15 results about "Color vector" patented technology

An information processing method according to one embodiment of the present disclosure comprises: calculating a first color vector on the basis of two or more pixel values representing the color of a pixel in a first image, and calculating a second color vector on the basis of two or more pixel values representing the color of a pixel in a second image different from the first image (S10); an angle formed by the first color vector and the second color vector is calculated (S20), and a corresponding point is searched for in the first image and the second image on the basis of the calculated angle (S30).

The invention provides a method, a device and a computer program product for measuring sludge sedimentation parameters. The method comprises the following steps: obtaining a first image sequence of a sedimentation process of sludge at a first concentration; determining a sludge sample area and a background sample area not including the sludge sample area in the initial to-be-processed image in the first image sequence; executing the following operations on each of a plurality of to-be-processed images in the first image sequence: determining a first mean vector based on color vectors of all pixels in a sludge sample region of the to-be-processed image, and determining a second mean vector based on color vectors of all pixels in a background sample region of the to-be-processed image; training a binarization judgment function for the to-be-processed image; obtaining a binary image of the to-be-processed image; and obtaining the sludge area height of the to-be-processed image based on the binary image of the to-be-processed image.

This invention provides a method for intelligent monitoring and early warning of water droplet impact in substations during rainy weather, relating to the field of intelligent operation and maintenance and condition monitoring of substations. The method includes: acquiring dual-optical-path video streams; constructing a cross-spectral neural radiation field network; using joint high-dimensional features as input to output volume density and radiation color vectors; calculating the equivalent water film thickness from these two vectors to generate an equivalent water film thickness distribution field; mapping this field to a three-dimensional model, subdividing the mesh according to the thickness gradient modulus, and solving for the electric field to obtain the electric field distortion tensor and calibrate the high-discharge-risk critical region; generating a Lagrange particle set, solving the dynamic equations through smooth particle hydrodynamics, and simulating the water film evolution to obtain the probability of conductive water bridge connection; inputting the distribution field, distortion tensor, and connection probability into a multi-modal impedance reconstructor to generate an electrochemical impedance spectrum to obtain the dynamic insulation margin attenuation rate; and triggering an early warning based on the determined conditions. This invention significantly improves the timeliness and accuracy of early warning for external insulation faults during rainy weather.

The method involves determining initial color vectors associated with digital pathology stains (or chromogens) from pure color digital pathology images, and then adjusting the determined color vectors to help improve stain unmixing performance. The adjustments are performed via an interface and / or automated techniques that apply adjustments to the color vectors based on actual multiplex images and one or more composite singleplex images. These adjusted color vectors are then further utilized for stain unmixing of a given multiplex image. [Effects] This method facilitates the detection of labeled cells and the quantification of signals using image analysis.

The invention relates to the field of three-dimensional modeling, in particular to a natural reserve ecological data dynamic fusion method based on a three-dimensional geographic information system. The method comprises the following steps: obtaining an original observation value of an ecological factor, and carrying out standardization processing to obtain a standardized value of the ecological factor; calculating a deviation degree based on the standardized value of the ecological factor; based on the standardized values and the deviation degrees of the ecological factors, an ecological data dynamic fusion algorithm is adopted, and ecological response indexes are obtained through calculation; and based on the ecological response indexes, generating a final color vector through an ecological response color mapping algorithm. The problems that according to a traditional ecological data dynamic fusion method, all monitoring points are regarded as independent individuals, and the propagation characteristics of short-distance high influence and long-distance gradual attenuation are difficult to quantify are solved; the problems that in a three-dimensional scene, abstract ecological stress intensity is difficult to convert into visual and gradually-changing color information, parameterized regulation and control on human eye sensitivity are lacked, and a high-risk area is easy to cover or misjudge are solved.

The present disclosure relates to stain unmixing of digital pathology images by determining initial color vectors associated with digital pathology stains (or chromogens) from pure-color digital pathology images. The determined color vectors may be fine-tuned or adjusted to help improve the stain unmixing performance. The adjustment may be performed via the interface and / or automated technique that, based on a real multiplex image and one or more synthetic singleplex images, perform adjustments to the color vectors. These adjusted color vectors may be further leveraged for stain unmixing of a given multiplex image. Additionally, the disclosure provides techniques to generate synthetic pixels and the associated color vectors, a recommended stain to be added to a multiplex image and / or generation of multiplex images from one or more digital pathology images based on the targeted color vectors.

This invention relates to the field of power data technology and discloses a method, system, device, and medium for classifying and managing multi-source power demand data. The method includes: constructing a multi-layer graph structure based on the acquired multi-source power demand dataset; constructing a search sample set based on the multi-layer graph structure, where each search sample includes a color vector, a state vector, and a fitness state. The color vector is determined by semantic features, and the fitness state is set to reflect the semantic connectivity of the semantic graph, the state fluctuation amplitude of the state graph, and the adjacency density of the spatial adjacency graph; iteratively updating the search sample set using an improved peacock optimization algorithm to obtain the optimal fitness state of each search sample, thereby determining the classification label corresponding to the search sample. The improved peacock optimization algorithm is configured to iteratively update each search sample based on the perturbation guidance vector and the direction of the gravitational gradient. This method improves the accuracy and adaptability of power demand sorting.

The present disclosure provides a color prediction model training method, device and equipment, relates to the technical field of artificial intelligence, and in particular to the technical field of deep learning and image processing. A specific embodiment of the method comprises: obtaining a training sample, wherein the training sample comprises a sample object image of a sample object and a sample color vector of a sample pixel point of the sample object image; sampling in a sample object space of the sample object to obtain a sample sampling point corresponding to the sample pixel point of the sample object image; performing color prediction on the sample sampling point by using a neural network to obtain a sample color vector of the sample sampling point; calculating a loss function based on the sample color vector of the sample sampling point and the sample color vector of the corresponding sample pixel point; and updating parameters of the neural network based on the loss function to obtain a color prediction model. The color prediction model trained by the embodiment has relatively accurate color prediction capability.

The application provides a traffic cone guiding indication control method based on a color change control algorithm, which comprises the following steps: initializing various parameters, the various parameters comprising color vector information and initial display content of a display screen; inputting a total number of traffic cones and a current traffic cone identifier; determining a color to be displayed by the current traffic cone according to the color change control algorithm based on the inputted total number of traffic cones and the current traffic cone identifier; when a change in the total number of traffic cones is monitored, inputting the changed total number of traffic cones again; inputting the current cone identifier again, and determining the color to be displayed by the current traffic cone again according to the changed total number of traffic cones and the current traffic cone identifier. The application can more intelligently control the guiding display of traffic cones, can provide guiding indication for vehicle flow and pedestrians, and can effectively avoid traffic congestion and even traffic accidents caused by the failure to provide guiding and indication for vehicles and pedestrians.

This provides an information processing method that can accurately search for corresponding points in multiple images. [Solution] In a shape measurement method according to one aspect of the present disclosure, a first color vector is calculated based on two or more pixel values ​​indicating the color of a pixel in a first image, and a second color vector is calculated based on two or more pixel values ​​indicating the color of a pixel in a second image different from the first image (S10), the angle between the first color vector and the second color vector is calculated (S20), weight values ​​are calculated based on the length of the first color vector and the length of the second color vector (S30), and corresponding points in the first image and the second image are searched based on the calculated angle and weight values ​​(S40).

The application relates to the technical field of image recognition, in particular to an intelligent green belt self-adaptive precise pruning system, which comprises an image time sequence acquisition module, a flexible micro-motion recognition module, a space contour aggregation module, a space contour aggregation module and a pruning boundary recognition module.In the application, continuous pictures are acquired to establish a fixed depth cache and consistency check, eliminate acquisition jitter, ensure stable space-time reference, capture flexible micro-motion by using gray scale symbol difference and switching count, eliminate rigid background interference, overcome light and shadow misjudgment through three-dimensional color vector direction calculation, realize discrete pixel space merging and path smoothing by combining grid density iteration and boundary constraint adjustment, avoid frequent mechanical correction, and ensure pruning precision.

This invention relates to the field of image recognition, and more particularly to an image recognition method for the health status of marine aquaculture fish. First, based on the acquired raw RGB color image, instance-level segmentation is performed to obtain the category label, confidence score, and pixel-level binary mask for each fish instance. Based on the confidence score, a confidence score filtering process is performed. After obtaining the fish instances that pass the confidence score filtering, image preprocessing is performed to obtain a normalized color vector and a preprocessed RGB color image. Then, based on the normalized color vector and the preprocessed RGB color image, a posture-sensitive morphology-color joint health index extraction algorithm is used to obtain a health-sensitive feature index. Finally, based on the health-sensitive feature index, a judgment result is obtained. This method solves the technical problems of morphological indicators becoming ineffective due to fish posture distortion, the inability to quantify abnormal color features such as red spots and white spots, and the impact of fish species differences on the universality and accuracy of the assessment results.

The present application belongs to the technical field of image processing and computer vision, and particularly relates to a low-illumination image enhancement method based on Gaussian light field sputtering guided self-attention. Firstly, multi-scale feature extraction is performed on the low-illumination image, and Gaussian geometric parameters and semantic Token are generated by using multi-scale Gaussian markers; then in the self-attention calculation process, Gaussian affinity is constructed according to the Gaussian geometric parameters, and is introduced into the attention weight calculation as a physical bias to obtain enhanced features fused with physical light field prior; a continuous space gain field is reconstructed based on the enhanced features, and the input image is corrected in brightness by using the space gain field to obtain an enhanced image; and the network is optimized by using an unsupervised training target containing a color vector angle loss and a brightness edge loss. The present application can improve the light recovery capability, color fidelity capability and detail preservation capability in a complex low-light scene, and can be applied to the fields of intelligent monitoring, automatic driving, mobile terminal imaging and the like.

Video delivery methods and apparatus providing dynamic range conversion with perceptually lossless backward compatibility. An example video delivery method includes generating a first lookup table (LUT) configured for converting images of a first dynamic range into images of a second dynamic range larger than the first dynamic range, the generating including adjustment of the first LUT based on differences between first and second color vectors of the first dynamic range, the second color vector being generated by applying a second LUT to a third color vector, the second LUT being configured for converting images of the second dynamic range into images of the first dynamic range, the third color vector being generated by applying the first LUT to the first color vector. The method further includes using the first LUT to convert a first video having the first dynamic range into a second video having the second dynamic range.

This disclosure relates to stain unmixing of digital pathology images by determining initial color vectors associated with digital pathology stains (or chromogens) from pure color digital pathology images. The determined color vectors may be fine-tuned or adjusted to help improve stain unmixing performance. Adjustments may be performed via interfaces and / or automated techniques that perform adjustments to the color vectors based on actual multiplex images and one or more composite singleplex images. These adjusted color vectors may be further utilized for stain unmixing of a given multiplex image. Additionally, this disclosure provides techniques for generating multiplex images from one or more digital pathology images based on composite pixels and associated color vectors, recommended stains to be added to the multiplex image, and / or target color vectors.