130 results about "Skin color" patented technology

The invention relates to the technical field of agricultural product storage management, in particular to an agricultural product storage intelligent management system based on big data, which comprises a multi-source data acquisition layer, an edge processing layer, a cloud processing layer and an execution control layer, compared with the prior art which only depends on manual recording of the picking date or simple visual sampling inspection for judging the freshness, the method has the advantages that the freshness key indexes such as the skin color degree and the texture structure of the agricultural product are synchronously and quantitatively extracted through the deep learning model in the warehousing link of the agricultural product; performing dual cross validation with the registration time to eliminate abnormal samples; the verified freshness reference data and parameters such as temperature, humidity, gas concentration and the like monitored in real time in a corresponding storage microenvironment are fused and input, and a self-adaptive shelf life prediction model based on a dynamic environmental stress factor is constructed; therefore, continuous and accurate quantification and early warning of decay processes of different types of agricultural products under various storage conditions are realized.

The invention discloses a remote photoplethysmography (rPPG) signal processing method and system based on a long and short term space-time convolution network, and belongs to the crossing field of biomedical signal processing and computer vision. In order to solve the problem of signal distortion caused by illumination fluctuation, motion artifacts and skin color differences, the method constructs a multi-scale space-time modeling framework: extracting local space-time features of transient changes of facial capillaries by adopting a 3D convolutional network, capturing long-range periodic features of heart rate rhythm in combination with a 1D expansion convolutional network, and establishing a multi-scale space-time modeling framework; the spatial-temporal characteristics are dynamically fused through the self-attention weight and the gating residual structure, and the anti-interference capability is improved. In the preprocessing stage, a face area is positioned through MTCNN, motion artifacts are compensated by using an optical flow equation, and signal purity is enhanced by combining a skin color mask and a YUV color space. The system adopts a deep separable convolution and parallel acceleration strategy to realize light weight, and optimizes the network through time domain MSE loss and frequency domain KL divergence. Experiments show that the phase error of the method is reduced by 40% in a dynamic scene, the signal amplitude of a deep skin color group is improved by 60%, the method is suitable for non-contact health monitoring equipment, and the robustness and the measurement precision of the rPPG technology in a complex environment are remarkably improved.

The invention relates to the technical field of photo three-dimensional processing, and discloses a three-dimensional head portrait model generation method based on a single photo, which comprises the following specific steps: photo input processing: firstly, extracting a face region from the single photo by using a face feature point detection algorithm; background segmentation is carried out by using an SAM algorithm, and a face region in the picture is extracted; finally, the depth information of the human face is speculated through a Depth Anything model, and the depth information is used for guiding generation of Gaussian point cloud; according to the three-dimensional head model generation method based on the single photo, the high-precision three-dimensional head model can be generated from the single photo. According to the method, the 360-degree rotating video is firstly generated, and then three-dimensional reconstruction is carried out by utilizing Gaussian splashing, so that the finally generated 3D head model is highly matched with the input picture, and the similarity of the model is relatively high. Besides, the method can retain abundant detail features, and the Gaussian splashing point cloud can accurately restore personalized features of the human face, such as wrinkles, skin colors, hairlines and the like, so that the model is more real and natural.

The invention provides a self-adaptive real-time image quality enhancement method and system, and the system comprises an image data conversion module which is used for converting YUV format data of a video stream into an RGB format through OpenGL, and further converting the RGB format into an HSL color space; the adaptive brightness enhancement module dynamically adjusts an enhancement coefficient according to the brightness component of the current frame; the multi-frame joint skin color detection module is used for determining a skin color area based on the chromaticity and saturation threshold range of the HSL color space; the local self-adaptive color enhancement module is used for inputting image generation dynamic parameters and performing color enhancement; the noise analysis and noise reduction module is used for performing soft threshold processing on the high-frequency signal; the texture adaptive detail enhancement module is used for adjusting sharpening strength based on texture complexity; and the GPU acceleration processing framework is used for rendering and outputting the enhanced RGB image in real time. Through a series of technical innovations, the image quality of video playing and the user experience are remarkably improved in multiple aspects, and the method has important practical application value.

The invention provides a face fusion preprocessing method and system based on hair repairing and lightweight skin beautifying, and relates to the technical field of face image preprocessing and computer vision. The core method comprises the following steps: firstly, performing hair shielding restoration on an original image, obtaining a restored image, and then performing lightweight skin beautifying to obtain a beautified image; performing risk assessment and intervention closed loop, wherein the system calculates three key indexes: a skin area non-natural smoothness index; a hair repair boundary structure distortion index; a skin color non-physiological deviation index; the indexes are combined with a weighting model to calculate risk measurement; based on the measurement, the warning threshold and the critical threshold, determining a dynamic fusion coefficient, and finally performing weighted fusion on the repaired image and the beautified image to generate a final image; according to the method, by introducing a boundary structure distortion index, the problem of lack of cooperative control between repairing and beautifying modules is solved, the beautifying strength can be dynamically adjusted, artifact superposition is effectively prevented, and the sense of reality and the sense of naturalness of an output image are improved.

An image capturing apparatus for capturing an object and generate an image, displaying the image, performing signal processing for the image, and accepting an input from a user, wherein the image is displayed with a vectorscope superimposed thereon, the vectorscope indicating a distribution of color components included in the image based on a hue and a color density and an index for specifying a range of a skin color region in the image, a display position of the index on the vectorscope is specified based on the hue and the color density values, and a size of the index is specified by the range of the skin color region value, and processing is performed for decreasing a detail level of an image signal of the image having a color component included in the index.

The invention discloses a tumor radiotherapy prognosis prediction simulation method and system based on big data and a medium, and relates to the technical field of tumor radiotherapy prognosis, and the method comprises the following steps: collecting basic physiological information of a tumor patient, and collecting a color image and a temperature image of skin of a radiotherapy part of the tumor patient; skin color feature extraction and skin temperature feature extraction are carried out to obtain skin color feature data and skin temperature feature data; a radiodermatitis prediction model is constructed; performing prediction simulation on the skin of the radiotherapy part of the tumor patient based on the radiodermatitis prediction model, and performing prediction simulation correction to obtain a radiodermatitis prediction result of the tumor patient; the method is used for solving the problem that when an existing tumor radiotherapy prognosis technology is used for predicting and simulating disease development, the risk and time of radiodermatitis of a tumor patient cannot be objectively and accurately predicted and simulated according to physiological characteristics of skin of the tumor patient after radiotherapy.

The invention relates to the technical field of computer vision and human-computer interaction, and discloses a self-adaptive hand image recognition method and system based on multi-source feature fusion. The method comprises the following steps: acquiring a real-time video stream image from a camera, carrying out various filtering processing on the real-time video stream image, and respectively generating an illumination enhanced image, a background subtraction mask, a skin color area mask and a dynamic motion mask; dynamically adjusting the fusion weight of each filtering processing result according to the scene features of the real-time video stream image, and generating comprehensive filtering output through weighted fusion; performing multi-stage hand segmentation processing on the comprehensive filtering output to obtain an initial hand region mask, performing time sequence smoothing processing, and extracting a maximum connected domain as a final hand region mask; and inputting the final hand region mask into a convolutional neural network for gesture recognition, outputting a prediction probability of each type of gestures, and displaying a gesture classification result in real time. According to the method, the adaptability of hand detection to strong interference backgrounds and variable environments is enhanced.

The invention discloses a low-cost robot imitation learning method and system based on a human video. The method comprises the following steps: S1, data acquisition; s2, data extraction and physical alignment are carried out to eliminate man-machine physical differences; the step is divided into two parallel processing modules of action space alignment and visual space alignment; s3, data set construction: mixing the aligned human data with real robot teleoperation data, carrying out balanced sampling, and constructing a mixed data set Dmix; and S4, cooperative training: constructing a strategy network based on diffusion Transform for training. According to the method, data can be acquired only through the monocular RGB camera, expensive robot teleoperation data are replaced with cheap and easily available human videos, and the data acquisition threshold is greatly reduced. Through a visual alignment strategy of random color grid rendering, a network can learn neglect skin color textures and pay attention to geometric structures without a complex generative model, so that the robot can be seamlessly migrated to robots in different forms.

The invention provides a light control method for lamps, a storage medium and a lamp control system applied to a live video scene, and the lamp control system is connected with at least one camera device and at least two lamps. Obtaining current display parameters sent by the camera equipment in the connected state, model information of the lamp and a face preference instruction and a skin color selection instruction input by a user; based on the model information, screening a target lamp meeting a first preset condition from the at least two lamps; performing matching in a plurality of target combination modes corresponding to the face preference instruction to obtain a target combination result corresponding to a target lamp meeting a second preset condition; matching the skin color selection instruction with a preset mapping relation to obtain a target display parameter of a target lamp in a target combination result; matching the target display parameter with the current display parameter to obtain a control instruction; and adjusting the target lamp based on the control instruction.

Disclosed is a leg thermotherapy rehabilitation device. Using a first convolutional neural network model with a temporal attention mechanism, the device mines hidden features of images from a patient's leg thermotherapy monitoring video over a predetermined time period to extract characteristic information about the temporal changes in the patient's leg skin color. A multi-scale neighborhood feature extraction module extracts multi-scale neighborhood correlation features of the device's heater power values ​​at different time spans within the predetermined time period. Based on the state change characteristics of the patient's leg thermotherapy, the device adaptively adjusts the heater power, dynamically controlling the thermotherapy temperature in real time based on actual conditions. This intelligently controls the thermotherapy temperature to prevent burns and secondary injuries to the patient.

The present disclosure relates to an image processing model training method, an image processing method, and related equipment. The method inputs a sample image and a sample reference image into a feature extraction network and a color mapping table generation network of an image processing model to be trained, respectively, to obtain a sample feature map and a generated color mapping table. The sample feature map is then input into the light scene recognition network of the model to obtain a light scene recognition result. Based on the light scene recognition result, the generated color mapping table is fused in the light scene dimension to obtain a target color mapping table. Based on the target color mapping table, a color mapping process is performed on the sample image to obtain a target processed image. A target loss is determined based on the target processed image, the sample image, and the sample reference image. The target loss is then used to train the image processing model to be trained to obtain an image processing model. The present disclosure improves image processing effects and can achieve skin color editing with any desired effect, thereby improving image processing effects.

The invention discloses a quantitative detection method for anti-perspective performance of a textile in a complex light environment, and belongs to the technical field of new material detection, a micro gap is arranged between the textile to be detected and a variable-frequency gray scale target to simulate a non-clinging state when clothes are worn and excite Fresnel diffraction and subsurface scattering effects, and the anti-perspective performance of the textile is detected. Complex illumination conditions such as backlight and sidelight are simulated by adjusting the light intensity ratio, and after a target perspective image is acquired by using an image acquisition unit, the modulation transfer function attenuation rate of a to-be-detected textile to the target is calculated, so that the anti-perspective performance of the textile is quantitatively represented; the interference of glare on the surface of the high-gloss fabric is effectively eliminated by introducing an orthogonal polarization modulation technology, and a detection result is highly consistent with the visual perception of human eyes by adopting a specific light source spectrum simulating the skin color of a human body; compared with a traditional light transmittance method, the method has the advantages that the dimension crossing from luminous flux measurement to image information fidelity evaluation is realized, and the accuracy, reliability and practicability of detection are remarkably improved.

A display apparatus includes: a display panel including a plurality of pixels; a data driver which applies data voltages to the pixels; a gate driver which applies gate signals to the pixels; and a driving controller which controls the data driver and the gate driver. The driving controller divides the display panel into a plurality of panel blocks, calculates a skin color inclusion ratio of each of the panel blocks based on input image data, determines at least one face region candidate block among the panel blocks based on the skin color inclusion ratio, determines a face region block of the at least one face region candidate block based on the at least one face region candidate block and face matching data, and performs image quality processing on the face region block.

The primer pair for amplifying eggplant fruit skin color gene is characterized in that the nucleotide sequence of the primer pair is as follows: the forward primer is Wz-1F: 5'-CTTGTCAAAGACTGATTACACGATA-3'; and the reverse primer is Wz-1R: 5'-CCTATCACACCTAATTGTTAAACTG-3'. The detection result is more accurate, stable and reliable.

A new variety of Citrus sp. tree ‘HAYASE’ that has medium tree habit in-between upright and spreading, spindle-shaped leaf blade with acuminate apex and acute base, vestigial wing, fruit with high diameter / height ratio (average 1.31) and medium to heavy weight (average 152.3 g), no neck, a flat distal part, bright orange (JHS 1305) skin color, thin peel thickness, easy to medium peeling, deep orange (JHS 1307) color when fresh, juicy when fresh, high sweetness, very few seeds, early maturity, and low acidity.

The invention belongs to an evaluation signal generation method, and aims to solve the technical problems that non-contact detection or non-invasive detection including an existing video photoelectric volume pulse wave detection technology lacks an effective signal evaluation method and evaluation device, and direct and accurate measurement is difficult to perform by depending on known signals. The invention provides a signal generation method for video photoelectric volume pulse wave detection and evaluation and a related device, which are used for respectively generating known ambient light signals, skin color signals and blood flow signals which are very important for video photoelectric volume pulse wave detection. Photoelectric volume pulse wave characteristic wavelength information is generated from the generated blood flow signals according to photoelectric volume pulse wave signal characteristics; then generating a visible light signal which can be displayed by a color LED display device according to a colored light superposition principle by visible light parts in the obtained ambient light signal, the skin color signal and the blood flow signal; and the characteristic wavelength signal and the visible light display signal are displayed in a time-sharing manner.

The invention discloses an RGB-D image-based thyroid body surface key point detection method and system, and the method comprises the steps: carrying out the image collection through an RGB-D camera, and carrying out the preprocessing of a collected RGB image and a depth image; constructing a key point detection model based on a convolutional neural network; training a key point detection model by using the marked thyroid image data set, and optimizing the model by adjusting parameters; extracting features from the preprocessed RGB image and depth image by using the optimized key point detection model, carrying out feature learning, and fusing the extracted RGB features and depth features; carrying out thyroid body surface key point detection on the fused feature map; according to the method, based on the RGB-D image, through deep learning and feature fusion technologies, high thyroid body surface key point detection is carried out, and the limitation of the prior art in the aspects of illumination interference, skin color similarity and the like is overcome; and the detection accuracy and stability are improved.

The utility model discloses a novel medical ice bag, which relates to the technical field of medical instruments, has the advantages that a user can check whether local skin color has cyanosis or not under the condition that the cold compress ice bag is not detached, and the possibility of local frostbite of an affected part is reduced, and adopts the technical scheme that the novel medical ice bag comprises the cold compress ice bag which is completely attached to the affected part, the cold compress ice bag is provided with a fixing assembly connected with an affected part and further provided with a movable assembly facilitating checking of local skin colors.

A semantic-driven immersive telepresence system for bandwidth-efficient transmission of three-dimensional human representations. The system extracts compact semantic details from captured image data instead of transmitting complete raw 3D content. Color semantics are derived by estimating scene lighting characteristics using a lightweight model, combined with pre-captured base skin and clothing color. Motion semantics are extracted via keypoint detection of body joints, facial features, and hand positions. A regression-based method using keypoint-anchored vertices (KAVs) maps sparse keypoints to parametric human model parameters in real-time. The system transmits compressed semantic information over networks and reconstructs human representations at receiving devices. A multi-stage neural rendering pipeline with patch-based processing generates photo-realistic visual output. The approach significantly reduces bandwidth requirements compared to traditional bit-by-bit transmission of point clouds or meshes while maintaining low latency, real-time performance, and high visual quality suitable for interactive telepresence applications.

The present application belongs to the technical field of plant molecular breeding, and particularly relates to a SNP marker combination for marker-assisted selection of apple fruit surface color degree, a probe set and application thereof. The SNP marker combination is composed of four SNP markers; the four SNP markers include SNP1 to SNP4; the nucleotide sequences of the SNP1 to the SNP4 are shown in SEQ ID NO. 1 to SEQ ID NO. 4 in turn. The present application provides a SNP marker combination for marker-assisted selection of apple fruit surface color degree, which can be applied to marker-assisted selection of apple fruit surface color degree traits at the seedling stage of apple hybrid offspring.

The utility model relates to the technical field related to medical equipment, in particular to a hand restraint device capable of allowing the palm of a patient to move within a small range. Comprising a mounting ring, a protective cover is mounted on one side of the mounting ring, an observation plate is mounted at the upper end of the inner side of the protective cover, a zipper is mounted on the inner side of the observation plate, a protective layer is mounted on the inner side of the protective cover, the protective cover wraps a palm, and the hand is bound under the condition that each finger is not fixed; a large space is arranged in the protective sleeve, so that the palm of the patient can be adjusted in a small range, more space is provided, the hand tightening feeling can be reduced, the discomfort of the patient is reduced, the patient feels more comfortable in a restrained state, anxiety can be relieved, the condition of the palm of the patient can be observed through the observation plate, and the patient can be protected from being damaged. The state of the palm can be directly observed through the observation plate, the state comprises skin color, swelling, wounds or other pathological changes, and medical staff can quickly make adjustment or treatment decisions according to observation results.

Systems and methods related to generating video sequences containing physiologically realistic avatars are provided. In an example, an albedo for an avatar is received, a subsurface skin color associated with the albedo is modified based on physiological data associated with a physiological characteristic, and the avatar is rendered based on the albedo and the modified subsurface skin color. The rendered avatar can then be composited in frames of a video. In some examples, the video containing the composited avatar can be used to train a machine learning model to detect the physiological characteristic. The machine learning model can receive a plurality of video segments, where one or more of the video segments contain a composited physiologically realistic avatar generated with the physiological characteristic. The machine learning model can be trained using the plurality of video segments. The trained model can be provided to a requesting entity.

The invention relates to the technical field of health monitoring, in particular to venous thromboembolism monitoring equipment which comprises an evaluation module used for providing an evaluation scale, collecting filling information of a patient and generating a risk level evaluation result; the behavior guidance module is used for generating corresponding guidance suggestions based on the risk level assessment result, so that the patient can be protected according to the guidance suggestions; the monitoring module is used for collecting monitoring data in the protection process of the patient according to the guidance suggestions, and the monitoring data comprise leg circumference data, pain feeling data, skin color data, body temperature data, limb swelling data and pulmonary embolism expression data; and the early warning module is used for generating early warning information based on the monitoring data, and sending the early warning information to related medical personnel, so that manual monitoring is replaced by the monitoring equipment, the self-protection consciousness of the patient can be autonomously stimulated, the monitoring effect is improved, and the morbidity of venous thromboembolism is reduced.

The invention relates to a high-yield potato planting method which comprises the following steps: step 1, selecting sandy soil in which cereal crops are planted in previous crops for planting, deeply ploughing 7-8 inches in early autumn, and controlling the pH value of the soil to be 5.5-6.5; step 2, selecting potato tubers which are smooth and tender in epidermis, bright in skin color, free of diseases and pests and free of freezing injury as seeds, cutting the potatoes into blocks, stacking the potatoes in a ventilated place, and covering the potatoes with fine sand with the thickness of 5-10cm; according to the method, soil is deeply ploughed during planting, it is ensured that the soil is ploughed to be about 30 cm deep, growth and development of potatoes are facilitated, sufficient nutrient supply is provided, potato seeds are planted through top buds, the emergence rate is increased, good conditions are created for high yield of the potatoes, and the thickness of a soil layer can be increased by adopting a ridging planting means; the method improves the air permeability and heat preservation of the soil, and timely carries out ridging to increase the height and width of ridges, so that larger growth space and nutrient supply can be provided, and the expansion and development of the potatoes are facilitated.

The invention relates to an oximeter adjusting method and device.The method comprises the steps that light intensity data corresponding to a target finger are obtained, a light intensity data set is formed, and the light intensity data are light intensity data which are detected by a light receiving component of an oximeter and penetrate through the target finger; calculating parameter information corresponding to the light intensity data set according to the state information corresponding to the light intensity data set; and adjusting the signal amplification factor of a light receiving component of the oximeter according to the parameter information. According to the oximeter adjusting method and device provided by the invention, the parameter information obtained according to the light intensity data is compared with the preset range, and when the parameter information does not fall into the preset range, the signal amplification factor of the light receiving component of the oximeter is at least adjusted; the influence of human body factors such as skin color and finger thickness and environmental factors on the accuracy of the measurement result of the oximeter is eliminated, and higher measurement accuracy and faster stable value obtaining of the oximeter are achieved.

A new and distinct variety of interspecific tree. The following features of the tree and its fruit are characterized with the tree budded on ‘Nemaguard’ Rootstock (non-patented), grown on Handford sandy loam soil with Storie Index rating 95, in USDA Hardiness Zone 9, near Modesto, California, with standard commercial fruit growing practices, such as pruning, thinning, spraying, irrigation and fertilization. Its novelty consists of the following combination of desirable features:1. Tree having a vigorous, upright growth habit.2. Tree being a regular and productive bearer of medium size fruit.3. Fruit with very good flavor and eating quality.4. Fruit with a high degree of red skin color.5. Clingstone fruit with firm, red flesh.

The utility model relates to the field of medical apparatuses and instruments, and discloses a pressure cold therapy device for treating concurrent forearm swelling after a radial artery intervention operation. The pressure cold therapy device comprises a lifting support used for lifting the forearm of a patient; the air bag cuff is arranged on the lifting support and used for fixing the forearm of the patient; and the connecting piece is used for detachably connecting the lifting support and the air bag cuff. The pressure cold therapy device can be tightly attached to the skin for cold therapy, meanwhile, pressure bandaging and operative limb lifting can be carried out, the pressure can be controlled and adjusted in a personalized mode according to the condition of a patient, the skin color of the swelling part of the patient can be checked through the design of the transparent air bag, a polymer hydrogel cold therapy patch is adopted for cold therapy, and the effect is good. The cold therapy patch can be better attached to the skin surface and is soft in texture, long in cold therapy time and remarkable in effect, frequent replacement of the cold therapy patch is not needed, nursing workload is relieved, patient compliance is improved, and patient rehabilitation is promoted.

1. The name of the design product: annular color chart. 2. The use of the design product: used in the process of filling the medical beauty model, the color comparison between the head model skin color and the standard color. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention discloses a student online learning video analysis type low-consumption identity recognition method and system, and relates to the technical field of data analysis. The method comprises the following steps: constructing a to-be-detected video data set; analyzing the to-be-detected video data set by using a video frame optimization model based on a time sequence behavior mode, and selecting an optimal video frame; determining a face region by using a self-adaptive face detection model based on regional spectrum migration and skin color constancy in combination with the optimized video frame; and performing recognition analysis on the face region by using the three-dimensional face identity recognition model based on hierarchical sparse coding to generate an identity recognition result. The method can effectively improve the recognition precision and reduce the consumption of computing resources.