866 results about "Background image" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses an irregular small target identification method under a non-high-definition complex background image. A multi-scale feature pyramid is constructed through bidirectional feature fusion, so that the feature expression ability of a small target is enhanced; applying a space-channel attention module to adaptively highlight target features and suppress complex background interference; by introducing a composite loss function including class balance focus loss and enhanced bounding box regression loss, model training is optimized to deal with class imbalance and improve the positioning precision of an irregular target. A self-adaptive multi-scale detection head is adopted, and dynamic feature fusion and scale perception branches are utilized to realize accurate detection of targets with different sizes; according to the method, the problems of low recognition precision and poor adaptability caused by weak features, background interference and irregular shapes of irregular small targets in low-resolution and complex background images are effectively solved, and the monitoring performance in actual applications such as unmanned aerial vehicle aerial photography and remote monitoring is remarkably improved.

The invention provides a hyperspectral anomaly detection method based on double-stage attention guidance and a state space model, which relates to the technical field of hyperspectral image processing and comprises the steps of scene background modeling based on an auto-encoding network, generation of a reconstructed background image and a reconstructed residual image. Carrying out target signal enhancement on the original hyperspectral data based on the reconstructed residual image to obtain attention enhancement data, carrying out target feature depth extraction by adopting a state space model based on the attention enhancement data, generating an abnormal semantic feature image, and fusing a background guide feature image and the abnormal semantic feature image to obtain a hyperspectral image; and an abnormal probability graph is generated through adaptive gating fusion, and collaborative optimization is carried out based on a multi-task loss function. According to the method, the internal contradiction of a single network architecture is fundamentally solved, the prior guiding capability of the reconstruction method and the strong feature representation capability of the state space model are fully combined, and the accuracy and reliability of hyperspectral anomaly detection are remarkably improved.

A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a foreground image and a background image. The foreground image depicts an object and the background image depicts a scene. The foreground image is encoded, using an image encoder of an image generation model, to obtain a foreground embedding. The foreground embedding preserves the identity of the object. A composite image is generated, using the image generation model, based on the background image and the foreground embedding. The composite image depicts the object from the foreground image within the scene from the background image.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

Apparatuses, systems, and techniques to infer synthesized images including digital representations of objects blended realistically with appropriate background images. In at least one embodiment, background image data and a description of semantic features for a type of object are fused together to infer such a synthesized image using one or more neural networks.

An image processing method for extracting a subject from an image including the subject using a background image includes: generating a background removed image obtained by removing a background region not including the subject in the image from the image; compressing the background removed image; transmitting the background removed image compressed via a network; receiving the background removed image compressed via the network; decoding the background removed image compressed and received; generating a restored image by synthesizing the background removed image decoded and the background image; and comparing the restored image with the background image to extract the subject.

The invention relates to the technical field of ocean structure monitoring, in particular to a method for monitoring the scouring degree of a pile foundation of an offshore wind power station through bubble tracing, which comprises the following steps of: 1, constructing a bubble feature data acquiring an underwater image, manually segmenting and selecting key bubbles, extracting contour features of the key bubbles and expressing the contour features by vectors, a Zernike moment is used for grouping according to shapes, and a bubble feature database is constructed; and 2, identifying underwater bubbles: obtaining an underwater background image, constructing a dynamic background, obtaining an image containing suspended particles, extracting a foreground main body through a background subtraction method, comparing contour features of the foreground main body with a bubble feature database, and screening out the bubbles. By introducing an innovative method combining image recognition, feature modeling and physical inversion, the key problems of bubble misjudgment, track loss, inaccurate flow velocity and the like in a traditional bubble tracing technology are effectively solved, and high-precision, low-cost and automatic monitoring of the scouring process of the offshore wind power pile foundation is achieved.

The invention provides a film thickness deviation measuring technology and a film manufacturing technology, which can acquire information of film thickness deviation of a film in a non-contact manner through a simple and safe device structure. Provided is a film thickness deviation measurement method for a film, in which a film to be measured is used as an object to be measured, a preset background image can be captured by an imaging means, and a reference image can be acquired. The reference image is an image obtained by capturing a background image by means of an imaging means in a state in which an object to be measured is not sandwiched, or an image obtained by calculating and deriving a visual representation based on the imaging means of the background image in a state in which the object to be measured is not sandwiched. The measurement image is an image in which the object to be measured is sandwiched between the background image and the imaging means and the background image is captured by the imaging means, and the amount of displacement of the background image in the two images is calculated on the basis of the acquired reference image and the measurement image, thereby obtaining the film thickness deviation of the object to be measured.

In one aspect, a computerized method of automatically generating suggestions for a graphic design using a one click virtual button using an artificial-intelligence (AI) assistant for generating one or more contextually relevant graphics designs includes the step of receiving a digital design in a digital design tool. The digital design tool comprises the one click virtual button using the AI assistant for generating the one or more contextually relevant graphic designs. The method includes the step of detecting an event trigger for the one click virtual button. The method includes the step of with the AI assistant. The AI assistants operations includes determining and suggesting a set of alternate background images for the digital design, determining and suggesting a set of alternate font types, determining and suggesting a set of color replacements, determining and suggesting a set of alternate layout suggestions of multiple elements in the scene, determining and suggesting a set of icon replacements, determining and suggesting a set of graphs and chart replacements, determining and suggesting a set of resizes of the digital design, and determining and suggesting a set of alternate collage configuration. The method includes the step of enabling a user to selection at least of the as a user selection. The method includes the step of integrating the user selection into an updated version of the digital design. The method includes the step of displaying the updated version of the digital design in the digital design tool.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

An image background blurring method includes: obtaining an image to be blurred; performing identification processing on the image by an image semantic segmentation model in response to a target object confirmation instruction to obtain a confidence map indicating a target region of a target object in the image; generating a background image of the image according to the confidence map; performing blur processing on the background image in response to a blurring instruction including a blurring degree to obtain a blurred background image, wherein the blur processing includes determining a reduction factor according to the blurring degree and performing reduction processing on the background image according to the reduction factor; and merging the blurred background image and the image according to the confidence map to obtain a target image. The present application enhances blur processing efficiency by reducing a data computation amount during the blur processing.

Provided in some embodiments of the present application is a workpiece machining quality inspection method based on artificial intelligence. In the embodiments of the present application, the method comprises: acquiring workpiece images of the current workpiece from at least two angles, and determining a camera pose and image depth information; establishing a three-dimensional voxel model of the current workpiece; acquiring attribute information of the current workpiece, assigning the attribute information to each voxel of the three-dimensional voxel model, and performing Gaussian splatting on each voxel; determining a mask of each workpiece image in a workpiece image set, and on the basis of the masks, classifying the workpiece images into workpiece structural images and background images; and determining a workpiece point cloud of the current workpiece, calculating the similarity between the workpiece point cloud and a preset standard workpiece point cloud, and if the similarity exceeds a preset similarity threshold, determining the current workpiece to be a qualified workpiece.

The invention discloses an image restoration method and device based on a rotation verification code and a storage medium, and the method comprises the steps: obtaining a verification code image, and analyzing the verification code image to obtain an original rotation angle; splitting the verification code image into an internal rotation image and an external background image, and inputting the internal rotation image and the external background image into a visual Transform model; capturing a long-distance dependency relationship between the internal rotation image and the external background image by adopting a self-attention mechanism of the visual Transform model, and outputting a predicted rotation angle based on the long-distance dependency relationship; and restoring the verification code image through the predicted rotation angle and a preset annular weight loss function. Through the self-attention mechanism of the visual Transform model, the long-distance dependency relationship between the internal rotation image and the external background image is effectively captured, and the rotation angle prediction precision is improved, so that the reliability of verification code image restoration and the accuracy of safety evaluation are enhanced.

Coding an image that includes text content and a background is disclosed. Text portions are identified in the image. The text portions are extracted from the image to obtain a background image, where the background image includes holes corresponding to respective areas of the text portions within the image. A filled-in background image is obtained based on the background image. The filled-in background image is encoded into a compressed bitstream using a block-based encoder. The text portions is also encoded into the compressed bitstream. Encoding the text portions includes encoding respective high quality text binarization upscaled binary maps.

The invention relates to the technical field of video generation, in particular to a human and scene dynamic generation method and system based on audio and video feature fusion. The method comprises the steps of collecting a source audio signal and a target reference image, obtaining a corresponding text in a to-be-generated video based on the source audio signal, performing text dependency analysis on the text, constructing a semantic relation graph and a spatial relation graph between phrases and description objects based on a semantic relation, and generating a to-be-generated video. Generating a background image in the to-be-generated video based on the semantic relation graph and the spatial relation graph; extracting time sequence features based on the source audio signal, extracting face features corresponding to the target reference image based on the target reference image, processing the time sequence features and the face features through a weighted cross attention fusion method to obtain fusion features, and generating a human-scene video stream from the fusion features and the background image; the dynamic background highly consistent with the semantic and spatial relationship of the voice content can be intelligently generated and fused based on the voice content, and the high synchronism and collaboration of the audio and the video are improved.

The invention discloses a chromosome image generation method and device for deep learning. Performing adaptive multi-scale image enhancement processing on the chromosome mid-term image to obtain an enhanced chromosome mid-term image; obtaining a chromosome karyotype arrangement diagram based on the enhanced chromosome metaphase image; generating a chromosome set based on the chromosome karyotype arrangement diagram; sequentially merging the chromosomes in the chromosome set into one image as a chromosome foreground image; fusing the chromosome foreground image with a randomly selected background image, and carrying out edge smoothing processing on the fused image to generate a random background chromosome middle-term image; wherein the random background chromosome metaphase image is used for training a target detection model established based on deep learning. According to the method, high-quality and diversified chromosome metaphase images can be generated, and when the method is used for training the deep learning model, the generalization ability and precision of the deep learning model in a target detection task are improved.

To improve interest in a performance.SOLUTION: This game machine is provided with a performance control board 300 capable of executing a picture scroll mode performance for changing the display contents of a background image displayed on the back surface side from performance symbols for each variable display of the performance symbols (special symbol 1 one digit symbol a2, special symbol 2 one digit symbol a3 and picture scroll mode exclusive mini symbol). Then, with regard to the display contents of the background image which is displayed during the picture scroll mode performance, the same display contents do not overlap. That is, in the Pachinko machine 1, when executing the picture scroll mode performance in which the display content of the background image displayed on the back side of the performance symbol is changed for each variable display of the performance symbol, since the same display content is not overlapped for the display content of the background image displayed during the picture scroll mode performance, the interest of the performance can be improved.SELECTED DRAWING: Figure 51

The invention relates to the technical field of infrared detection, and discloses an infrared detector dead pixel detection method, which comprises the following steps: acquiring a current scene image during the working period of an infrared detector and a plurality of frames of isothermal uniform surface background images acquired by the infrared detector at different working temperatures to form a background image sequence; performing singular point detection on each frame of image in the current scene image and the background image sequence by adopting an improved wavelet transform algorithm, screening out effective singular points, and calculating a singularity value of each effective singular point; based on the singular point detection result, the singularity value difference and historical background image data, static dead pixels, dynamic dead pixels and noisy points are distinguished through a multi-layer judgment rule, and a dead pixel detection result is obtained; and periodically evaluating the accuracy of the defective pixel detection result, and dynamically calibrating the detection parameters according to the evaluation result. Through collaborative analysis of the current working image and the multi-temperature background image sequence, dead pixels can be accurately detected in real time in the normal working process of the detector.

The invention relates to the technical field of image processing, and discloses a method and system for switching backgrounds in batches to form a new image based on a convolutional neural network, and the method comprises the steps: carrying out the analysis of a cartoon image, obtaining a foreground segmentation mask and a foreground material feature tensor, carrying out the analysis of a target background image, and obtaining a background illumination feature vector; dynamic light and shadow interaction between a cartoon foreground and a new background is simulated by constructing a double-medium light interaction field, fine fusion of an edge region is realized in combination with an edge transmission matrix, the problem of visual splitting caused by light and shadow interaction deficiency in background switching of a traditional convolutional neural network is solved, and meanwhile, through a light and shadow residual image correction mechanism, the dynamic light and shadow interaction between the cartoon foreground and the new background is corrected. The illumination consistency is further optimized, the fusion matching degree of the cartoon image and various real or virtual scenes is remarkably improved, the diversified marketing scenes can be efficiently adapted through a batch processing mode, and the batch replacement requirement is met while the visual naturalness is guaranteed.

The invention discloses a defect detection sample data generation method and related equipment, and relates to the technical field of defect detection sample data generation, and the defect detection sample data generation method comprises the steps: obtaining a good product background image and an expected defect direction; based on the expected defect direction, respectively generating a structure guide mask and a semantic guide text cue word according to the defect knowledge base; inputting the non-defective product background image, the structure guiding mask and the semantic guiding text cue word into a local redrawing model, synthesizing a local defect image, and determining defect labeling information of the local defect image according to the structure guiding mask; and integrating the local defect image and the defect labeling information, and outputting defect detection sample data. According to the method, the structure guide mask and the semantic guide text cue word are generated through the defect knowledge base and are input into the local redrawing model to synthesize the local defect image, so that the defect image is effectively generated, and finally, the beneficial effect of improving the accuracy when the defect sample image is used for defect detection is achieved.

The invention relates to the technical field of image data processing, in particular to a flange forge piece surface defect detection method and system based on image processing, and the method comprises the steps: obtaining an original image of a flange forge piece, and carrying out the differential Gaussian band-pass filtering to obtain a preprocessed image; performing multi-scale structure tensor analysis on the preprocessed image, and determining a texture abnormal value according to a gradient magnitude and an included angle between a gradient direction and a local texture direction; extracting a background image through morphological reconstruction, and calculating a geometric suppression weight based on brightness difference; and fusing the texture abnormal value and the geometric suppression weight to obtain a defect response value, and identifying the surface defect through threshold segmentation. According to the method, through combination of multi-scale texture analysis and a geometric suppression mechanism, weak defects can be accurately extracted under a strong texture background, artifacts generated by a flange geometric structure are effectively suppressed, and the accuracy and robustness of defect detection are improved.

Systems, methods, and software are disclosed herein for designing and generating custom complex documents in various implementations. In an implementation, program instructions direct a computing apparatus to at least receive, in a user interface, a user request for a document. The program instructions further direct the computing apparatus to generate a design specification for the document based on the user input and retrieve a seed image based on the design specification. The program instructions further direct the computing apparatus to generate a text layer for the document based on the user request and to elicit, from an image generation model, a custom background image for the document based on the seed image and the design specification. The program instructions further direct the computing apparatus to generate the document based on the template and the custom background image.

A computer-implemented is disclosed. The method includes: obtaining a composite image depicting an identifiable foreground object; performing image segmentation to isolate a foreground object from the composite image, the image segmentation yielding a segmented composite image; determining object attributes of the foreground object based on image analysis of the segmented composite image; obtaining at least one prompt for a text-to-image model based on the object attributes of the foreground object, the at least one prompt defining an intended replacement background for the composite image; providing, to the text-to-image model, instructions to generate a replacement background image for the composite image based on the at least one prompt; receiving, from the text-to-image model, a replacement background image for the composite image; and generating a new composite image, the generating including compositing portions of the composite image corresponding to the foreground object with the replacement background image.

The invention relates to the technical field of image processing and extremely weak light imaging, and particularly provides a photon counting image reconstruction method and system, and the method comprises the steps: determining a target threshold value based on the adjustment of a dynamic adjustment coefficient under different illumination or gain conditions based on an initial threshold value; counting a first photon number of a single photon signal in the background image and a second photon number of the single photon signal in the signal image based on a target threshold, and generating noise photon number statistical distribution and signal photon number statistical distribution to determine a dynamic photon number processing interval; and performing time-space joint accumulation on the pixels conforming to the dynamic photon number processing interval, generating a two-dimensional photon accumulation matrix, and converting the two-dimensional photon accumulation matrix into a gray level image to form a single group of reconstructed gray level images. According to the method, real signals can be effectively extracted, the influence of dark noise, shot noise and readout noise is suppressed, zero noise detection is realized, and finally, the dynamic range, the signal-to-noise ratio and the stability of the image are remarkably improved through single-group image reconstruction and multi-group integration processing.

The invention relates to a subway door anti-pinch method and device based on video analysis and a medium, and the method comprises the steps: S1, collecting a continuous video stream in real time when a subway door is dynamically closed, and taking a current frame image as a to-be-detected image; s2, the to-be-detected image is processed through the Gaussian mixture background model, whether a target exists or not is judged, if not, the step S1 is executed, if yes, the target is extracted, the step S3 is executed, and the background image is the image when the subway door is in the open state; s3, calculating a pixel motion vector field between the to-be-detected image and the previous frame of image; and S4, on the basis of the pixel motion vector field, detecting a normal optical flow generated by the motion of the subway door and an abnormal optical flow of the target, if the abnormal optical flow is detected, judging a target door rushing behavior and triggering an anti-pinch mechanism by combining a background difference method, and if the abnormal optical flow is not detected, returning to the step S1. Compared with the prior art, the method has the advantages of safety, reliability and the like.

A method includes obtaining a reference frame from among multiple image frames of a scene. The method also includes generating a segmentation mask using the reference frame, where the segmentation mask contains information for separation of foreground and background in the scene. The method further includes applying the segmentation mask to each of the multiple image frames to generate foreground image frames and background image frames. The method also includes performing multi-frame registration on each of the foreground image frames to generate registered foreground image frames. The method further includes performing multi-frame registration on each of the background image frames to generate registered background image frames. In addition, the method includes combining the registered foreground image frames and the registered background image frames to generate a combined registered multi-frame image of the scene.

According to an aspect, a display system includes: a display panel having a display area provided with pixels and configured to allow a user to visually recognize a background transmitted through the display area; a light source configured to emit light toward a side surface of the display panel; a signal processing circuit configured to generate input gradation values and output either first gradation values that are the input gradation values or second gradation values obtained by multiplying each of the input gradation values by an adjustment coefficient of 1 or smaller corresponding to attenuation of light propagating in the display panel; an illuminance sensor; and a camera. The signal processing circuit is configured to output the first gradation values when average luminance of the background image is a first predetermined value or larger and illuminance acquired by the illuminance sensor is a second predetermined value or larger.