183 results about "Cropping" patented technology

The invention relates to the technical field of textile printing and dyeing, in particular to a setting machine cropping moisture content control method and system based on MPC. An MPC model is constructed, and the MPC model comprises the steps that actually-measured and preset water content and the first control parameter state vector serve as input, and predicted water content is obtained; minimizing the difference value between the predicted water content and the preset water content as a target; solving and optimizing the control increment by using the optimization matrix and sequential quadratic programming; and performing action regulation and control on an execution mechanism by utilizing the optimal control increment. The invention solves the problems of insufficient detection precision, hysteresis and poor interference resistance in the existing single temperature feedback control of the moisture content of the fabric.

The invention relates to the technical field of intelligent agriculture, in particular to a planting management method and system for intelligent agriculture, and the method comprises the steps: S1, obtaining multi-modal data through configuring an independent sensing module and a local control execution end for each micro-block; s2, recognizing a current crop variety in real time according to the multi-modal data by adopting a low-power-consumption edge computing device in combination with image recognition; when the system is used, each micro-block operates independently, maintenance of each plant can be refined, multi-source sensing data can be fused, the problems of data lag and weight deviation among different sensors are solved, full-automatic feedback regulation and control are achieved, the labor burden of workers is reduced, low-power-consumption and high-real-time recognition is facilitated, multi-source data such as images and fusion of temperature and humidity are achieved, and the system is suitable for popularization and application. According to the method, the identification accuracy is improved, the problem of crop confusion which is like a long variety can be solved conveniently, accurate management in multi-variety mixed cropping planting can be achieved conveniently, and the accuracy of fine management and control of different demands of multiple different crops in a small range can be improved.

The invention relates to a projector automatic obstacle avoidance method and device combining adaptive picture cutting and deep learning, and the method comprises the steps: enabling a projector to project a white background picture, capturing a projection picture through a camera, calculating a difference image based on a projection region background image and a projection picture image, carrying out the adaptive cutting of the difference image, and obtaining a projection region image, the difference image can highlight the difference between the projection area and the background area, the projection area image can be found out more conveniently, then a deep learning model is used for detecting an obstacle in the cut image and recording position information, the maximum projection area is calculated, the position information of an obstacle-free area is obtained and then converted to a projector coordinate system, and the position information of the obstacle-free area is obtained. And finally projecting a final picture. Therefore, the automatic obstacle avoidance of the projector is realized, the clear and complete projection picture is ensured, the problem of poor projection effect in a complex environment due to traditional manual processing and the prior art can be effectively solved, and the projection quality of the projector in a complex scene is improved.

The invention discloses a transformer substation bird nest hidden danger identification method and system based on multi-model collaboration, and belongs to the technical field of computers, and the method comprises the steps: carrying out the image collection in a transformer substation scene through employing unmanned plane equipment, and obtaining a transformer substation bird nest hidden danger image set; inputting the substation bird nest hidden danger image set into a target area identification model for potential target identification and image clipping, and outputting a to-be-detected image; based on a preset recognition task, performing visual feature enhancement on the to-be-detected image, and generating a prompt word text; inputting the to-be-detected image and the cue word text into an image analysis model for bird nest recognition to obtain a recognition result; and outputting a bird nest position and an alarm signal in the to-be-detected image according to the identification result. Therefore, by implementing the method and the device, the problem that non-nest objects with similar colors and forms cannot be effectively distinguished in a complex environment in the prior art, so that the false recognition rate of hidden danger recognition of the transformer substation is high can be solved.

The invention provides an automatic image cutting method and system based on face key points, which are applied to the technical field of image processing, and the method comprises the steps: inputting a portrait image into a portrait segmentation model for foreground extraction, and obtaining a figure region mask; performing top positioning estimation based on the figure area mask to obtain a top position of the portrait image; inputting the portrait image into a face key point detection model to obtain a plurality of key points; determining a face midpoint of the portrait image based on the position relationship of the plurality of key points; performing affine transformation estimation based on the source point set and a preset target point set to obtain an affine transformation matrix; adjusting the vertical offset of the affine transformation matrix to obtain a longitudinal correction matrix; performing edge correction on the longitudinal correction matrix to obtain a cutting transformation matrix; and performing image transformation on the portrait image based on the cutting transformation matrix to obtain a standard composition image. According to the invention, the cut image with uniform size, standard composition and good detail retention can be generated.

The invention belongs to the field of image processing, particularly relates to an automatic association adjustment method for ground identification lines based on block division, and aims to solve the problems of long updating period, potential errors and omission, high resource consumption and low efficiency. The method comprises the following steps: analyzing airport AIP vector data to generate a structured data set; and constructing a topological sub-region in each functional region according to the connection relation of the marked line end points, subdividing the grid units when the marked line density exceeds a threshold value, and outputting a layered region map. And taking the area / grid unit as a detection unit, detecting new and old AIP change areas and position identifiers, and generating new version change area data. Recursively searching an N-degree correlation line by taking the change area as a starting point, and only updating the correlation line of geometric, type or topological connection change. Finally, the changed data and the unchanged data are spliced, and a complete airport model is generated through boundary adaptive extension cutting and global topology reconstruction. According to the method, efficient and accurate updating of the airport ground identifier is realized.

A method for monitoring the grassland vegetation coverage based on continuous photography, comprising: determining an observation quadrat and continuously taking multiple vegetation photos of the vegetation within the observation quadrat throughout the entire growing season; cropping and geometrically correcting the multiple vegetation photos; obtaining the vegetation pixel ratio corresponding to the vegetation photos and comparing the vegetation pixel ratio with a preset second threshold; when the vegetation pixel ratio is greater than the second threshold, calculating the vegetation coverage corresponding to the vegetation photos by using the Otsu algorithm; otherwise, using the vegetation pixel ratio as the vegetation coverage; obtaining a dynamic change curve of the vegetation coverage based on the vegetation coverage corresponding to each vegetation photo. The present invention effectively improves the monitoring accuracy and efficiency of the dynamic change characteristics of the grassland vegetation coverage by combining continuous photography and the Otsu algorithm.

The invention discloses a cross-modal anti-attack method and system based on mask weight and random cutting, and belongs to the technical field of deep learning. The method comprises the following steps: extracting a saliency mask of a source image; disturbing the source image by using the disturbance variable to generate an adversarial sample; randomly sampling K cutting frames in combination with a significance mask; respectively extracting clipping characteristics of the clipping frame on the adversarial sample and target embedding selected on the target image according to an alignment mode; calculating the cutting loss of the cutting frame according to the cutting feature and the target embedding; calculating the weighted sum of the cutting loss according to the significance mask to obtain the total loss; performing back propagation according to the total loss so as to update the disturbance variable; and re-perturbing the source image by using the perturbation variable based on the updated perturbation variable until the maximum step number or total loss convergence is achieved. The method gives consideration to attack success rate, mobility and disturbance concealment, and can adapt to quality evaluation of different large language models.

The invention relates to the technical field of industrial detection, and provides a steel ladle air brick defect detection method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of a target steel ladle air brick image shot by an industrial camera, including edge feature extraction, cutting and enhancement, and obtaining a preprocessed image set; for the block corresponding to each preprocessed image, acquiring a multi-angle image set with different resolutions shot by a multi-angle sensor; inputting the multi-angle images into a defect feature extraction network to generate a feature image set, and integrating to obtain an integrated feature image; generating defect positioning and type information based on the integrated feature map and the defect identification model set; and finally, combining the initial image, the model set and the defect information to generate a defect detection result. According to the method, the defect detection precision is improved through multi-angle image fusion and model collaborative verification. According to the invention, the accuracy and reliability of steel ladle air brick defect detection can be improved, and the accuracy of defect positioning and classification is enhanced.

A system for recognizing vertically oriented alphanumeric text in images, the system including a processor configured to receive one or more images comprising vertically oriented alphanumeric text and detect one or more regions-of-interest in each image via a trained text detector. The processor is configured to execute a cropping of the detected one or more regions-of-interest encompassing vertically oriented alphanumeric text from each image to obtain one or more text crop portions and rotate the one or more text crop portions to obtain one or more orthogonally rotated text crop portions. The processor is configured to execute a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions and generate a set of candidate recognized text strings based on the executed trained ensemble and determine a final recognized text string.

The present invention discloses a method for extracting two-dimensional pore structures of engineered wood materials based on an autoencoder. The method includes the following steps: obtaining SEM images of engineered wood materials; performing cropping and image enhancement processing on the SEM images; designing an autoencoder network model including an encoder and a decoder, and inputting the processed SEM images into the autoencoder network model for training; using the trained autoencoder network model to perform binary extraction on the processed images, and the two-dimensional pore structures of the engineered wood materials can be obtained. The present invention uses an unsupervised autoencoder network model to perform binary extraction of the pore structures of the SEM images of engineered wood materials, which has the characteristics of high robustness and high precision, and can avoid noise and pseudo-pores of traditional threshold methods at the same time.

The invention provides a display device and a picture cutting method. After the display device enters the cutting mode in response to the cutting instruction, when the size of the first original image is larger than the size of the display area, the first original image can be zoomed according to the first zooming proportion to obtain the second image, and the first preview image corresponding to the cutting area is previewed in real time in the cutting process with the second image as the preview basis. When the user confirms cutting, the first preview image can be mapped to the first sub-original image in the first original image based on the first scaling, and the content consistency of the first sub-original image and the first preview image is ensured. And cutting out a first sub-original image from the first original image, and displaying the first sub-original image in the display area after zooming processing according to the first zooming proportion so as to ensure that the display effect of the zoomed first sub-original image is consistent with the display effect of the first preview image.

The application discloses a target detection domain adaptation method based on background and foreground cropping and interchanging. The application adopts a teacher-student model framework of semi-supervised learning, slowly updates through an exponential moving average of a teacher model, enables the model to obtain information from main knowledge learned by a student model, and thus realizes a self-supervision effect. A domain discriminator is introduced to perform adversarial training, enhances the ability of the model to extract domain-invariant representation, and thus improves robustness. In addition, a foreground-background mixed instance strategy further enhances the domain adaptation capability of the model, effectively creates target domain instance images with source domain backgrounds and source domain instance images with target domain backgrounds through cropping, padding and pasting operations. Finally, an iterative optimization strategy is adopted to continuously improve the model performance, a student model is updated through gradient back propagation by calculating multiple loss functions, and a teacher model is updated using an exponential moving average.

The invention provides a leaf image mode classification method and system for double cropping rice spike fertilization, and the method comprises the steps: collecting a first leaf image parameter array and a second leaf image parameter array at a preset time window of target double cropping rice and at a plurality of positions of a first leaf and a second leaf; carrying out image parameter discrete analysis on the parameter arrays to obtain a first dispersion and a second dispersion, and calculating to obtain a leaf pollution degree by combining a preset dispersion; and carrying out averaging processing and leaf pattern classification on the parameter array, and respectively obtaining two leaf pattern arrays based on the image parameters and the pollution degree, thereby determining a proper leaf pattern and carrying out double cropping rice panicle fertilizer application. The technical problem that in the prior art, due to the fact that the test data amount is insufficient, the fertilization recommendation accuracy on different field parcels is not high is solved, and the technical effects that through analysis of the double cropping rice leaf image parameters and the mode classification of the pollution degree, accurate regulation and control of the panicle fertilizer are achieved, and the fertilization recommendation accuracy is improved are achieved.

The invention provides an electronic image stabilization control method, a program product, electronic equipment and a storage medium, and the method comprises the steps: converting a current frame image based on a homography matrix corresponding to the current frame image, and obtaining a first image region; determining whether a preset cutting area in the current frame image is completely located in the first image area or not; in response to the fact that the preset cutting area is not completely located in the first image area, adjusting the homography matrix; and carrying out conversion again based on the adjusted homography matrix until the preset cutting area is completely located in the first image area, and carrying out cutting based on the preset cutting area to obtain a stable image corresponding to the current frame image.

The present invention provides a method and apparatus for detecting vehicle damage, the method comprising the steps of: dividing the overall appearance of the target vehicle into N predefined blocks; performing image acquisition for each of the N blocks according to a predefined image acquisition model to obtain N original images corresponding to the N blocks; performing vehicle part detection for each of the N original images to obtain a vehicle part position detection result; cropping each of the N original images into M sub-images of a predefined size according to a predefined cropping model; performing damage detection for each of the N original images and its corresponding M sub-images to obtain a damage detection result; and fusing the vehicle part position detection result with the damage detection result to obtain a vehicle part damage result for the target vehicle.A standardized image acquisition and image preprocessing process reduces the number of images taken, thus increasing the speed and efficiency of the entire damage detection process.

The application discloses a kind of provincial range rural black and odorous water body satellite remote sensing identification method and system, it is related to environmental remote sensing technical field;A kind of provincial range rural black and odorous water body satellite remote sensing identification method, comprising the following steps: obtaining data, data processing, data image inlaying, obtaining water body distribution data, data cropping, obtaining index data product, obtaining classification grid, preliminary obtaining black and odorous water body distribution, obtaining the final distribution of black and odorous water body;The present application utilizes multi-source satellite remote sensing image data, in combination with water quality parameters, constructs the remote sensing identification model of provincial range rural area water body using NIR, NDWI, SWI index and slope data etc., then uses BOI, NDBWI etc. Index constructs rural area suspected black and odorous water body extraction model, in combination with the relevant data of water body field measurement in monitoring area determines the extraction threshold of water body and black and odorous water body, realizes the automatic identification and extraction of rural area black and odorous water body, and develops relevant software system.

The invention discloses a deep learning rice planting area extraction method fusing remote sensing image time-space spectrum information. The method comprises the following steps: step 1, constructing a time sequence of Sentinel-2 image spectral features and texture features; step 2, establishing an image set fusing spectrum-time-space features; step 3, constructing a CTH-Net hybrid deep learning model; the CTH-Net hybrid deep learning model comprises a CNN branch, a Transform branch, a TSSF module, a residual module, a Dropout regularization layer and a full connection layer; by optimizing a spectrum-time-space multi-dimensional data fusion method, efficient integration of remote sensing image spectrum, time and space features is realized. A space-time spectrum fusion (TSSF) module and a residual module are introduced into the CTH-Net, so that the capability of the model for extracting rice in a complex landscape is remarkably improved. The results show that the CTH-Net reaches 99.69% in the aspects of accurry, precision, recall and F1score extracted from the rice, and the CTH-Net is stable in different categories such as single cropping rice, double cropping rice, abandoned land and the like.

The present invention relates to a method and device for automatic obstacle avoidance for a projector that combines adaptive cropping of images with deep learning. First, the projector projects a white background image, then uses a camera to capture the projected image. A differential image is calculated based on the background image of the projection area and the image of the projection screen. The differential image is adaptively cropped to obtain the projection area image. The differential image can highlight the difference between the projection area and the background area, making it easier to find the projection area image. A deep learning model is then used to detect obstacles in the cropped image and record their position information. The maximum projected area is calculated, and the position information of the obstacle-free area is obtained. This information is then converted to the projector coordinate system, and the final image is projected. This method achieves automatic obstacle avoidance for the projector, ensuring a clear and complete projection image. It can effectively solve the problem of poor projection effects in complex environments caused by traditional manual processing and existing technologies, and improve the projection quality of the projector in complex scenes.

This invention provides an information processing method that can generate an image from an image containing multiple people, with the main person in the composition being the central figure, according to the type of event depicted in the image. [Solution] An information processing method according to one embodiment of the present disclosure acquires an overall image in which multiple people are pictured, identifies the event type of the overall image, identifies a target person from the multiple people according to the identified event type, determines the area containing the target person as a cropping area, and generates individual images by cutting out the cropping area.

The application provides an image dynamic cropping and frame buffer method and device, and relates to the technical field of image processing. The method comprises the following steps: in response to an image display task, acquiring a to-be-processed image frame sequence, cropping requirement information and display adaptation information; determining a buffer write control rule and a cropping read control rule according to the cropping requirement information and the display adaptation information; alternately writing the image frame into a first buffer area and a second buffer area based on the buffer write control rule; when the image frame is written in one buffer area, reading buffer frame data corresponding to a target cropping area from another buffer area in which the image frame has been written to obtain cropping frame data; and performing output adaptation processing on the cropping frame data according to the display adaptation information to obtain display image data. Through the method provided by the application, the cropping area adjustment flexibility can be improved, the image buffer and cropping processing efficiency can be improved, and the adaptability between the cropped image and the display output can be improved.

Embodiments of the present disclosure relate to utilizing geometric image cropping for improved image processing. Such geometric image cropping improves efficiency and / or throughput of various image processing tasks, for example for reading a machine-readable symbology via a specially-configured scanner. Some embodiments generate cropping parameter(s) using raytracing projections from lens data and ranging data for use in cropping image(s). Some embodiments generate cropping parameter(s) using magnification estimation for use in cropping image(s). Generated cropping parameter(s) may be stored via a reader, for example to a range-parameter table, to efficiently be retrieved and utilized for cropping subsequently captured images while remaining accurate and efficient for image processing.

An image cropping method includes: displaying a to-be-cropped image on a first screen, and receiving a first input performed on a second screen in a case that a crop box is displayed on the to-be-cropped image; in response to the first input and in a case that the relative position of the to-be-cropped image and the crop box is kept unchanged, adjusting the size of the to-be-cropped image and the size of the crop box according to the same contraction or magnification ratio; receiving a second input performed on a target border line of the crop box displayed on the first screen; and in response to the second input and in a case that the display position of the to-be-cropped image is kept unchanged, adjusting the position of the target border line and cropping the to-be-cropped image.

The invention discloses a reconfigurable crop image processing method and system based on software and hardware cooperation, and belongs to the technical field of embedded systems and image processing. The problems that in the prior art, an FPGA neural network accelerator model is solidified, the hardware resource utilization rate is low, and the detection precision and the reasoning speed are difficult to consider at the same time are solved. According to the method, the complexity score of an input image is calculated through an edge detection operator, a color histogram and a gray level co-occurrence matrix, a rapid detection mode, a fine recognition mode or a cooperative reasoning mode is selected according to the complexity score and delay constraint, and dynamic partition configuration is carried out on a processing unit array in the reconfigurable accelerator; in a cooperative reasoning mode, executing the lightweight target detection model through the first processing unit group to quickly detect and output a candidate box, extracting a region-of-interest feature map through the ROI cutting unit, routing the region-of-interest feature map to the second processing unit group, executing the high-precision target detection model to perform fine recognition, and fusing double-model output to generate a final detection result; the two models realize hardware resource sharing through a three-level weight storage architecture. The precision and speed balance capability of agricultural image detection are effectively improved, the resource utilization rate is improved, and the method can be applied to intelligent agricultural scenes such as crop disease recognition and fruit grading.

This invention relates to the field of image cropping, specifically disclosing a method, system, terminal, and medium for segmenting an image to generate a jigsaw puzzle cropping path. The method involves initial segmentation of elements based on element parameters, dividing each element into rectangles; these parameters include the number of rows, columns, and image size. The coordinates of the four corners of each element are obtained from the initial segmentation results and stored in a coordinate file. The concavity / convexity state of each edge of each element is configured. A cropping path is generated based on the concavity / convexity state of each edge of each element and the coordinates of its four corners. This invention enables individual editing of any jigsaw puzzle element, thereby generating a cropping path that can be directly applied to the generated image, improving generation efficiency and reducing costs.

The present application relates to a video cropping method and apparatus for a dashcam, a device, and a medium. The video cropping method for a dashcam comprises: acquiring multi-channel video frames collected by a multi-channel dashcam, and acquiring cropping parameters corresponding to each video frame, wherein the cropping parameters comprise a cropping range; performing image cropping on each video frame according to the cropping range, so as to obtain cropped multi‑channel video frames; and performing encoding on the basis of the cropped multi‑channel video frames, and generating and displaying a target video. According to the technical solution of the present application, the display range of a dashcam video can be flexibly controlled.

An augmented reality (XR) system provides a method for displaying a virtual object in a hand-centric XR experience. The XR system provides a user with an XR user interface of the XR system. The XR system captures video frame data of a user's hand and detects the user's hand based on the video frame data and a hand detection model. The XR system generates a cropping bounding box based on the detection of the hand and the video frame data, and generates cropped video frame data based on the cropping bounding box and the video frame data. The XR system generates a 3D model of a portion of the user's hand based on the cropped video frame data, and generates a virtual object based on the 3D model and the 3D texture of the portion of the user's hand. The XR displays the virtual object in an XR user interface.

The technology provides for enhanced image cropping via in-context learning. It includes an efficient prompt retrieval mechanism for image cropping to automate the selection of in-context examples. It also includes an iterative refinement strategy to iteratively enhance the predicted crops. The image cropping framework is applicable to a wide range of cropping tasks, including free-form cropping, subject-aware cropping, and aspect ratio-aware cropping. The approach employs a trained large vision-language model associated with in-context learning. For instance, given an input image (whether from free-form, subject-aware or aspect ratio-aware cropping), the top-K semantically similar images from a dataset are retrieved as an in-context learning prompt. Then the in-context learning prompt is fed to a pretrained vision-language model to generate a set of crops. The crop candidates of the set are iteratively refined to yield a final output crop. The final output crop can then be applied to a downstream imaging task.

The application provides a video image fast clipping and dimension reduction method and system based on a hierarchical bounding box, applied to the technical field of image processing, wherein the method collects multi-view dynamic scene point cloud data, and then divides the space by using a hierarchical bounding box to establish a bounding box structure with density characteristics; subsequently, optical diffraction array pre-filtering is used, and point clouds are separated based on wavelength selectivity and density distribution to generate a first subset and a background subset; then, adaptive pulse code modulation is used on nodes in the first subset to obtain high-precision data, and nodes in the second subset are compressed into low-code-rate data by using run-length encoding; subsequently, the two types of data are input into an improved k-d tree based on the spatial relationship of the bounding box to generate a two-dimensional point set representing key features, and a spatial index is constructed to realize real-time scene retrieval. The application can realize image dimension reduction while retaining key geometric features of the image, thereby improving efficient real-time spatial retrieval in a dynamic scene.

The application provides a lane line recognition processing method and device and a vehicle terminal, and relates to vehicle driving technology. The method comprises the following steps: collecting an original image in front of a road; wherein the original image comprises a plurality of lane lines to be recognized; performing scaling processing on the original image to obtain a first image after scaling, inputting the first image into a preset resize model for lane line detection to obtain a first detection image; performing cropping processing on the original image to obtain a second image after cropping, inputting the second image into a preset crop model for lane line detection to obtain a second detection image; and performing fusion recognition processing on the first detection image and the second detection image to obtain a target detection result about the lane lines to be recognized. The method greatly improves the detection accuracy of remote lane lines and solves the technical problem of low detection accuracy of remote lane lines.