5 results about "Feature estimation" patented technology

Identify an object in various environments. 【Solution means】Communication terminal ( 2 ) is From the color image data using the first estimation model (30) 、 color image Colors that indicate the amount of facial features included a color feature estimation unit (40) that estimates a feature vector; From depth image data using the second estimation model (31) 、 depth image Depth of facial features included a depth feature estimation unit (41) that estimates a feature vector; From the color feature vector and depth feature vector using the third estimation model (32) 、 a three-dimensional image including a color image and a depth image Three-dimensional representation of facial features included in the image. a three-dimensional feature estimation unit (42) that estimates a feature vector; Color characteristics feature vector, depth degree characteristic feature vector, and three-dimensional Gentoku and an authentication unit (24) that performs face authentication using any of the feature vectors.

The application discloses an infrared optical image fusion method and system based on manifold adaptive decomposition, and belongs to the technical field of image processing and multi-modal information fusion, mainly including the steps of image structure scale feature estimation, adaptive multi-scale decomposition construction, structure perception fusion mechanism design and gradient domain reconstruction. Experimental results show that the method has significant advantages in maintaining structure integrity, enhancing details and textures, and improving fusion contrast, and can obtain high-quality fusion results with clear structure and natural vision in a complex environment.

To provide a feature amount estimation method, an estimation program, and a feature amount estimation apparatus for improving the estimation accuracy of a shape feature amount on a surface of a substrate.SOLUTION: The method for estimating a shape feature amount on a surface of a substrate after applying processing for semiconductor manufacturing, includes: generating a plurality of regression models representing a relation between a target parameter and the shape feature amount according to each of a plurality of pre-processing conditions having a plurality of parameters, based on actually measured data of the shape feature amount on the surface of the substrate that has been pre-processed; calculating, for each of the plurality of regression models, an index value representing the reliability of estimated results of the shape feature amount; evaluating the suitability of a setting method of the plurality of pre-processing conditions; selecting a model to be selected as a selection model from among the plurality of regression models based on calculated results of the index value and evaluated results of the suitability of the setting method; and calculating an estimated value of the shape feature amount corresponding to any value of the target parameter by using the selection model.SELECTED DRAWING: Figure 11

This application discloses a method, system, and storage medium for detecting slowly varying faults in inertial measurement units (IMUs) under dynamic environments. Addressing the technical challenges of difficult identification, high false alarm rates, and missed alarm rates in IMUs, this application introduces a Gaussian process regression model and a particle swarm optimization (PSO) algorithm for multidimensional feature estimation of the IMU. A dynamic envelope model of the normal signal is constructed using the Gaussian process regression model, and the PSO algorithm is used to optimize the multidimensional statistical feature thresholds within the envelope. This achieves high-precision detection of slowly varying faults without the need for fault data, making it suitable for scenarios with small data volumes and high noise levels. Experiments demonstrate that this method can meet the diagnostic needs of various IMU faults, can be applied to different types of IMUs, exhibits better generalization ability, and has higher fault diagnosis accuracy.

A kind of regional scale large water delivery channel leakage feature estimation method, including selecting target large water delivery channel, collecting the spatial structure characteristics of target large water delivery channel, hydraulic characteristics and the spatial geographic data of study area, selecting the typical branch channel of target large water delivery channel to carry out hydrostatic test and measure channel bed leakage coefficient, obtain the channel system network after target large water delivery channel is dispersed, generate channel segment spatial topological relation, obtain the spatial geographic two-dimensional array of study area, generate channel water dynamic calculation input file, according to the spatial geographic two-dimensional array of study area and channel water dynamic calculation input file, construct surface water-groundwater coupling model, calculate surface ditch channel network water dynamic according to finite difference method, channel system leakage amount result statistics, the channel bed leakage coefficient of the present application is measured by the hydrostatic test of typical branch channel in local position and the spatial characteristics of large water delivery channel, to accurately calculate the leakage difference of large channel in different positions and overall leakage characteristics.