate. The camera is suitable for receiving a signal reflected by the surface of the to-be-measured object after the diffusion plate enters the surface of the to-be-measured object so as to obtain a phase deflection technology (PMD) image, or receiving a signal diffused by the surface of the to-be-measured object after the reflection mirror enters the surface of the to-be-measured object so as to obtain a fringe projection profile measurement (FPP) image. A phase deflection technology and a fringe projection profile measurement technology are fused in one set of defect detection system, rapid switching of two three-dimensional measurement defect detection technologies is realized for products with complex materials and different surface roughness, and the defect detection system is ensured to be high in efficiency, high in detection capability, wide in applicability and high in detection rate.

The invention discloses a screen orange peel detection method and system based on a phase deflection technology. The method comprises the following steps that a camera collects images in a stripe light source image selection and projection mode; distortion correction is carried out on the image acquired by the camera; performing phase extraction and unwrapping on the corrected image; according to the unwrapped phase data, performing three-dimensional reconstruction and gradient calculation on the image to obtain image surface height and gradient information thereof; curvature calculation and defect feature extraction are carried out according to the surface height distribution and gradient information of the reconstructed image; according to the curvature and defect feature information, the orange peel defects are quantitatively evaluated, rapid, high-precision and non-contact orange peel defect detection is achieved, the detection efficiency and accuracy are improved, and the detection cost is reduced.

In one aspect, the method includes causing presentation of a camera interface, the camera interface to display a first image captured by a camera of a computing device of a first user, causing presentation of a plurality of augmentation icons within the camera interface, each of the image augmentation icons being associated with a respective image augmentation mechanism, detecting user selection of an auxiliary augmentation icon of the plurality of image augmentation icons, the second image icon being associated with an second image augmentation mechanism, determining an second image selection criterion for an second image augmentation mechanism, selecting an second image based on the second image selection criterion, the second image being selected from a plurality of photographs associated with the first user, and overlaying the second image on the first image to generate a composite image that is presented within the camera interface.

Techniques are described for learning feature representations of medical images using a self-supervised learning paradigm and employing those feature representations for automating downstream tasks such as image retrieval, image classification and other medical image processing tasks. According to an embodiment, computer-implemented method comprises generating alternate view images for respective medical images included in set of training images using one or more image augmentation techniques or one or more image selection techniques tailored based on domain knowledge associated with the respective medical images. The method further comprises training a transformer network to learn reference feature representations for the respective medical images using their alternate view images and a self-supervised training process. The method further comprises storing the reference feature representations in an indexed data structure with information identifying the respective medical images that correspond to the reference feature representations.

The invention relates to the field of machine learning and image processing, provides an image restoration data set distillation method based on uniform image selection and neural representation, and solves the problems of large calculation amount, poor distillation performance and the like of an existing matching-based method. The method comprises the steps of training a restoration model in an original data set; based on the restoration model, calculating peak signal-to-noise ratios of restoration results of the low-quality samples in the training set and sorting the peak signal-to-noise ratios; removing partial images with the lowest peak signal-to-noise ratio and the highest peak signal-to-noise ratio, and uniformly selecting the images according to the compression ratio; respectively training the corresponding neural representation of each selected image; the mask nerve represents a part of pixels with the minimum reconstruction loss; reconstructing a recovery data set based on the mask coordinates, the distillation data and the neural representation; and training a deep learning model on the reconstructed data and testing the restoration performance. The method has the advantages that the image restoration model is efficiently trained, the storage space of an image restoration data set is greatly reduced, and iterative updating of a restoration algorithm is accelerated.

A system comprising a first image processing arrangement and a second image processing arrangement, wherein the first image processing arrangement comprises a controller configured to: a) receive an image; b) select a task and a task identifier associated with task data; c) compress the image based on the task data; and d) transmit the compressed image to the second image processing arrangement for processing, and wherein the second image processing arrangement comprises a controller configured to: e) receive the compressed image and task identifier; f) retrieve task parameters associated with the task identifier; g) process the compressed image based on the task parameters; h) determine results and i) transmit the at least indications of the determined results to the first image processing arrangement, and the controller of the first image processing arrangement is further configured to: j) receive at least indications of a result of the processing from the second image processing arrangement; and k) indicate the result.

Computing an output image of a dynamic scene. A value of E is selected which is a parameter describing desired dynamic content of the scene in the output image. Using selected intrinsic camera parameters and a selected viewpoint, for individual pixels of the output image to be generated, the method computes a ray that goes from a virtual camera through the pixel into the dynamic scene. For individual ones of the rays, sample at least one point along the ray. For individual ones of the sampled points, a viewing direction being a direction of the corresponding ray, and E, query a machine learning model to produce colour and opacity values at the sampled point with the dynamic content of the scene as specified by E. For individual ones of the rays, apply a volume rendering method to the colour and opacity values computed along that ray, to produce a pixel value of the output image.

A multi-selection shutter camera application and method selectively sends images to different artificial intelligence and innovative platforms for fast sharing and informational purposes. An electronic device with a touch sensitive display and a processor is utilized. A capture screen on the display includes a multi-shutter view (a live view and at least two selective capture buttons (e.g., shutters)) for capturing media content. The method may include receiving input from the buttons to capture the content and direct it to an artificial intelligence platform; processing the content information based on the selected button; analyzing the content through parameters and show options to the user on the same screen that displays the content; and presenting a plurality of selectable options related to the user's selected shutter and intent of capturing the content, including, but not limited to uses related to at least one of the following: discovery, shopping and sharing functionality alternatives.

The invention relates to an image phase shift evaluation and phase calculation method and system based on an image selection mechanism, and belongs to the technical field of optical measurement. The method comprises the following steps: acquiring an original video, and obtaining a reference interferogram group through an image acquisition module based on the original video; obtaining an initial position interferogram based on the reference interferogram group, optimizing the recombined interferogram based on the initial position interferogram through an anti-vibration algorithm, and outputting a recombined interferogram anti-vibration sequence; calculating a phase shift accuracy index value based on the recombined interferogram sequence, presetting a phase shift accuracy index threshold, and extracting and outputting the recombined interferogram sequence of which the phase shift accuracy index value is greater than the phase shift accuracy index threshold as the target interferogram group; and calculating a phase shift quantity of the target interferogram group through a phase calculation module, calculating a phase shift evaluation index based on the phase shift quantity, and judging and outputting final phase information through the index. And image phase shift evaluation and phase calculation based on an image selection mechanism are realized.

Disclosed are an image selection method and an electronic device. In the method, an electronic device can detect feedback information related to a user operation. The feedback information may include an optimal image selected by a decision model and a changed optimal image. The feedback information may further include images that are deleted, browsed, added-to-favorites, or shared and operation records. The feedback information may further include a facial feature in a gallery and a proportion of images including the facial feature in the gallery. The electronic device adjusts, according to the feedback information, parameters of the decision model configured to perform image selection, to obtain an updated decision model. The electronic device can perform image selection according to the updated decision model. Through the implementation of the technical solution, a selected optimal image is more in line with user habits, thereby improving convenience.

The invention discloses an AI image processing system and method, and relates to the technical field of AI image processing, and the system comprises an image collection module, an image feature extraction module, an image processing mode detection module, an image processing mode recommendation module, an image processing parameter recommendation module, and an image processing module. The image collection module is used for acquiring images, selecting and storing unprocessed images to obtain original images, and selecting and storing processed images corresponding to the original images; according to the AI image processing system and method, the processing features of the processed image can be found and recognized through the training model, so that the processing mode of the processed image can be quickly obtained, and the model can be trained by using the processing mode subsequently; and the relationship between the processing mode and the processing parameter adopted by the processing image with a good display effect and the original image is found through the training model, so that the model can obtain the recommended processing mode and the processing parameter through the original image, the image is quickly processed, and the display effect of the image is improved.

A language feature output unit (210) inputs a text describing base class image and outputs language feature. An image feature output unit (220) inputs a base class image and outputs an image feature. A base class image selection unit (230) inputs an language feature, the image feature, and the base class image and selects a base class image corresponding to the image feature having a distance from the language feature less than or equal to a predetermined threshold value. A neural network lower-layer unit (110s) inputs the base class image selected by the base class image selection unit (230) and a novel class image and outputs a value based on the base class image and a value based on the novel class image. A base class classification output unit (121) outputs a base class classification based on the base class image and the novel class image. A novel class classification output unit (122) outputs a novel class classification based on a novel class image.

The invention discloses an image editing method and device and a storage medium. The method comprises the steps that diffusion processing is conducted on an original image through a diffusion model; determining the total number of noise reduction iterations by using a first model trained in advance; and in each noise reduction processing process, sampling a network module of the noise prediction network in the diffusion model by using a pre-trained second model, and carrying out noise reduction processing by using the network module selected by sampling to obtain an edited target image. By applying the scheme of the embodiment of the invention, the first model determines the total number of iterations of noise reduction, and the second model samples the network module of the noise prediction network, so that the proper number of iterations and the proper network module can be adaptively selected for different images, and the noise reduction efficiency is improved on the basis of ensuring the image quality. The complexity of a long iteration process and a network structure is greatly avoided, and the generation efficiency of image editing is effectively improved.

The invention discloses a double-detector sparse patch attack method based on dynamic boundary adaptive optimization. The double-detector sparse patch attack method comprises the following steps of 1, performing data enhancement on an input infrared image; 2, selecting a target detector and designing a related loss function; 3, designing the shape and the position of the patch; and 4, adopting a dynamic boundary adaptive optimization method. According to the method, an infrared image with few feature textures is used as input of a target detection attack model, data enhancement is performed on the infrared image by adopting histogram equalization and two-dimensional empirical mode decomposition operation, and an input image with relatively good quality is provided for a subsequent attack process; selecting a two-stage target detector and a single-order target detector to carry out related attacks; the shape and position of the patch are designed, the attackable range is expanded, and the performance of the anti-patch is improved; and the time cost of the attack method is reduced by adopting dynamic boundary adaptive optimization.

In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

The invention discloses a dynamic holographic VCSEL chip based on current addressing mode multiplexing, a design method, a dynamic holographic display system and application. According to the current addressing mode multiplexing-based dynamic holographic VCSEL chip implementation method, a multi-mode VCSEL is combined with a current addressing mode multiplexing optical digital hologram, so that an ultra-compact holographic system and an ultra-high switching frame rate are realized. A holographic image is coded using an orthogonal OAM state, and a dominant OAM mode component with a maximum weight coefficient is selected to minimize channel crosstalk. The hologram is directly integrated on the surface of the VCSEL through a 3D laser nanometer printing technology to form a chip comprising a hologram array. And dynamic holographic display is realized by changing the injection current. The method fully considers the crosstalk between addressable current mode multiplexing, remarkably reduces the crosstalk in the optical multiplexing holographic technology through a systematic mode selection process, and improves the quality and efficiency of holographic display and information processing.

An image display method applied to fields, including artificial intelligence (AI) and human computer interaction, includes detecting an input operation; displaying an image selection interface on a display in response to the input operation; determining at least one image related to the input operation from a group of associated images in a local memory or a cloud storage; displaying a thumbnail of the at least one image in the image selection interface; hiding another image; detecting a first operation where the first operation selects a first thumbnail in the image selection interface; and performing, on the first thumbnail, a processing procedure corresponding to the input operation.

The invention discloses a radar anti-interference method based on frequency domain modeling and image collaboration, and the method comprises the steps: S10, carrying out the preprocessing of an original echo, and finally packaging a preprocessing result into a three-order tensor form; s20, performing FNO interference feature extraction and identification, and outputting interference type probability distribution and an interference-to-signal ratio estimation value through a de-wharf; s30, selectively processing the image, and reconstructing the histogram into an SAR image in an equalization manner; s40, evaluating the credibility of the image, and outputting an imaging credibility score between 0 and 1 through a regression head; s50, generating a credibility-interference combined criterion for judging the reliability of the radar guidance state; s60, a loss function is designed, back propagation is carried out through a total loss function, the model gradient is updated, and model parameters are optimized; and S70, dynamic anti-interference strategy matching: retrieving candidate anti-interference actions from a preset strategy library based on the interference types, performing grading and sorting according to complexity, and when the joint risk score is lower than a set threshold value, activating the optimal high-complexity anti-interference process of the corresponding interference type.

The invention discloses an AOI back inspection reflection inhibition method based on black flannelette pasting, and relates to the technical field of AOI detection, and the method comprises the steps: placing a to-be-detected silicon wafer on a silver gray aluminum material carrying platform; starting the AOI equipment to collect a background image; black flannelette is selected as a reflection inhibition material; black flannelette is attached to the center detection area of the silver gray aluminum material carrying table, and the light source perpendicular incidence area is of a double-layer structure formed by combining a bottom-layer reflective sticker and surface-layer black flannelette. After surface mounting is completed, the background image is collected again; and performing defect identification on the acquired background image, and outputting a detection report. According to the invention, through the physical light absorption characteristic of the black flannelette, the average gray value of the reflection area of the carrying platform is reduced, the high-intensity mirror reflection interference is effectively eliminated, the difference between the background gray level and the edge gray level of the silicon wafer after mounting is obvious, image analysis software can realize accurate edge searching by setting the minimum identification gray level threshold, and meanwhile, the edge searching precision is improved. The black flannelette can improve the background gray scale stability and reduce the interference of gray scale fluctuation on a threshold segmentation algorithm.

In an image selection method according to the present invention executed by a processor using a memory, a non-detection degree based on a count of undetected objects in each of a plurality of unlabeled images is predicted by inputting the plurality of unlabeled images to an undetected object prediction model (S30), and one or more unlabeled images, to which labels are to be imparted, are selected from among the plurality of unlabeled images on the basis of the non-detection degree of each of the plurality of unlabeled images (S40).

A system includes: a model configured to determine uncertainties of results of a task based on images, respectively; a classification module configured to selectively classify the images into a first category or a second category based on the uncertainties, respectively; an embedding module configured to: determine first embeddings based on the images using an embedding function; determine second embeddings for textual explanations of errors, respectively, using the embedding function; a clustering module configured to: cluster first ones of the first embeddings for images classified in the first category into first clusters; cluster second ones of the first embeddings for images classified in the second category into second clusters; and an explanation module configured to: determine similarities between each of the first and second clusters and each of the textual explanations; and determine k of the textual explanations for one of the second clusters based on the similarities.

A system generates item images using an item image generation model. The system receives a prompt for the model. The prompt is configured to request the model generate item images for an item. The system executes the model using the prompt to generate a set of item images. The system evaluates each of the set of item images to determine performance data of each of the set of item images. The system iteratively improves the set of item images by performing the following steps. The system updates the prompt based on the performance data of each of the set of item images to obtain a new prompt. The system executes, using the new prompt, the model to generate a new set of item images, and the system evaluates the new set of item images to determine performance data of each of the new set of item images.

The invention relates to a citrus surface defect detection method based on a Retinex algorithm and deep learning, and the method comprises the following steps: collecting citrus images of different defect types under different illumination conditions, and marking defect regions and defect types; adjusting the illumination uniformity and detail contrast of the citrus image by using a Retinex algorithm to form a Retinex enhanced image; introducing an attention mechanism of Transform, and performing feature extraction and attention modeling in combination with a convolutional neural network; predicting illumination distribution of the Retinex enhanced image through a deep learning model, and correcting illumination deviation; noise or artifacts in the Retinex enhanced image are removed, and an image after dynamic preprocessing is obtained; and selecting a deep learning model for defect detection, and training a defect detection model by using the dynamically preprocessed image. According to the method, the traditional advantages of image enhancement and the adaptive ability of deep learning are combined, an efficient and accurate solution is provided for citrus surface defect detection, the problems of uneven illumination, shadow interference and the like are effectively solved, and the robustness of defect detection is improved.

The invention relates to the technical field of image classification, in particular to an image classification method and system based on a visual language large model, and the method comprises the following steps: 1, obtaining a plurality of original images, and constructing an image classification network; 2, selecting one original image from a plurality of original images, inputting the selected original image into an image classification network, and finally obtaining a category prediction result; 3, constructing a loss function by utilizing a category prediction result and a real category; 4, circulating the steps 2 and 3, minimizing the loss function until the loss function converges or the number of iterations reaches a set number of times, and updating the weight of the image classification network to obtain a trained image classification network; and 5, deploying the trained image classification network to a device end, and classifying the images by using the device end to obtain a classification result. According to the method, the problems of insufficient global information capture and low visual and language information fusion efficiency in a traditional single-mode classification method are solved, and higher classification precision and task generalization ability are realized.

The invention belongs to the technical field of medical data processing, and discloses a rectal cancer neoadjuvant therapy curative effect prediction method and system based on multi-modal knowledge decomposition. The invention provides a multi-modal data fusion algorithm based on multi-modal knowledge decomposition, which decomposes multi-modal data into a plurality of independent modal knowledge, and does not lose important inter-modal complementary information while reducing redundant information. According to the method, the unsupervised clustering algorithm and the supervised CNN algorithm are used at the same time, and compared with a traditional image selection method only using unsupervised random selection or based on information entropy, the interference of noise data on image screening can be reduced, so that representative enteroscope images and CT images can be selected.

The invention discloses a power transmission line lightning classification method, which comprises the following steps: acquiring an original image, and cutting by using a lightning target cutting method to obtain a lightning target image; selecting different background images in the original image, splicing the lightning target image with the different background images, and performing first preprocessing on the spliced image to obtain a background image; modeling the background image by using a Gaussian mixture model to obtain a background model, and calculating the matching degree between each pixel of the background model and each Gaussian model distribution to obtain a foreground; judging the foreground through a threshold cutting method to obtain a lightning region, and performing second preprocessing on the lightning region to obtain a first lightning region; and inputting the first lightning region into a deep learning model for training and classification reasoning to obtain a confidence score, and marking the lightning type and the hazard level according to the confidence score to obtain a lightning classification result, thereby meeting the requirements of power transmission line monitoring safety guarantee.