27 results about "Age estimation" patented technology

The application provides a human body image age estimation method, system, medium and device, comprising: acquiring a human body image and a corresponding age label; training a human body image age estimation model based on the human body image and the age label, so as to estimate the age of the human body image based on the trained human body image age estimation model. The application uses a convolutional neural network as a backbone model to extract human body image age-related features, and adds a design scheme of a plurality of age stage classifier network structures that overlap with each other at the end of the backbone model; the learning of the model on different age difference characteristics is strengthened, the correlation between different ages is related through the mutual overlap between the age stages of each classifier, and the accuracy of the human body image age estimation model is effectively improved.

The invention relates to the technical field of deep learning and multi-modal feature fusion, and provides an age estimation method and device based on multi-modal feature fusion. Extracting an image feature vector of the face image by using an image encoder in the CLIP model, and extracting a text feature vector by using a text encoder in the CLIP model; splicing the image feature vector and the text feature vector to generate a fusion feature vector; inputting the fusion feature vector into a regression module for regression processing; and using the loss function to update trainable parameters of an image encoder and a text encoder in the attention fusion module, the regression module and the CLIP model. According to the method, multi-modal sample data is acquired, image features and gender text features are extracted by using a CLIP model, and a fusion feature vector is generated based on a weighted fusion mechanism, so that age information expression is realized; and the fusion features are input and predicted by the regression module, so that the age estimation accuracy is improved, and meanwhile, the method is also applied to various face recognition scenes.

The application discloses a kind of family audio-video imprint automatic aggregation and personal biography film generation method and system based on genealogy figure. First, structured family knowledge base including family member node, blood relationship edge and associated multimedia data is constructed. In response to biography generation request, start multi-modal figure imprint aggregation: fusion face recognition and blood relationship auxiliary matching, automatically identify and extract all image fragments of target figure appearance from audio-video library, form cross-modal material set. Further, blood narrative guided intelligent editing is carried out: based on age estimation, material time sequence segmentation is carried out, narrative structure is automatically generated in combination with blood event node, and material enhancement technology is used to output into film. The system automatically generates the "family imprint" column in personal data card, and aggregates and displays related audio-video materials. Through integrated path, scattered family memories are gathered into personal image biography, which greatly reduces the production threshold, realizes the automatic aggregation and generation of family memories.

The application discloses a face image age estimation method based on a global-to-local ordinal regression network. The application comprises the following steps: step 1, preprocessing a data set; step 2, extracting features of face images in the preprocessed data set; step 3, local age domain division; performing k-means clustering analysis on the extracted feature data of the entire data set, and dividing the entire data set into multiple local data domains according to the clustering result; step 4, constructing a global regression network and a local regression network; and step 5, age estimation. The application solves the problem of insufficient precision of an existing deep ordinal regression network in a face age estimation task. Without increasing the complexity of network design, the application effectively improves the accuracy of the ordinal regression network for the face age estimation task. The application has the advantages of high recognition accuracy and strong robustness.

Relating to the estimation of the development month age of a child, the present invention obtains an estimation result with high objectivity regarding the development month age while increasing the degree of freedom of inspection items. [Solution] A development month age estimation device 3 comprises: a processor 15 for executing processing for estimating the development month age of a child which is the subject of inspection on the basis of inspection results for a plurality of inspection items; and a storage device 14 for storing data on a continuous probability distribution indicating a passage rate where month ages in a predetermined range are taken as a variable, for each of the plurality of inspection items. The processor 15 generates a development month age estimation function on the basis of data on a continuous probability distribution for two or more implementation inspection items used for inspection among the plurality of inspection items and an inspection result for each of the implementation inspection items, estimates the development month age of a child which is the subject of inspection on the basis of the mode value of a distribution of passage rates obtained by the development month age estimation function, and outputs an estimation result of the development month age.

Systems and methods for generating an age determination model based on a test data set of a test instrument. A control system of the computer-aided system is configured to use an age determination model to provide a change in instrument age of an operating instrument under its control. The control system may provide guidance to the user based on an age of the operating instrument and may perform one or more remedial actions when an updated instrument age of the operating instrument exceeds an age threshold.

The present application relates to the technical field of geological disaster assessment, and provides a fault activity age estimation method, which comprises the following steps: obtaining the depth domain stratum data, the faulted state data and the dating sample data of a target fault, generating a stratum burial depth sequence and a depth-geological era corresponding relationship; determining an active burial depth constraint interval according to the stratum burial depth sequence and the faulted state data; calculating an active response parameter based on the sedimentary difference of the two plates of the target fault, dividing an active period and determining a period burial depth interval; converting the dating age into an age mapping burial depth interval, and matching the age mapping burial depth interval with the active burial depth constraint interval or the period burial depth interval to obtain an effective age anchor point; and finally generating a fault activity age genealogy chart. The present application takes the stratum burial depth sequence as a main control framework and takes the experimental dating data as evidence, so that the dependence on a single dating object can be reduced, and the misjudgment caused by the multi-solution of the dating data can be reduced.

The application discloses a local face age estimation method based on multi-channel learning in a shielding scene and relates to the technical field of age estimation. The application uses a method of simulating shielding, generates partially shielded face images and corresponding shielding labels based on a face age image dataset, trains a shielding detector to detect grids with shielding, proposes an age feature extraction model based on multi-channel learning, divides a to-be-detected image into grids, inputs the to-be-detected image into a feature extraction module, and extracts features of the grids. In combination with the shielding detector result, a feature fusion module is used to fuse features of the grids that are not damaged by shielding, obtain final age features, and further obtain an estimated age value. The application can accurately estimate the face age under a local shielding condition, meets the robustness and accuracy requirements of a face age estimation task in a local shielding scene, and solves the problem that the age estimation is inaccurate due to local shielding in the prior art.

The invention discloses an age estimation method based on a diffusion model, and relates to the technical field of face recognition. Comprising the following steps: constructing a face age estimation model, and inputting a data set into the face age estimation model; performing rearrangement marking and noise removal through a pairing comparison module to obtain a face age image; extracting features of the face age image through a trunk feature extractor, and decoupling the extracted features into additional variables; on the basis of the non-age features, Gaussian noise is gradually added to the face age image in multiple iterations through the forward process of a diffusion model, and a target image with noise is obtained; synthesizing the target image with noise and the age feature through a reverse process of a diffusion model to obtain an age corresponding to the face age image; and inputting a to-be-detected face image into the trained face age estimation model to obtain an age corresponding to the to-be-detected face image. The method can effectively separate and recombine age-related and irrelevant features, and improves the accuracy of face age estimation.

The invention discloses an age estimation network and method based on an elastic law and a non-uniformly distributed X-ray human skeleton image, and mainly solves the problem of insufficient optimization effect on a sample distribution imbalance task in medical image age estimation in the prior art. The network construction comprises the following steps: establishing a feature extractor for extracting batch features from an input image; introducing a feature queue, and using supervision force, distance elasticity and midpoint elasticity to do work on the samples so as to carry out comparative learning between the samples; defining the basic age estimation loss by using the supervision force, and obtaining the total loss of network iteration training; establishing a monotonicity constraint residual block for mapping the sample pair feature distance into a partial monotone increasing distance feature between samples; and establishing mutually independent age estimation regression heads for outputting an estimation result. According to the method, the performance of the automatic age estimation model on few sample data is improved, and the method has a good aging change characterization effect and can be used for disease risk assessment, individualized health management and medical research.

Systems, methods, and computer-readable storage media for age estimation / classification, and more specifically to estimating / classifying the ages of people appearing within videos. Systems configured as disclosed herein can receive a video, then identify multiple facial images for each individual captured within the video. The system can create embeddings of the facial images, then cluster those images together based on distances between the corresponding embeddings. The system can also execute a matchability algorithm on those facial images, determining which of the images provides the clearest image of the individual(s), and can then estimate the age of the individual(s) using the best matchability images and / or send the best matchability image for each individual to a third party for analysis.

The invention discloses a core rot tree age estimation method based on a tree growth model and a probability theory thought, and the method is characterized in that the method comprises the following steps: (1) building a regional homologous growth model: building a regional homologous tree species growth equation cluster based on a plant morphological occurrence consistency principle; (2) radial growth data probability characterization: aiming at core rot sample wood, converting an accumulated radial growth amount sequence of a measurable annual ring section into a parameterized data set; and (3) Bayesian inversion and core rot tree age estimation: calculating a final age estimation value by a convergence parameter set based on parameter prior distribution constructed by a regional prior database. The method has the following characteristics: (1) spatial heterogeneity internalization: the influence of site conditions is automatically integrated, and a tedious factor separation step in a traditional method is avoided; and (2) the method breaks through the technical barrier of chronology reconstruction of the core-rot xylem by deeply coupling the growth model and the probability thought, and solves the problem of core-rot tree age estimation.

An object of a system according to an embodiment is to accurately estimate the age of a pet and obtain information for providing appropriate care.SOLUTION: A system includes a photograph analysis unit, a question analysis unit, and an age estimation unit. The photograph analysis unit analyzes the photograph of the pet. The question analysis unit analyzes an answer to a simple question. The age estimation part integrates the analysis results of the photograph analysis part and the question analysis part to estimate the age of the pet.SELECTED DRAWING: Figure 1

The invention relates to an age estimation method based on methylation chip sequencing, in particular to a DNA methylation level data set, a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. By screening the Beta value of the methylation site related to the time sequence age and / or the biological age, the time sequence age and / or the biological age of the testee can be accurately predicted by utilizing an elastic network machine learning model.

A system, method, and computer-readable storage medium for age estimation / classification, more specifically for estimating / classifying the age of people visible in a video. The system configured to be disclosed herein can receive a video and then identify multiple facial images for each individual captured in the video. The system can create embeddings of the facial images and then cluster those images together based on the distance between the corresponding embeddings. The system can also run a resemblance algorithm on those facial images to determine which of the images provides the clearest image of the individual, then estimate the individual's age using the best-resemblance image, and / or transmit the best-resemblance image for each individual to a third party for analysis.

The invention discloses a lung organ age prediction method based on relation learning, and relates to the technical field of computers, and the method comprises the steps: S1, constructing an age prediction model; s2, acquiring a training data set; s3, training and optimizing the age prediction model to obtain an optimized age prediction model; s4, performing prediction by using the optimized age prediction model to obtain age relation estimation of each image; s5, analyzing the age estimation of each image; and S6, analyzing the age of the lung organ. The age prediction model can capture the overall aging characteristics of organs, then age relation prediction is carried out, and the biological age of the lung CT images in the test set is inferred according to the age relation prediction results of the multiple lung CT images; based on a 3D sample age inference strategy of a reference sample and an optimization method, through ablation experiments with other strategies and contrast experiments with other model frameworks, the superiority of the method in multiple evaluation indexes is proved, and the method can effectively improve the accuracy of lung age prediction.

Systems, methods, and computer-readable storage media for age estimation / classification, and more specifically to estimating / classifying the ages of people appearing within videos. Systems configured as disclosed herein can receive a video, then identify multiple facial images for each individual captured within the video. The system can create embeddings of the facial images, then cluster those images together based on distances between the corresponding embeddings. The system can also execute a matchability algorithm on those facial images, determining which of the images provides the clearest image of the individual(s), and can then estimate the age of the individual(s) using the best matchability images and / or send the best matchability image for each individual to a third party for analysis.

The present application belongs to the field of pattern recognition, and particularly relates to a face age estimation method and system fusing gender and race information, comprising: establishing a face age prediction model fusing gender and race information, including feature mapping and stacking a RepPsconv module into a convolutional neural network, the feature mapping including a main module and an auxiliary module, and the specific steps are: obtaining a face image set with labels of age, gender, race and the like for preprocessing; inputting the preprocessed face image into the convolutional neural network to extract features and fuse them; connecting the fused features to the main module and the auxiliary module, and optimizing the result of the main module by the auxiliary module; solving the loss of the output result of the main module and the label information of the face image, and iteratively training until convergence; inputting a face image to be measured into the trained model, and outputting an age estimation result, and the present application proposes an efficient RepPSconv module, fuses gender and race information, and improves the precision of face age estimation.

Systems, methods, and computer-readable storage media for age estimation / classification, and more specifically to estimating / classifying the ages of people appearing within videos. Systems configured as disclosed herein can receive a video, then identify multiple facial images for each individual captured within the video. The system can create embeddings of the facial images, then cluster those images together based on distances between the corresponding embeddings. The system can also execute a matchability algorithm on those facial images, determining which of the images provides the clearest image of the individual(s), and can then estimate the age of the individual(s) using the best matchability images and / or send the best matchability image for each individual to a third party for analysis.

The invention relates to an auxiliary attribute-guided multi-scale feature fusion face age estimation method, and aims to solve the problems that fine-grained aging features are insufficient to capture, global and local single-scale features are difficult to balance, and noise is easy to introduce by an auxiliary attribute fixed weight in an existing method. The method comprises the following steps: preprocessing a face data set containing age labels, genders and crowd attribute labels, extracting multi-scale features through ResNet18, enhancing the features through deformable convolution and an attention mechanism, and integrating multi-scale information by adopting a progressive fusion strategy; sub-age estimation branches corresponding to attribute combinations are constructed, branch weights are dynamically calculated in combination with an adaptive weight module, and the model is optimized through a joint loss function. According to the method, the accuracy and the cross-domain robustness of age estimation are effectively improved, the influence of redundant features and data deviation is reduced, and the method is adaptive to multiple scenes such as medical diagnosis, public safety and intelligent service and has good practicability and economic benefits.

The invention belongs to the technical field of unattended monitoring of wild animals, and particularly discloses a wild animal body size and age group estimation method and system, and the method comprises the steps: synchronously collecting a visible light image and a thermal infrared temperature map of a wild animal; performing instance segmentation on the visible light image to obtain a binary mask, and extracting a pixel-level body size parameter; extracting a temperature distribution vector from the thermal infrared temperature map; determining an optimal scale factor based on the binary mask, the pixel-level body size parameter and the temperature distribution vector; and correcting the pixel-level body size parameter by using the optimal scale factor, fusing the corrected pixel-level body size parameter and the temperature distribution vector, inputting the fused and corrected pixel-level body size parameter and the temperature distribution vector into a double-branch lightweight regression network, and synchronously outputting a volume agent value, a weight estimation value and an age group classification result of the wild animal. The problem that an existing wild animal monitoring technology cannot meet the requirement for long-term, safe and all-weather monitoring of rare wild animals in the aspects of scene adaptability, functional integrity and equipment deployment difficulty is solved.

The present application relates to a training and estimation method, apparatus, device, and storage medium for an age estimation model; wherein, the training method for the age estimation model comprises: obtaining a training set; wherein, the training set comprises: multiple facial images, and each facial image is annotated with an age label and a gender label; inputting each facial image into a preset neural network model to obtain facial image features and age estimation results corresponding to each facial image; obtaining gender output results corresponding to each facial image feature; based on the age estimation results, gender output results, age labels, and gender labels corresponding to each facial image, performing adversarial training between age estimation accuracy and gender prediction accuracy on the preset neural network model to obtain the age estimation model. The present application is used to solve the problem of poor robustness of the existing age estimation model.

We estimate useful information other than the presence or absence of lesions from dental panoramic X-ray images. [Solution] The AI ​​program or AI estimation method causes a computer 110 included in an AI system 100 to execute an image input module 11 that receives at least one dental panoramic X-ray image, a feature extraction module 12 that extracts at least one feature related to the jawbone, including the alveolar bone, from the at least one dental panoramic X-ray image using CNN and outputs at least one numerical value of the at least one feature, and an age estimation module 13 that estimates at least one biological age from the at least one numerical value using regression models 14 and 15 based on DNN.

Disclosed are systems and methods including computing-processes executing machine-learning architectures implementing label distribution loss functions to improve age estimation performance and generalization. The machine-learning architecture includes a front-end neural network architecture defining a speaker embedding extraction engine of the machine-learning architecture, and a backend neural network architecture defining an age estimation engine of the machine-learning architecture. The embedding extractor is trained to extract low-level acoustic features of a speaker's speech, such as mel-frequency cepstral coefficients (MFCCs), from audio signals, and then extract a feature vector or speaker embedding vector that mathematically represents the low-level features of the speaker. The age estimator is trained to generate an estimated age for the speaker and a Gaussian probability distribution around the estimated age, by applying the various types of layers of the age estimator on the speaker embedding.

The invention provides a computer image-based horse fetal age measuring and calculating system, which comprises two groups of detection frames fixed on the ground, sliding rails are fixed at the front ends and the rear ends of the two groups of detection frames, and two vertical plates are symmetrically mounted on the surfaces of the sliding rails in a sliding manner; the wrapping assembly comprises a vertical frame, the vertical frame is fixed to the outer side of the middle position of the detection frame, two screw plates are slidably installed in the vertical frame, an upper bandage is installed on the surface of the screw plate at the upper end of the vertical frame, and a lower bandage is installed on the surface of the screw plate at the lower end of the vertical frame; the blocking frame comprises a telescopic frame, the blocking frame is rotationally opened, the horse enters the detection frame, the blocking frame blocks the inlet end and the outlet end of the detection frame, the body of the horse is wrapped and limited through the wrapping assembly, the ultrasonic detection assembly penetrates into the body of the horse, the detection result is reflected to the display end, and images are collected. And the collection convenience is improved.

The invention discloses a method for evaluating lumbar vertebra aging based on a lumbar vertebra age estimation network of slice contrast learning, mainly solving the problem that the prior art cannot meet individualized and continuous quantitative early warning of precision medical treatment, and the scheme comprises the following steps: obtaining a lumbar vertebra MRI image and dividing the image into a training set and a test set; training and testing an existing segmentation network to obtain a segmentation result of the lumbar anatomical structure, and performing grouping cutting on the segmentation result according to lumbar segments to obtain a lumbar anatomical structure image data set; dividing the cut images of the first lumbar vertebra, the second lumbar vertebra and the third lumbar vertebra into a training set and a test set for lumbar vertebra age estimation; constructing a lumbar vertebra age estimation network of slice contrast learning, and training the lumbar vertebra age estimation network by using a lumbar vertebra age estimation training set; inputting the lumbar vertebra age estimation test set into the trained lumbar vertebra age estimation model, and outputting a predicted age result and a lumbar vertebra age estimation significance heat map; and according to an age prediction result, evaluating a degenerative aging risk result of different anatomical regions of the lumbar vertebra. According to the method, automatic segmentation of the lumbar intervertebral disc anatomical structure and estimation of the lumbar vertebra age can be realized, and the method can be used for lumbar vertebra age and senescence risk assessment and early risk early warning.