3978results 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.

The invention provides a digital image watermarking method and device based on a diffusion model and a spread spectrum technology, and relates to the technical field of image processing and artificial intelligence. The method comprises the steps of obtaining a carrier image and watermark information; performing down-sampling and forward diffusion on the carrier image by using a variational auto-encoder to obtain a first potential vector; performing channel dimension division on the first potential vector, selecting a one-dimensional channel, performing frequency domain transformation through a discrete cosine transform technology, extracting a high-frequency coefficient, and obtaining an embedded matrix; performing spread spectrum modulation on the watermark information by using an orthogonal code to obtain a spread spectrum watermark; embedding the spread spectrum watermark into the embedded matrix by combining a strength factor to obtain a watermark potential vector; and after carrying out reverse denoising on the watermark potential vector by utilizing a denoising network, restoring the watermark potential vector into a watermark image through a variational auto-encoder, and combining the watermark potential vector with a carrier image to obtain a final watermark image. According to the method, the imperceptibility and robustness of the watermark are greatly enhanced, and regenerative attacks can be effectively resisted.

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.

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 relates to the technical field of titanium anodes, and provides an automatic coating method of a titanium anode and related equipment, and the method comprises the following steps: obtaining a target coating area of the titanium anode, coating, carrying out data acquisition on the target coating area in the coating process to obtain real-time coating data, and calculating coating uniformity and thickness change conditions; evaluating the coating uniformity, and generating a coating quality evaluation result and a coating defect distribution condition; based on the coating quality evaluation result and the coating defect distribution condition, the thickness change condition is analyzed, and thickness optimization parameters are obtained; and generating a feedback atlas according to the thickness optimization parameters, and adjusting coating parameters of the automatic coating equipment based on the feedback atlas. Through application of automatic coating equipment, a sensor array, real-time data acquisition and a coating analysis model, the problems that when uniformity and thickness in the coating process are controlled, the coating quality is unstable, the coating thickness is not uniform, and coating parameters are difficult to monitor and adjust in real time are solved.

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.

The present application provides a watermark information embedding method, device, storage medium and program product, wherein the method includes obtaining the target watermark information to be embedded and the original carrier information in response to a watermark embedding instruction; mapping the target watermark information to a latent variable space to obtain a watermark latent vector, and mapping the original carrier information to the latent variable space to obtain a carrier latent vector; fusing the watermark latent vector and the carrier latent vector to generate a noise latent vector with a watermark; denoising the noise latent vector to generate a target carrier information with the target watermark information. The present application realizes that when the target carrier information is subjected to various complex editing processes, the watermark information can still be effectively extracted, thereby improving the robustness of the watermark and providing a reliable data basis for subsequent digital information ownership protection.

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.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

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.

The invention discloses a model watermark robustness enhancement method based on antagonistic immunity, which mainly solves the problem that the existing model watermark is difficult to resist various watermark removal attacks, and adopts the scheme that a public data set and a convolutional neural network suitable for an image classification task are respectively used as original data and a target model; training a target model by using the original data; taking the generation model as a watermark generation model and training the watermark generation model by using the original data; inputting the original data into the trained generated watermark model for forward propagation to output watermark data; splicing the original data and the watermark data, and inputting the spliced original data and watermark data into the trained target model for forward propagation to obtain a model watermark; tiny disturbance is added to original data to generate an adversarial sample, the adversarial sample is spliced with the original data and then input into the model watermark for robustness training, and the model watermark with enhanced robustness is obtained. The method can effectively enhance the generalization ability of the model watermark under different data distributions, improves the robustness of the model, and can be used in the fields of information security, financial services, medical care and the like.

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.

In various examples, visual differences between video data and an encoded version of the video data are used to determine updates to quantization parameters (QPs) used to encode the video data to store or upload video clips that corresponds to notable events associated with a machine. The video data may correspond to images applied to a machine learning model to perform control operations for the machine. A metric may be used to quantify the visual differences. To evaluate the visual differences, samples of the video data and encoded video data may be determined and analyzed, rather than entire images or frames. To selectively enable updates to QPs, the system may detect that a deviation between a bitrate corresponding to the encoded data and a reference bitrate has exceeded a threshold.

The invention discloses an intelligent anti-counterfeiting color box printing positioning control method, and relates to the technical field of printing positioning control, and the method comprises the steps: dividing a color box layout into a plurality of detection regions, setting a cross-shaped and circular combined positioning mark in each region, and collecting positioning information in real time through a high-resolution image sensor to establish a feature database; extracting regional deformation and position features by using a multilayer convolutional neural network, calculating actual deviation by combining a residual network, constructing a mapping model of process parameters and position deviation, and establishing a differential compensation model by using a deep reinforcement learning algorithm; and the compensation parameters are distributed to a servo control unit, a motion control instruction is generated, a compensation effect evaluation index is adopted to carry out real-time evaluation on position deviation, a decision model is established to realize compensation parameter adaptive optimization, and regional compensation adaptive adjustment is realized. According to the method, intelligent monitoring and optimization of the anti-counterfeiting printing process are achieved, and the printing quality stability and the production efficiency of the anti-counterfeiting color box in batch production are greatly improved.

The invention provides a disordered material identification and positioning method and system based on a 3D visual technology. The method comprises the steps that S1, a 3D model of a material object is converted into point cloud data, downsampling processing is conducted on the point cloud data, template point cloud of the material object is generated, and PPF features of the template point cloud of the material object are calculated; s2, in a disorderly stacked material scene, based on the obtained RGB image and depth image of the material scene, performing material segmentation by using an SAM model to obtain a material point cloud of each instance, and calculating a PPF feature of the material point cloud in the material scene; and S3, matching the PPF features of the material point clouds in the material scene with the PPF features of the template point clouds to obtain a 6D pose of each material, and realizing recognition and positioning of disordered materials through single matching. According to the method, the deep learning model and the 3D vision technology are combined, the accuracy and efficiency of disorderly stacked material recognition are improved, and the method is widely applied to automatic grabbing and assembling tasks in storage, production line and other complex environments.

Methods, devices, and systems for digital space management in virtual reality and augmented reality are described. A method can include a terminal that logs in to a preset application based on a first user identifier, presents a first interface, where the first interface includes a virtual space registration option. In response to an enabling operation of a user for the virtual space registration option, the terminal photographs a first scene to obtain a preview stream, obtains a target geometry, and sends the first user identifier, the preview stream, and the target geometry to a server. The preview stream and the target geometry are used to determine a target virtual space in which a user with the first user identifier has virtual content editing permission in a first virtual space. The first virtual space is a virtual space corresponding to a real world including the first scene. The target geometry is used to represent a form of the target virtual space.

The invention discloses a multi-modal information embedded image generation service watermark protection method, which belongs to the technical field of image processing, and comprises the following steps: obtaining text information, an original image and an edited image after the original image is edited by an image generation service; and embedding the original image and the text information into the edited image by adopting a trained bireversible neural network, and outputting a watermark-containing image. The problems that in the prior art, watermarks are easy to perceive, the image editing process cannot be restored, and evidence is incomplete during evidence obtaining are solved.

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 a 3D Gaussian spatter model copyright protection method, which comprises the following steps: acquiring a 3D Gaussian spatter model, performing secondary training on the 3D Gaussian spatter model, embedding watermark information into the 3D Gaussian spatter model, and acquiring the 3D Gaussian spatter model subjected to secondary training; and constructing a message extractor based on a deep neural network, retrieving the image rendered by the 3D Gaussian splash model through the message extractor, and extracting watermark information. According to the method, the watermark information is embedded into the model through secondary training of the 3D Gaussian splash model, and then the watermark information is extracted by using a specific extractor. And meanwhile, relatively high rendering quality is kept. And meanwhile, the model can be effectively prevented from being maliciously stolen.

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.

The invention relates to the technical field of digital image processing, in particular to a digital image mixed watermarking method based on time domain and frequency domain synchronization. According to the method, a time domain and frequency domain synchronous watermark embedding algorithm and a multi-stage watermark extraction algorithm are innovatively combined, so that the concealment and robustness of the watermark are remarkably improved, and the problems of digital image copyright protection in the face of printing and scanning attacks and network attacks are effectively solved; the method ensures the security and reliability of digital image copyright protection, provides a more advanced and effective technical means for digital image copyright protection, has the advantages that a time domain and frequency domain synchronous watermark embedding algorithm enables watermarks to be fused into image details and is high in concealment, the watermarks are effectively protected in the face of various attacks, and the security and reliability of digital image copyright protection are improved. The multi-stage watermark extraction algorithm ensures accurate extraction when the image quality is reduced, and the watermark performance is integrally improved.

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 discloses a digital archive safe circulation tracking method and system based on a dynamic watermark, and relates to the technical field of digital archives, and the method comprises the steps: collecting the physical layout features and semantic features of an archive at the same time, generating a comprehensive archive feature map, and dynamically adjusting the watermark intensity in combination with a rank screening threshold; a visual fidelity and watermark extraction degree double-index evaluation system is introduced, watermark position evaluation is carried out, and archive carrier sequence substandard and standard labels are obtained; and by monitoring the watermark survival rate, the visual fidelity and the circulation error rate, a correction strategy selection rule is constructed and comprises position iteration correction or sequence correction, so that evaluation and dynamic calibration of the watermark embedding position are realized, and dynamic selection of a correction strategy is realized.

The invention relates to the technical field of digital watermarking, and discloses a self-adaptive image watermark embedding and recovering system, which comprises an image preprocessing module used for carrying out discrete wavelet transform on a carrier image to be embedded with a watermark, decomposing the image into a plurality of frequency sub-bands, and selecting a low-frequency sub-band as a watermark embedding region; the watermark preprocessing module is used for carrying out orthogonal transformation on the watermark image, so that orthogonality is kept among a plurality of watermarks, and mutual interference of the watermarks is reduced; and the watermark embedding module is used for embedding the watermark into the low-frequency sub-band of the carrier image based on the output of the watermark preprocessing module. According to the method, the concealment and robustness of the watermarks are improved through the technologies of self-adaptive optimization of the watermark strength, redundancy embedding and orthogonal transformation, and compared with the prior art, the interference between the watermarks is effectively reduced, and the resistance of the watermarks to attacks such as compression, noise, cutting and rotation is enhanced.

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.