2982results about "Image data processing details" patented technology

A pattern which does not appear at a flat portion in normal binarization processing is set as a code pattern, and a code formed from this pattern is attached. At this time, code attachment with little degradation in image quality is implemented by selecting an unnoticeable pattern.

In a measurement processing apparatus for measuring a geometric feature of an object image: a measurement-reference-element setting unit automatically sets at least one first measurement reference element for use in measurement of the geometric feature of the object image, at at least one first position on the object image based on first image data representing the object image and position information indicating at least one second position of at least one second measurement reference element which is set on a measurement reference image corresponding to the object image; and a geometric-feature measurement unit measures the geometric feature of the object image based on the at least one first position of the at least one first measurement reference element.

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

A medical image processing device of an embodiment includes first and second image acquirers, a generator, and a calculator. The first image acquirer acquires a first fluoroscopic image of a patient. The second image acquirer acquires a second fluoroscopic image according to radiation of the patient at a time point different from that of acquisition of the first image using a detector. The generator generates a reconstructed image by reproducing the second image from the first image virtually arranged in a three-dimensional space on the basis of a position of the detector in the three-dimensional space. The calculator obtains a suitable position on the first image in the three-dimensional space on the basis of a degree of similarity between the second image and the reconstructed image. The generator generates the reconstructed image for use in the calculator and has a range larger than a range corresponding to the second image.

The invention discloses a multistage video watermark embedding and extracting method and system, and the method comprises the steps: carrying out the perception analysis of an original video through a space-time attention neural network, and dynamically dividing a time domain stable region and a space domain sensitive region; a three-level watermark system is constructed, the three-level watermark system comprises a basic-level watermark, a first-level enhanced watermark and a second-level enhanced watermark, the basic-level watermark adopts an anti-rotation binary matrix generated by a Fourier descriptor, the first-level enhanced watermark is a DCT frequency domain Hash sequence, and the second-level enhanced watermark is a semantic fingerprint generated based on a motion vector. According to the method, through multi-dimensional collaborative optimization of the anti-attack capability, the dynamic security mechanism and the verification efficiency, the core defects that erasing is easy due to insufficient reversibility in a traditional video watermarking technology and counterfeit affiliation chaos is caused by a static key are overcome, and reliable technical guarantee is provided for digital content copyright protection.

The invention discloses an image steganography method and device, electronic equipment and a storage medium, a Res-SS2D module is designed, and through four-way global space modeling, the linear complexity is ensured, and meanwhile, the quality of a secret-containing image is improved; a high-frequency feature enhancement strategy is put forward, and secret information is guided to be preferentially embedded into a high-frequency area by extracting carrier image edges and fusing the carrier image edges to an encoder, so that the possibility of steganalysis and detection is reduced; and designing a multi-objective loss function, optimizing the generation quality by combining the PSNR and MS-SSIM, and ensuring that the secret information is preferentially hidden in the high-frequency component through the low-frequency consistency of the L1 norm loss constraint carrier and the secret-containing image of the low-frequency component. The invention provides an efficient and safe solution for the image steganography technology.

Techniques for utilizing invisible and / or inaudible watermarking techniques in media streams (e.g., video and / or audio streams) as a form of in-band steganography to authenticate the source capture camera and / or microphone to prevent deepfakes in network-based communication systems. User device(s) may establish cloud-based connections to a network-based communication system hosting media stream(s). An encoder of a watermarking component of a user device may receive video / audio data directly from a camera / microphone thereof and encode the video / audio data with watermark(s) representing a signed signature. A decoder of a watermarking component of a recipient user device and / or another device associated with the communication system may extract the watermarks and authenticate the signed signature using public certificate(s) associated with the sending user device to validate authenticity of the capture source(s) of the sending user device and that the video / audio data has not been modified since the time of capture.

The invention is suitable for the field of digital watermarking, and provides an anti-compression coding robust video watermark generation method based on an adversarial neural network, and the method comprises the steps: constructing an MSCA-GAN model; the model comprises an encoder, a decoder, a distortion layer, a discriminator network and an opponent network, and the specific steps are as follows: step S1, the encoder extracts different scale features of a video frame by using a multi-scale convolution attention mechanism, calculates attention weights, efficiently hides and embeds binary watermark information into the video, generates a watermark-containing video, and transmits the watermark-containing video to the decoder; performing confrontation optimization on an embedding strategy with a discriminator in training; according to the method, for H.264 compression layer special training, the multi-scale convolution attention mechanism and the depth separable convolution are combined, and the anti-compression robustness of the watermark under the H.264 standard is effectively improved; through common attack training such as noise layer simulation cutting and zooming, the watermark can still keep high extraction accuracy and robustness in a complex environment.

Techniques for latent space based steganographic image generation are described. A processing device, for instance, receives a digital image and a secret that includes a bit string. A pretrained encoder of an autoencoder generates an embedding of the digital image that includes latent code. A secret encoder is trained and utilized to generate an embedding of the secret to act as a latent offset to the latent code. The processing device leverages a pretrained decoder of the autoencoder to generate a steganographic image based on the embedding of the secret and the embedding of the digital image. The steganographic image includes the secret and is visually indiscernible from the digital image. Further, the processing device is configured to recover the secret from the steganographic image, such as by training and leveraging a secret decoder to extract the secret.

The invention discloses an artistic picture traceability intelligent anti-counterfeiting tracking method based on block chain fusion. The method comprises the following steps of S1, collecting surface micro texture image data of an artistic picture and performing preprocessing; s2, extracting high-dimensional texture features by using a deep convolutional neural network; s3, constructing a deep hash network and performing preliminary coding; s4, optimizing deep hash network hyper-parameters by applying a sea condition optimization algorithm; s5, generating an original anti-counterfeiting hash fingerprint and writing the fingerprint into the block chain for evidence storage through the smart contract; s6, collecting images in real time in the circulation process to generate a current Hash fingerprint, and comparing and verifying the current Hash fingerprint; and S7, synchronously chaining the verification result and the transaction record through the smart contract to form a traceability chain. According to the invention, through fusion of deep hash fingerprint generation and block chain intelligent evidence storage technologies, high-precision anti-counterfeiting authentication and credible traceability tracking of the artistic picture in the whole life cycle are realized.

A method, apparatus, non-transitory computer readable medium, apparatus, and system for image processing include obtaining an input prompt describing an image element, generating, using an image generation model, an output image depicting the image element and including a watermark, and identifying the training image as a source of the output image based on the watermark. The image generation model is trained using a training image including the image element and the watermark.

The invention discloses a screen shooting traceability method, device and equipment and a readable storage medium, which are applied to the field of computer software, and comprise the following steps: encoding watermark information to obtain a watermark information sequence; embedding the watermark information sequence into the image through an R channel, a G channel and a B channel of RGB (Red, Green, Blue) to obtain an image with a watermark; and acquiring the shot image with the watermark image, and tracing to obtain watermark information based on the shot image. According to the method, the watermark is generated by using the RGB channel of the single pixel image, so that the visual concealment of the watermark image is improved, the difficulty in perceiving of human eye recognition is ensured, and the visual quality of a user is not influenced; the watermark embedding position is not influenced by an original image carrier, and watermark information can be hidden in any area of the image; and the watermark information recovery success rate is high.

The invention provides a deep steganography image secret information blind extraction method based on self-supervised learning. The deep steganography image secret information blind extraction method comprises a coding stage, a self-supervised learning task generation supervision signal, attention coupling through an attention coupling module and a decoding stage. The coding stage comprises the following steps of: respectively taking two secret-containing images as input, performing Haar transformation method processing, splitting input features generated by the processing of the Haar transformation method of the secret-containing images into two paths through a bidirectional coupling mechanism of a coupling layer, realizing feature decoupling and cross-path interaction by utilizing nonlinear transformation, different paths are enabled to focus different components of secret-containing images respectively, and meanwhile, information lossless in the feature transformation process is ensured through reversible design. According to the method, the self-supervised learning technology is innovatively utilized, the model can deeply mine the characteristics and rules of the secret-containing image, and therefore secret information blind extraction independent of a secret key is achieved.

The invention relates to a source tracking and forgery detection method and system based on a face semantic mask, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a to-be-processed image; dividing a face semantic core region and a face semantic non-core region of the to-be-processed image, and generating masks of the face semantic core region and the face semantic non-core region; respectively embedding a detection watermark and a traceability watermark into the human face semantic core area and the human face semantic non-core area through a watermark encoder to obtain an image embedded with the detection watermark and the traceability watermark; processing the image embedded with the traceability watermark and the detection watermark through a noise layer to obtain a noise image; a traceability watermark and a detection watermark are extracted from the noise image through a traceability decoder and a detection decoder respectively, and whether the image is deeply counterfeited or not is judged; and carrying out loss function constraint training. According to the invention, the accuracy of image forgery detection can be improved.

Systems and methods for detecting phishing using image hashing include obtaining a plurality of images from different sources, generating a hash for each image, comparing at least one hash associated with a first image to one or more hashes associated with a second image, calculating a similarity score based on the comparing, and classifying the first image based on the similarity score.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The invention discloses a marine seat framework welding track real-time correction system based on machine vision and mechanical feedback. The marine seat framework welding track real-time correction system comprises a robot control unit, a welding unit, a vision sensing unit, a force sensing unit and a track correction module running on the robot control unit. The visual sensing unit is installed on an end effector of the robot and used for collecting image data including a to-be-welded weld joint and a peripheral area of the to-be-welded weld joint in real time. The force sensing unit is installed between a robot end effector and a welding gun and used for detecting six-dimensional force and torque signals between the welding gun and a workpiece in the welding process in real time. The core of the track correction module is a real-time correction algorithm based on multi-sensor information fusion, and the track correction module receives image data from the visual sensing unit and mechanical data from the force sensing unit. The sensing limitation of a single sensor is overcome, the vision provides macroscopic position deviation, the force sensing provides microcosmic contact and attitude information, and more comprehensive environment sensing is realized through complementation of the vision and the force sensing.

The invention discloses a generative image steganography method and system based on a diffusion model and face key points, and relates to the technical field of image steganography, and the method comprises the steps: inputting secret information needing to be hidden into a pre-trained face key point generative adversarial network, and automatically selecting face key points based on a face key point selection coding strategy, the secret information is coded into the face key points, and a face key point image of the coded secret information is generated; inputting the face key point image of the coded secret information into a diffusion model as a condition, and generating a secret-carrying face image corresponding to the face key point image by guiding the diffusion model; obtaining a secret-carrying face image, and extracting face key points in the secret-carrying face image by using a Dlib model; and inputting the extracted face key points into a face key point generative adversarial network, selecting a coding strategy based on the face key points, and recovering secret information through an inverse process of secret information coding. According to the invention, the detection resistance and safety of the steganography system can be improved.

The invention relates to the field of computers, in particular to a method and device for extracting watermarks. A digital watermark is embedded in a three-dimensional model, the risk that the three-dimensional model is illegally used can be reduced, however, the three-dimensional model is likely to be converted into two-dimensional data, an attack simulated by a neural network in the training process is generally an attack aiming at the three-dimensional model, and when the three-dimensional model is converted into the two-dimensional data, the form of the watermark is difficult to predict; effective watermarks are difficult to extract from two-dimensional data based on the neural network trained by the attack network. In the application, after the electronic device obtains the target file, the corresponding decoder in the neural network is used to extract the watermark based on the type of the target file, for example, the three-dimensional model decoder is used to process the three-dimensional model file, and the two-dimensional data decoder is used to process the two-dimensional data file, so that damage to the watermark caused by an attack means can be eliminated in a targeted manner, and the watermarking efficiency is improved. And the success rate of extracting the watermark from the attacked three-dimensional model is improved.

The invention discloses a bicycle center shaft press-fitting machining system based on machine vision positioning. The bicycle center shaft press-fitting machining system comprises a servo press-fitting module, a data acquisition module, a data processing module, a machine vision positioning module, a press-fitting control module, a quality detection module and a data management module. According to the system, image data and assembly quality data are acquired through data, and initial coordinates of mounting holes are extracted through median filtering, Canny edge detection and Hough circle transformation; further adopting an RANSAC algorithm to fit the cylindrical surface of the inner wall of the five-way pipe, accurately calculating a center point space coordinate and an axis direction vector, and converting the center point space coordinate and the axis direction vector to a press-fitting coordinate system through hand-eye calibration; the press-fitting process is dynamically regulated and controlled in three stages of pre-pressing, main pressing and final pressing, the press-fitting speed is dynamically regulated, and meanwhile, online judgment of the assembly quality is achieved through displacement-pressure curve analysis; according to the system, the problem of bearing abrasion caused by alignment deviation of a traditional press-fitting central axis is solved, and the press-fitting precision and the yield are improved.

The invention discloses an image hiding method based on a space-channel attention mechanism and a related device, and belongs to the technical field of image hiding. The method comprises the following steps: inputting a secret image and a carrier image into a pre-trained image hiding network model to obtain a secret-containing image and loss information, and completing image hiding; the image hiding network model is an image hiding network model based on a space-channel attention mechanism and comprises a preprocessing network, a hiding network and an extraction network; the hidden network takes a reversible neural network as a basic framework, forward propagation and reverse propagation processes of the reversible neural network are modeled as encoding and decoding processes of an image according to the requirement of an image steganography task, and reversible hiding and high-quality extraction of a secret image are realized in combination with DWT (Discrete Wavelet Transform).

The invention discloses a numerical control machine tool autonomous tool setting method based on 3D visual guidance. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutter and a workpiece by using a 3D visual sensor and preprocessing; then, segmenting the point clouds through a region growing algorithm, realizing the separation of the tool point clouds and the workpiece point clouds, and extracting the point clouds of each cylindrical section of the workpiece; thirdly, based on a cylinder fitting algorithm, cylinder model fitting is conducted on the segmented tool and workpiece point cloud, and geometric parameters such as the axis direction, the radius and the center coordinate are obtained; calculating a relative position relation between the tool and the workpiece according to the geometric parameters, generating a tool setting path and converting the tool setting path into an NC code; and finally, the NC code is transmitted to a grinding machine control system through a grinding machine communication module, and tool setting operation is completed. The automatic tool setting device has the advantages that the relative position relation between the workpiece and the tool can be automatically measured in the machining process, automatic and rapid tool setting is completed, and the machining quality and efficiency are guaranteed.

The invention discloses a watermark processing method and device, equipment, a medium and a program product. The method comprises the steps of obtaining an original document and an original watermark in response to a watermark generation instruction determined by a user; performing feature extraction on the original document to obtain structural features, semantic features and visual features corresponding to the original document; constructing an adaptive intensity mapping function based on the structural features, the semantic features and the visual features; constructing a quantum state watermark function based on the original watermark and a preset quantum parameter; and according to the adaptive intensity mapping function, the quantum state watermark function and a preset quantum entanglement enhancement function, a target watermark is generated, and the preset quantum entanglement enhancement function is used for realizing conversion between the original watermark and the quantum entanglement state. According to the embodiment of the invention, the watermark strength can be adaptively adjusted according to the document features, the watermark security is improved, and the document security is guaranteed.

The invention belongs to the technical field of time sequence settlement data, and discloses a data processing method and device and electronic equipment. According to the invention, spatial registration and reference unification of the multi-source time sequence settlement result image can be realized, that is, data standardization processing based on the target time sequence result settlement image is carried out, and to-be-processed time sequence result settlement images with different sources, different resolutions, different ranges and point position differences are subjected to data standardization processing. The resolution, the range and the point position which are completely consistent with the target time sequence result settlement image are unified, a solid foundation is provided for subsequent data fusion analysis and long-time sequence settlement monitoring, the problem that in the prior art, multi-source data are difficult to directly match and join is effectively solved, and the availability, the consistency and the accuracy of the data are improved.

The invention discloses a watermark embedding and detecting method and system based on a large model. The method comprises the following steps: acquiring a real text sequence of a watermark to be embedded; constructing a watermark embedding and detecting model based on a large model; and completing watermark embedding and detection of a real text sequence by using the watermark embedding and detection model. According to the method, the watermark is directly embedded in the model training process, and the detection mechanism is optimized, so that efficient embedding and accurate detection of the watermark are realized. A generative adversarial network and a binocular telescope structure are innovatively introduced, so that the natural fluency of a generated text is ensured, and the robustness of a watermark under adversarial attack and natural rewriting is remarkably enhanced. Meanwhile, the calculation cost is reduced by adopting a low-rank adapter technology, so that the method can be widely applied to various pre-training large models and has very high adaptability.

The invention provides a precision machining and 3D printing mixed manufacturing execution system, and relates to the technical field of data processing, and the system is used for dividing a three-dimensional model into a configuration area and a finishing area, generating a layer-by-layer closed contour path, obtaining configuration path data, extracting a boundary segment close to the finishing area, and obtaining a configuration path data set; calculating a space projection vector on a finishing target surface to obtain primary finishing path data; calculating a boundary torsion value; superposing interference offset in a historical sample path to a primary finishing path; calculating an interference amplitude index; the fitting path data and the configuration path data are subjected to path connection and unified processing according to the manufacturing sequence, and a mixed execution path instruction is obtained. Precise machining and 3D printing are integrated, and path overlapping, empty cutting or unnecessary machining is avoided.

The invention discloses an intelligent planning method for a motion track of a multi-axis mechanical arm, and relates to the technical field of automatic control, and the intelligent planning method comprises the following steps: setting a task target and initial parameters of the mechanical arm, collecting environment data to generate an environment map, and constructing a dynamic environment model, the initial parameters of the mechanical arm comprise the initial position and posture, the joint angle, the speed and the acceleration; a mechanical arm track generation model is constructed, a mechanical arm preliminary track is generated based on the mechanical arm track generation model, the dynamic constraint of the mechanical arm preliminary track is checked, and the mechanical arm preliminary track is optimized; and predicting a future track of the obstacle according to the dynamic environment model, and combining the optimized preliminary track of the mechanical arm and the future track of the obstacle to generate a motion track of the mechanical arm. According to the method, through dynamic environment modeling, accurate pre-judgment of the motion trail of the dynamic obstacle is achieved, and the real-time adaptability of the mechanical arm in a complex scene is remarkably improved.

The invention discloses an image watermark processing method and device, electronic equipment and a storage medium, and relates to the field of financial science and technology. The method comprises the following steps: in response to an image sending request, generating a first chaotic sequence according to a sender identity, a sending timestamp and a chaotic sequence control parameter; segmenting the to-be-sent image into a plurality of image blocks, and determining basic watermark data according to channel values of pixels in the image blocks, a pre-specified private key, a sender identity identifier and a sending timestamp; determining the watermark length of a single image block according to the total length of the basic watermark data and the number of the image blocks, and distributing block-level watermark data for the image blocks from the basic watermark data according to the watermark length; according to the first chaos sequence, determining a first starting coordinate in the image block and a first channel in which a watermark needs to be embedded; and embedding a watermark into the image block according to the first initial coordinate, the first channel and the block-level watermark data. According to the technical scheme, the security and reliability of the image watermark are improved.

The invention relates to the technical field of visual inspection, and particularly discloses a visual control system and method for plastic bottle sorting based on visual inspection, and the method comprises the following steps: S1, starting a camera when a plastic bottle is detected, and calculating the imaging time; s2, obtaining an exposure value of the imaging picture, presetting an exposure interval, marking abnormal pixel points, calculating the proportion of the abnormal pixel points, and carrying out preliminary screening on the plastic bottles based on the proportion of the abnormal pixel points; s3, marking to-be-sorted bottles, arranging an NIR sensor, emitting near-infrared light when the to-be-sorted bottles pass, acquiring a spectrum reflected by the to-be-sorted bottles, and determining wavelengths of absorption peaks corresponding to the to-be-sorted bottles based on the spectrum; and S4, the materials of the bottles to be sorted are determined based on the wavelengths, the total time and distance coefficient are calculated, and the plastic bottles are blown out through the corresponding air valves to complete sorting. The sorting efficiency is improved, the labor cost is reduced, and meanwhile, the false drop rate can be further reduced through automatic work of a machine.