44 results about "Local regression" patented technology

This disclosure provides methods, apparatus and systems for performing image processing regression for approximating multidimensional color transformation. According to an exemplary method, a shaping matrix is selected to minimize a cost function associated with a local linear regression representation of the color transformation. In addition, an alternating least squares algorithm is utilized to jointly optimize regression and shaping parameters.

This disclosure provides methods, apparatus and systems for performing image processing regression for approximating multidimensional color transformation. According to an exemplary method, a shaping matrix is selected to minimize a cost function associated with a local linear regression representation of the color transformation.

The invention relates to a color constancy method based on a texture pyramid and regularized local regression, belonging to the technical field of image processing. The color constancy method based on a texture pyramid and regularized local regression comprises the following steps: establishing an algorithm candidate set for the fusion algorithm according to different parameter selections through a Minkowski paradigm based on common color constancy, segmenting an image into overlapped blocks according to different resolution ratios, describing the texture features of each block by adopting Weibull distribution parameters, selecting K images of which the texture features are similar to the texture features of the given images to be measured from a training database according to the defined image similarity norm, estimating the illumination of the images to be measured by adopting the regularized local regression according to the K reference images in the fusion stage, and finally mapping the images to be measured to the standard white light by using a Von Kries model according to the illumination estimation value to obtain the corrected images. The invention effectively improves the illumination estimation accuracy and can be widely applied to target tracking, object recognition and image retrieval, and the like.

The disclosure provides an approach for denoising (also referred to as “filtering”) rendered images. In one embodiment, a denoising application takes as input rendered images and feature buffers that encode image information such as surface positions, surface depths, surface normals, surface albedos, and distances to the camera. For each pixel in a received image, the denoising application performs a first-order regression in a predefined neighborhood of the pixel to find a linear combination of pixel features that fits pixel colors in the predefined neighborhood. In such a first-order regression, the local regression weight of each pixel in the neighborhood may be determined using a metric which computes distances based on color values in patches around pixels being compared. In another embodiment, collaborative filtering may be performed in which filtered output from the first-order regression in each neighborhood is averaged with filtered output from overlapping neighborhoods to obtain a final output.

The present invention provides an intelligent evaluation device for hemodialysis dry weight, which is used for evaluating the dry weight required by hemodialysis patients for hemodialysis. The linear model of the relationship between the point and the weight after differential dialysis, and based on the least square method to solve the optimal parameters of the linear model to form the overall trend model of the individual body weight of the hemodialysis patient; the local regression model construction department, based on the Gaussian kernel function method to obtain weight data set and perform local regression correction on the overall trend model of body weight to form a local weight regression model; the dry weight prediction department will input the current hemodialysis time point into the overall weight trend model and local regression model, and combine the body weight after dialysis The index average is used to obtain the predicted dry weight; and the dry weight adjustment unit adjusts the predicted dry weight according to the correction factor corresponding to the patient characteristics of the patient to obtain the dry weight of the patient to be hemodialysis.