67 results about "Cropping" patented technology

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 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.

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.

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 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.

The invention provides a redried tobacco strip grading method and system based on computer vision, and belongs to the technical field of tobacco processing and quality control. The method comprises the following steps: firstly, detecting working conditions, including detecting the position of a pressure head, the position of a smoke box, obstacles, the working state of a press and the like; after the working conditions are met, the camera obscura is started to move to the position vertically above the smoke box; starting a light source and a camera, and collecting a clear tobacco strip image; preprocessing operations such as denoising, enhancement, cutting and color correction are carried out on the collected image; abnormal conditions including box covering, exposure, local shielding, leaf breaking and the like are detected; inputting a grading algorithm to the normal condition image, and outputting tobacco lamina grade and matching degree information; and visualizing a tobacco strip analysis result, aligning the result with the image data, and storing the result in a database. According to the method and the system, automation, high efficiency and accuracy of tobacco strip data acquisition and grading are realized, abnormal conditions can be effectively identified and processed, and the method and the system are suitable for online quality control of a tobacco production line.

The invention relates to a coding and decoding device, a coding and decoding method, a bit stream transmission device and a storage medium. This encoding device is provided with: a circuit that, during operation, designs each of a plurality of sub-pictures included in a picture, encodes configuration information indicating the configuration of each of the plurality of sub-pictures included in the picture, and stores the encoded configuration information; the circuit encodes each of the plurality of sub-pictures, the circuit, in action, designs each of the plurality of sub-pictures such that at least one pixel contained in each sub-picture is contained in the same consistent cropping window in the picture, conditions for designing the plurality of sub-pictures include that a right end of each of the plurality of sub-pictures is located rightward of a left end of the consistent clipping window.

The present application relates to the technical field of target detection and template matching, in particular to a workpiece recognition and counting method based on target detection and template matching in a complex scene, comprising the following steps: obtaining a standard workpiece original data set by using a high-definition industrial camera; obtaining an enhanced data set by pre-processing, data augmentation, cropping and classification labeling of the workpiece image set; constructing a target detection network model based on an improved YOLOv5 and performing data set training; deploying the trained target detection model to read the industrial camera in real time to detect the workpieces on the production line, crop and save the recognized workpiece images after rotating the angle. The present application has the advantages that: firstly, the workpieces are recognized from the complex scene image by the deep learning target detection method, and the position information of the workpieces is obtained, which has better robustness for the sensitivity of environmental information. It can handle multi-target and multi-angle recognition tasks, and can excellently complete workpiece recognition and counting in a relatively complex production scene.

A first object detection section (30) detects an object from a captured image and outputs object position information in a non-fast processing mode. A switching section (20) switches to a fast processing mode in a case where the object position information is output from the first object detection section (30). An image cropping section (40) extracts a cropped image from the captured image based on the object position information output from the first object detection section (30) in the fast processing mode. A second object detection section (60) detects an object from the cropped image and outputs object position information. An extension determination section (70) determines whether the object detected by the second object detection section (60) extends from the cropped image. The switching section (20) switches to the non-fast processing mode in a case where it is determined that the object detected by the second object detection section (60) extends from the cropped image.

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.

This application provides a mobile robot task scheduling method and system based on a heterogeneous computing architecture, relating to the field of intelligent scheduling technology. The method includes: constructing a workspace geometric model based on the geometric structure of a planting row; obtaining the motion state parameters of the mobile robot using a first computing unit; determining the look-ahead compensation amount by combining the statistical value of the inference time of the target recognition task by a second computing unit; determining a spatiotemporal pre-aiming slice in the visual field region based on the look-ahead compensation amount and the workspace geometric model, and cropping image data located within the slice from the original image data; the second computing unit performing target recognition on the image data, generating a work instruction corresponding to the recognized target, and controlling the lateral actuator to perform the work according to the work instruction. This can alleviate the spatiotemporal asynchrony problem between perception results and execution position in a moving work scenario, reduce missed and misaligned tasks, and reduce the communication and computational load of the heterogeneous computing system.

This application provides a method, system, device, and storage medium for identifying smoking behavior in industrial parks. The method includes: acquiring video stream data from park surveillance; decoding the video stream data to obtain image frames; performing human target detection on the image frames to determine the location of each human body within the image frames; cropping sub-images containing the human bodies from the image frames based on their locations; performing smoking behavior recognition on the sub-images to determine whether the human body in the sub-image is smoking; and outputting the smoking behavior recognition results for each human body in the image frames. This method, through a two-stage processing architecture of first human detection and then region cropping, transforms the problem of identifying small-target smoking behavior in a large-scale park surveillance scenario into a refined behavior discrimination problem at a normalized scale. This effectively alleviates the contradiction between sparse features of small targets and background interference, improving the balance between recognition accuracy and computational efficiency.

This invention relates to the field of document image processing, proposing a method and system for batch cropping of stitched images based on segmentation line detection and quality verification. The method acquires an image formed by stitching multiple pages of content row by row and column, initializes the ultra-high resolution image, performs grayscale conversion and noise reduction, performs edge detection, and enhances linear structures using directional morphological operations; based on the enhanced results, candidate line segments are obtained through line detection, and segmentation line coordinates are filtered by directional constraints and boundary exclusion, and similar segmentation lines are merged, deduplicated, and sorted; cropping regions are constructed based on ordered segmentation lines to generate candidate sub-images, and invalid slices are filtered through minimum size / area thresholds and geometric consistency verification, outputting a verification list when necessary; the numbers of sub-images that pass verification are saved, and anomaly information is recorded. Through the above scheme, the cropping consistency and processing stability of automatic splitting of stitched document images are improved, erroneous cutting is reduced, and the traceability of results is enhanced.

The invention discloses a method for measuring the motion direction of flagellates under a flowing condition. The method comprises the following steps: step 1, acquiring and processing an image sequence; 2, cutting and aligning the cell image; step 3, preprocessing the cell image; step 4, creating a sector ring detection area; step 5, constructing a time sequence frame; step 6, calculating time sequence difference characteristics; and 7, calculating confidence and determining a motion direction. According to the method for measuring the motion direction of the flagellum under the flowing condition, the technical problem that the flagellum cannot be identified due to poor flagellum imaging quality and instantaneous information loss in a flowing water environment is solved, and high-precision, automatic and continuous measurement of the motion direction of the flagellum is realized through time sequence dynamic characteristic analysis and biological motion characteristic judgment.

The application discloses a cross-modal adversarial attack method and system based on mask weight and random cropping, and belongs to the technical field of deep learning. The method comprises the following steps: extracting a saliency mask of a source image; using a disturbance variable to disturb the source image to generate an adversarial sample; randomly sampling K cropping boxes in combination with the saliency mask; respectively extracting cropping features of the cropping boxes on the adversarial sample and target embeddings selected according to an alignment mode on a target image; calculating a cropping loss of the cropping box according to the cropping features and the target embeddings; calculating a weighted sum of the cropping loss according to the saliency mask to obtain a total loss; performing back propagation according to the total loss to update the disturbance variable; and re-executing the step of using the disturbance variable to disturb the source image based on the updated disturbance variable until a maximum step number is reached or the total loss converges. The application can adapt to quality evaluation of different large language models by taking into account attack success rate, transferability and disturbance concealment.

The invention discloses a wheat aphid hazard assessment method based on an improved YOLO instance segmentation model. The method comprises the following steps: S1, preprocessing an instance perception image; s2, constructing an instance segmentation model, performing structure improvement, introducing a plurality of EV Blocks as convolution modules, and embedding an FMCA module; carrying out model training; and S3, outputting a result, and calculating a plurality of quantitative indexes for evaluation. According to the invention, the structure of the YOLO instance segmentation network is improved, and global features are better captured to solve the problem of compatibility between a tiny target and a large-scale target; a slice processing process is improved, dynamic cutting and regular grid cutting are combined, a geometric topology repair and negative sample probability retention mechanism is introduced, and the training sample quality of a tiny target in a high-resolution image is improved; a multi-dimensional quantitative evaluation index system is constructed, and the density, the coverage degree and the aggregation degree are fused through a nonlinear function, so that the unified quantitative characterization of the multi-aspect harm degrees of the wheat aphids is realized.

A media application receives an input image including a subject. The media application segments the subject from the input image. The media application generates a subject mask based on segmenting the subject, the subject mask including subject pixels associated with the subject. The media application determines whether a portion of the subject is severed by one or more boundaries of the input image based on the subject mask. In response to the portion of the subject not being severed, the media application provides the input image and the subject mask as input to a restorer machine learning model. The media application utilizes the restorer machine learning model to generate an output image that expands one or more boundaries of the input image by adding restorative pixels to the input image.

The invention provides an image cutting method based on subjective intention and multi-mode recommendation, which comprises the following steps of: dividing the subjective intention of a user into two categories of'adjustment 'and'highlight', and formulating a corresponding classification rule; and an image cutting model capable of simultaneously predicting the cutting area and the validity score of the cutting area under various subjective intentions is constructed. By introducing a subjective intention classification and effectiveness evaluation mechanism, the user satisfaction of the cutting result is further improved.

The invention provides an air cutting method and device based on quadratic programming end control. The method provided by the invention comprises the steps that a bidirectional cognitive task planning architecture is constructed, and the bidirectional cognitive task planning architecture comprises a slow thinking layer and a fast thinking layer; extracting the aerial clipping skill of the unmanned aerial vehicle through the slow thinking layer and generating task path points; the task path points are converted into a Bezier curve flight path containing a convex polyhedron safety channel through the fast thinking layer, and the unmanned aerial vehicle is controlled to move to the position over a cutting target along the Bezier curve flight path; based on a cutting target image fed back by an airborne camera of the unmanned aerial vehicle in real time, a mechanical arm tail end speed instruction is issued through a force feedback hand controller, after the speed instruction is integrated to obtain a position instruction, control parameters are solved in combination with a quadratic programming model containing an unmanned aerial vehicle rotation matrix and angular velocity compensation, and the mechanical arm tail end speed is obtained. And the mechanical arm is driven to carry the machine-mounted scissor tool to be positioned to a cutting point, and the machine-mounted scissor tool is controlled to be opened and closed for aerial cutting.

The invention provides a synthetic video noise reduction sample set establishment method and system and an image processor, and the method comprises the steps: carrying out the noise addition and dynamic cutting processing based on a first data set, and obtaining a first sample set; wherein the first data set is a dynamic noise reduction data set; performing noise addition and static cutting processing based on the second data set to obtain a second sample set; wherein the second data set is a static noise reduction data set; and establishing a video noise reduction sample set based on the first sample set and the second sample set. According to the method and system for establishing the synthetic video noise reduction sample set and the image processor provided by the embodiment of the invention, the establishment efficiency of the video noise reduction sample set is improved.

The application provides a shooting device focus breathing effect inhibition method, system, device and storage medium, relates to the technical field of image processing, and the method comprises the following steps: constructing a calibration mapping table according to voice coil motor position information and field of view angle information of a shooting device; obtaining a field of view angle offset amount by performing piecewise linear interpolation on the calibration mapping table and a real-time focusing position; determining a compensation scaling intensity according to angular velocity information and a motion threshold parameter of the shooting device, multiplying the field of view angle offset amount to obtain a target field of view angle offset amount; calculating a digital cropping compensation scaling factor according to the target field of view angle offset amount, and performing time domain smoothing processing to obtain a target scaling factor; determining an effective maximum cropping amount of breathing compensation according to an electronic anti-shake cropping proportion of the shooting device; and finally calculating a cropping window based on the target scaling factor, and performing breathing compensation cropping based on the cropping window. The application automatically coordinates breathing compensation and the use of limited cropping space by electronic anti-shake.

The invention discloses a human face detection-based figure image cutting adaptation method. The method comprises the following steps of: performing human face detection on an image to obtain image human face information; when the human face is not detected, cutting a target image in the middle area of the original image; when a human face is detected, selecting one piece of human face information in a human face detection result as a human face information reference of the current image, and cutting and adapting the image based on the human face information reference; when the cut image has transverse and vertical lines penetrating through the image, removing the transverse and vertical lines, and adjusting the face information reference to carry out adaptation again; and cutting the adaptive result to obtain a target image conforming to the target size proportion. According to the method, different cutting adaptation methods are adopted according to the ratio of the face area to the image area, the image aspect ratio serves as the cutting adaptation target, different cutting methods are selected according to the face information and the difference between the face width and the target width, and good cutting adaptation results can be obtained for various image types.

A computer includes a processor and a memory, the memory stores instructions executable by the processor to generate a first cropped image by applying a first cropping operation to a captured image, in which the first cropping operation is selected based on a first component to be actuated. The instructions can additionally be to apply a live person image recognition algorithm to the first cropped image to determine whether a recognized person is represented in the first cropped image and to output a signal to initiate actuation of the first component in response to the live person image recognition algorithm responsive to determining that the recognized person is in the first cropped image and is permitted to actuate the first component.

The present disclosure relates to a method, apparatus, a device, a medium and a program product for cropping images. The method comprises displaying an image in a plurality of images to be cropped and a set of cropping ratios for the plurality of images. The method also comprises in response to receiving a selection for a cropping ratio in the set of cropping ratios, displaying the cropped image. The method further comprises in response to the cropped image being displayed, displaying a control for applying the cropping ratio to the plurality of images on the cropped image. The method also comprises in response to receiving an operation for the control, applying the cropping ratio to the plurality of images. According to this method, after one of a plurality of images is cropped, a control is displayed on the cropped image and the “one-click cropping” is fulfilled using the control.

Disclosed in some examples are methods, systems, computing devices, and machine readable mediums that provide for cropping systems that automatically crop digital images using one or more smart cropping techniques. Smart cropping techniques may include: cropping an image based upon emotion detection, cropping based upon facial recognition and matching, and cropping based upon landmark matching. In some examples, a single smart cropping technique may be utilized. In other examples, a combination of the smart cropping techniques may be utilized.

The invention is suitable for the technical field of terminals, and particularly relates to an image cutting method, electronic equipment and a computer readable storage medium. According to the method, after an original image including a target object is obtained, the electronic equipment can determine the limb attractiveness and / or the face attractiveness of the target object. Then, the electronic equipment can determine a target cutting mode capable of highlighting the posture aesthetic feeling and / or the facial aesthetic feeling of the target object according to the limb aesthetic degree and / or the facial aesthetic degree of the target object, and can cut the original image according to the target cutting mode to obtain a cut image; the cut image can keep the posture aesthetic feeling and / or the facial aesthetic feeling of the original image, the aesthetic feeling of the cut image can be improved, and the user experience is improved.

This invention discloses a leaf area measurement method based on photography. After constructing a standardized leaf image acquisition background, leaf photos are obtained. The obtained leaf photos are then image-converted and binarized. The binary image is then subjected to erosion and dilation to obtain connected components, followed by image cropping. The cropped image is then converted to grayscale, and the optimal threshold is determined using the Otsu's method before binarization. Object extraction is then performed, and finally, the actual leaf area is calculated pixel-by-pixel. This invention aims to achieve rapid, accurate, and low-cost plant leaf measurement, solving problems in existing measurement systems such as poor background applicability, high image correction complexity, poor system stability, and difficulty in avoiding the effects of photo distortion.