272 results about "Image selection" patented technology

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 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 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 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 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.

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.

The embodiment of the invention provides an infrared and visible light image fusion method and system based on a pseudo-supervised generative adversarial network, and a medium, and the method comprises the steps: inputting an infrared image and a visible light image, carrying out the foreground and background segmentation, selecting a distance measurement mode based on the infrared segmentation image and the visible light segmentation image, and calculating the distance, performing weighting processing on the infrared distance image and the visible light distance image, and fusing the processed images to obtain a real pseudo ground image; performing analysis and fusion enhancement on the infrared image and the visible light image based on a pseudo-supervised generative adversarial network to obtain an enhanced fusion image; performing comparative analysis on the enhanced fusion image and the pseudo ground real image to obtain a judgment result, and adjusting parameters of the pseudo-supervised generative adversarial network based on the judgment result; by introducing a pseudo ground real image and a pseudo supervision generative adversarial network, the image is subjected to fusion weight fusion, and network parameters are dynamically adjusted, so that the quality of the fused image is remarkably improved.

A system, method, and computer program product for generating a resulting image from a set of images is disclosed. The method comprises receiving an image set that includes a first image of a photographic scene based on a first set of sampling parameters and a second image of the photographic scene based on a second set of sampling parameters, and generating a resulting image based on the first image and the second image according to depth values in a selection depth map. Each distinct depth value in the selection depth map corresponds to a different image in the image set.

Described herein are systems, methods, devices, and other techniques for comprehensive and automated evaluation of digital images generated from artificial intelligence (AI) models in order to promote accurate representations of real-world content. Prompts are received at the system that are then passed to both a search engine and a generative AI model. Synthesized digital images are obtained from the generative AI model. The top-matching image from the search engine is used as a verification of the ground truth of the synthesized digital images. A realism score is generated for each synthesized digital image that characterizes the accuracy of the synthesized digital image with reference to the verification image. The realism score can be used to assist and expedite the image selection process, as well as serve as input to fine-tune the performance of generative models.

A media application receives an initial image, user input that selects one or more objects in the initial image, and a textual request to generate an output image that modifies the one or more selected objects in the initial image. The media application generates a user-selected mask that includes object pixels corresponding to the one or more selected objects. A diffusion model receives the textual request to generate the output image, a depth map, and the user-selected mask, where the diffusion model is trained to generate output pixels that are not associated with a human subject. The diffusion model outputs the output image that satisfies the textual request.

The invention relates to the technical field of visual inspection of photovoltaic modules, in particular to a line scanning solar module image self-adaptive splicing method based on a battery piece grid structure. The method comprises the following steps: synchronizing sub-images of the solar module and corresponding cell grid parameters, and extracting key points of each sub-image based on the cell grid parameters; determining a splicing mode of the sub-images, and calculating a position posture of each sub-image based on the key points and the splicing mode; determining the placement position of each sub-image based on the position attitude; selecting different splicing effective areas for each sub-image, and generating a standard splicing image and an extended splicing image based on the placement positions and the splicing effective areas; mapping the cell grid parameters to a standard spliced image and an extended spliced image to obtain a complete spliced image; according to the invention, the adaptive splicing of the solar module image is realized, the splicing precision of the image is improved, and the splicing precision is ensured to meet the technical requirements of photovoltaic module defect detection.

This map creation device is provided with: an acquisition unit that acquires images captured by an imaging device; a selection unit that selects an acquired image as a key frame, each time the amount of movement of a vehicle exceeds a predetermined value; a feature point detection unit that detects a plurality of feature points from the images of the key frames; a feature point selection unit that excludes, from the association of feature points, feature points, among the detected feature points, for which a distance, on the images, between consecutive key frames is smaller than a predetermined value and a luminance value is greater than a predetermined value; a feature point association unit that associates the feature points between the images of the key frames on the basis of features of the feature points that have not been excluded; and a position calculation unit that calculates three-dimensional positions of the associated feature points.

The application discloses a kind of hydraulic tunnel real-time defect monitoring method and system, the method utilizes fuzzy algorithm to calculate out the danger value of the hydraulic tunnel to be detected according to the real-time environmental factors of the hydraulic tunnel to be detected, and according to the danger value of the hydraulic tunnel to be detected obtained by calculation, for the real-time acquisition of the hydraulic tunnel image selects appropriate convolution layer and autonomous intention layer, so that the tunnel image recognition model can accurately identify the defects of the real-time image of the hydraulic tunnel to be detected, improve the accuracy of defect identification, and according to the danger value calculated according to environmental factors and defect identification result, determine the maintenance strategy of the hydraulic tunnel to be detected, improve the accuracy and effectiveness of maintenance strategy, solve the problem that the prior art only tunnel defect identification is carried out to the collected tunnel image data, while lacking the influence of internal environmental factors of hydraulic tunnel, leading to unable to accurately identify the defects of hydraulic tunnel, and then reduce the accuracy and effectiveness of maintenance strategy.

A system for determining when an occupant in a motor vehicle seat and restrained by a seat belt restraint system has an incorrect body posture, the system comprising: a seatbelt buckle sensor for detecting the presence of a seatbelt buckle locking plate in the seatbelt buckle to determine whether the seatbelt is fastened; a seat belt dispensing sensor for detecting a first seat belt dispensing length and for detecting a second seat belt dispensing length; an occupant posture sensor for capturing an image of an occupant on the vehicle seat; a control module in communication with the seat belt buckle sensor, the seat belt release sensor, and the occupant posture sensor, the control module having executable code to: Determine the presence of the seat belt locking plate in the seat belt buckle; Determining whether the difference between the first seat belt output length when the seat belt buckle is present in the seat belt buckle and the second seat belt output length is greater than a seat belt output length change threshold; Comparing the image of the occupant captured by the occupant posture sensor with at least one stored image and posture zone if the difference between the first seatbelt output length and the second seatbelt output length is greater than the seatbelt output length change threshold; and Determine whether the occupant has an incorrect posture by comparing the captured image of the occupant with at least one of the stored images and the posture zone. wherein the occupant posture sensor is an ultrasonic sensor, wherein the stored image is at least one of a reference posture image and an actual initial posture image, wherein the posture zone is at least one of a reference posture zone and an actual initial posture zone, where the actual initial posture zone is determined from an actual initial posture image, wherein the control module further comprises executable code for storing the first seatbelt output length within at least a first time window and a second time window, wherein the reference posture image is selected based on the image of the occupant detected by the occupant posture sensor, wherein the reference posture zone is selected from a plurality of stored reference posture images based on a shape of the occupant determined from the captured image of the occupant, wherein the control module further comprises executable code for comparing the image of the occupant captured by the occupant posture sensor with a stored image, further comprising executable code for capturing an image of an occupant on the vehicle seat within at least a first time window or a second time window and storing the image as the stored image, wherein the control module further comprises executable code to determine whether the occupant has an incorrect posture based on the comparison of the captured image of the occupant and the stored image and / or the posture zone, and the posture zone further comprises executable code to determine whether a head, a torso, or a leg of the occupant exceeds a posture threshold.

A control device (10) is provided with a scaling determination unit (142), an image selection unit (143), and a reconfiguration unit (144). When a time constraint corresponding to each of at least one of one or more manufacturing facilities (40) has been changed, the scaling determination unit (142) determines, on the basis of the time constraint corresponding to each of the one or more manufacturing facilities (40) after the change, whether to execute scaling processing for changing a calculation resource to be allocated to a virtual calculation environment. When it is determined that the scaling processing is to be executed, the image selection unit (143) selects, as the selection image, an image of the virtual calculation environment that satisfies the time constraint corresponding to each of the one or more manufacturing facilities (40) after the change, said selection being made on the basis of the amount of the calculation resources allocated to the virtual calculation environment. The reconfiguration unit (144) reconfigures the virtual calculation environment on the basis of the selection image.

A method for in-situ scan band localization includes generating an in-situ scan band localization database by selecting a plurality of high contrast alignment targets from a set of pixel-to-design alignment images within a pixel-to-design alignment database. The method includes executing a first runtime scan band of a plurality of runtime dies, wherein each runtime frame associated with the first runtime scan band includes a high contrast alignment target from the in-situ scan band localization database. The method may include determining one or more offset values for each runtime die based on the plurality of high contrast alignment targets and one or more runtime frames associated with the first runtime scan band. The one or more offset values may include an x offset value and a y offset value for each die. The method may include adjusting an initial stage position based on the determined one or more offset values prior to executing a second runtime scan band.

The application provides a label distribution image selection method and system based on representation and diversity, comprising: acquiring an image dataset and performing image feature extraction; training an initial label distribution learning model according to a small part of labeled dataset; calculating a dissimilarity matrix and a diversity matrix by using the features of the unlabeled image sample set and the label distribution of the unlabeled image sample predicted according to the label distribution learning model; and selecting valuable image samples according to the dissimilarity matrix and the diversity matrix for query. The application actively selects the most valuable images as training samples from the two aspects of representation and diversity, constructs a label distribution model of active learning, and has great significance for improving the performance of the label distribution model under the condition of insufficient image label data.

Global index values for selecting global reshaping functions for an input image are generated using luminance codewords in the input image that have a relatively low dynamic range. Image filtering is applied to the input image to generate a filtered image. Filtered values of the filtered image provide a measure of local brightness levels in the input image. Local index values for selecting particular local reshaping functions for the input image are generated using the global index values and the filtered values of the filtered image. A reshaped image of a relatively high dynamic range is generated by reshaping the input image with the particular local reshaping functions selected using the local index values.

There are provided data processing apparatus, method, and program which can improve accuracy of image selection. A data processing apparatus that processes data of a plurality of slices acquired by a spectral CT device includes a processor, in which the processor outputs a first image generated on the basis of the data to a display destination, receives setting of a region that is an analysis target, on the first image output to the display destination, analyzes a second image of the plurality of slices generated on the basis of the data to generate statistical information indicating a distribution of a material in the region for each slice, and outputs the statistical information to the display destination.

The present application belongs to the technical field of artificial intelligence, and discloses a wood board sorting method and system based on machine learning, which comprises: establishing a multi-scale normal wood board image database; training wood board detection models for each size specification to form a multi-scale wood grain deviation detection model set; obtaining a wood board image to be sorted, determining the optimal segmentation size according to the size; segmenting the wood board image into multiple sub-images; selecting a corresponding model to judge the sub-images and mark the defect images; calculating the overall defect index, defect distribution index and defect continuity index to comprehensively evaluate the wood board quality; and determining the wood board grade according to the evaluation result and sorting. The method not only considers the number of defects, but also analyzes the position distribution and size continuity of the defects, realizes multi-dimensional quality evaluation, can more accurately reflect the actual use value of the wood board, and improves the scientificity of wood board grading and the utilization efficiency of wood resources.

The invention discloses a cigar tobacco tissue thickness measuring method and a cutting device, and relates to the technical field of tobacco tissue thickness measurement, and the method comprises the following steps: S1, carrying out screening treatment before cigar tobacco and rootstock cutting; s2, device cutting module selection and parameter setting work; s3, tobacco plant rhizome and leaf tissue structure slices are cut; s4, tissue slice electron microscope scanning and image selection work; and S5, carrying out tissue slice image part identification and division and average thickness calculation. The method solves the problems that in the prior art, in thickness measurement of different tissue structures of tobacco leaf sections, most methods or leaf surface image analysis systems are used for estimating and reading scale scales, and due to the fact that thickness distribution of tissue parts of leaves is not uniform and consistent, a traditional scale measurement method is large in error due to different selected measurement point positions, and the measurement accuracy is poor. And the average thickness of each part cannot be quickly and accurately measured.

The invention discloses a digital material selection method and material selection equipment for an AIGC home design scene, relates to the technical field of artificial intelligence, and is used for improving the accuracy of selecting a physical entity material according to an AIGC image. In the method, a material selection device identifies and quantifies a non-physical rendering style fingerprint of an AIGC image, and rejects the non-physical rendering style fingerprint from a feature level to obtain an intrinsic texture topology tensor representing a texture structure; and aligning the intrinsic texture topology tensor to a feature space of a real physical building material through a preset domain generalization feature mapping network, thereby forcibly ignoring the interference of an AIGC rendering style in a retrieval process, focusing on the comparison of the texture topology structure, and further improving the accuracy of selecting a physical entity material according to the AIGC image.

An object of the system according to the embodiment is to enable a programming beginner to obtain an idea of an appropriate product and concentrate on learning.SOLUTION: A system according to an embodiment includes a skill selection unit, an image selection unit, a deliverable generation unit, and a learning material provision unit. The skill selection unit selects a programming skill of the user. The image selection unit selects an image of a product that the user wants to create. The deliverable generation unit generates an idea of the deliverable based on the information selected by the skill selection unit and the image selection unit. The learning material providing unit provides learning materials based on the information selected by the skill selection unit and the image selection unit.SELECTED DRAWING: Figure 1

The invention discloses an image-text associated cognitive training system and a method thereof, and relates to the technical field of cognitive training and artificial intelligence. According to the method, image memorability calculation, image-text semantic matching and user behavior driven personalized recommendation and cognitive ability evaluation mechanisms are fused, the problems that in the prior art, image selection is random, image-text pairing is extensive, training lacks personalized adaptability and feedback analysis and the like are solved, and personalized, self-adaptive, efficient and intelligent cognitive training is achieved. The system comprises the following core modules: an image memorability database module; an image automatic matching module; a personalized recommendation module; and a cognitive training module.

The following operations are performed for an encoding process for input video. One of multiple padding methods is selected to perform padding of an outer region defined outside a region. The region is from a picture used in the encoding process, and the selecting is performed based on information providing a ranking of the multiple padding methods. The selected padding method is performed to form an extension region defined outside the region. The following operations are performed for a decoding process using a bitstream. A selected padding method is determined to use in relation to a region of a picture from the bitstream. The selected padding method is applied to the region to form an extension region that is defined outside the region.

The invention provides an image processing method and device, electronic equipment and a storage medium, and the method is applied to the image processing device, and comprises the steps: obtaining a target image and N primary color concentration sets corresponding to the target image; generating N preview images corresponding to the N primary color concentration sets according to the target image and the N primary color concentration sets; displaying the N preview images; determining a target preview image in response to a preview image selection operation triggered by a user; and adjusting a primary color concentration parameter of the image forming device according to the primary color concentration set corresponding to the target preview image. A user can see the presentation effect of the target image on the image forming medium before the image forming device executes the image forming operation, and after the image forming device completes the image forming operation and generates the output image, the output image is almost the same as the preview image, so that the image forming efficiency is improved. At the moment, the color of the actual output image is the same as the color of the preview image seen by the user, and the user experience is improved.

The present application is applicable to the technical field of data processing, and provides an image processing method and apparatus, a terminal device, and a storage medium. The method comprises: in response to a first operation, determining at least one first image from a gallery; displaying a thumbnail of the at least one first image in an image selection interface; and in response to a second operation, obtaining at least one second image on the basis of a target image among the at least one first image. In the embodiments of the present application, when a user opens an image selection interface of an application having an AI image processing function, images in a gallery can be screened so as to determine a first image that can be used for new AI image processing, reducing the number of displayed images, thereby reducing the difficulty for users in selecting images, and improving the operation efficiency of users.

An example method for selecting product images for training a machine-learning model includes obtaining product images to include in an image population; receiving an indication of an image selection strategy for determining if a product image is to be included in a set of images of interest; determining image transforms based on configuration data for the indicated image selection strategy, wherein the image transforms perform image manipulation operations to obtain transformed image data for each of the product images in the image population; selecting a subset of images from the image population for inclusion in the set of images of interest based on the indicated image selection strategy and the transformed image data; determining one or more descriptive labels and applying the one or more descriptive labels to the respective sets of images; and training an inspection model for a product inspection system based on the labeled images.

The present disclosure relates to a method for synchronous navigation and annotation of a set of interrelated heterogeneous medical images.SOLUTION: The method includes correlating the first and second sets of sequential tomographic medical images of the subject based on the first metadata and the second metadata to generate a plurality of correlated image pairs, superimposing each correlated image pair to generate a fused image corresponding to the correlated image pair, selectively and sequentially rendering the correlated image pair, the fused image, and / or a third tomographic medical image of the subject, and selectively rendering a visual indication of a pixel of interest and / or a region of interest for the correlated image pair, the fused image, and / or the third tomographic medical image. A machine learning algorithm is used to identify pixels in the medical image that are associated with the medical condition. A graphical annotation is rendered at or near the location of the pixel.SELECTED DRAWING: Figure 3