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3 results about "Active shape model" patented technology
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Active shape models (ASMs) are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image, developed by Tim Cootes and Chris Taylor in 1995. The shapes are constrained by the PDM (point distribution model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model). The ASM algorithm aims to match the model to a new image.
Intelligent road surface friction performance evaluation method and system
The invention discloses an intelligent pavement friction performance evaluation method and system. The method comprises the steps of multi-source data acquisition, pavement image depth feature extraction, vehicle-road interaction friction performance evaluation and pavement friction performance dynamic evaluation. The invention relates to the technical field of data processing, in particular to an intelligent road surface friction performance evaluation method and system.According to the scheme, advanced vehicle-road interaction friction performance evaluation is innovatively proposed, then a vehicle-road interaction result and environmental data are combined for analysis, and accurate and comprehensive road surface friction performance evaluation is achieved; an active shape model is adopted to extract road surface macrostructure features, and an improved local binary texture method is adopted to extract road surface microstructure features, so that the accuracy of a vehicle-road interaction friction performance evaluation result is improved; a probability sparse self-attention mechanism, a distillation mechanism encoder and a one-time generation decoder are introduced to carry out an improved Transform algorithm, so that the calculation efficiency is improved, and the accuracy of a model output result is improved.
Owner:SHANGHAI NOLAI TECH DEV CO LTD
Methods of automatic segmentation of anatomy in artifact affected CT images with deep neural networks and applications of same
Methods and systems for segmentation of structures of interest (SOI) in a CT image post-operatively acquired with an implant user in a region of interest in which an implant is implanted. The method includes inputting the post-operatively acquired CT (Post-CT) image to trained networks to generate a dense deformation field (DDF) from the input Post-CT image to an atlas image; and warping a segmentation mesh of the SOI in the atlas image to the input Post-CT image using the DDF so as to generate the segmentation mesh of the SOI in the input Post-CT image, wherein the segmentation mesh of the SOI in the atlas image is generated by applying an active shape model-based method to the atlas image.
Owner:VANDERBILT UNIV
Intelligent pavement friction performance evaluation method and system
ActiveCN121579995Bimprove accuracyImprove practicalityBiological modelsFeature extractionData acquisition
The application discloses a kind of intelligent pavement friction performance evaluation method and system, method includes multi-source data acquisition, pavement image depth feature extraction, vehicle-road interaction friction performance evaluation and pavement friction performance dynamic evaluation.The application relates to the field of data processing, specifically refers to a kind of intelligent pavement friction performance evaluation method and system, the scheme of the application innovatively proposes to carry out vehicle-road interaction friction performance evaluation first, then the vehicle-road interaction result is combined with environmental data to analyze, accurate and comprehensive pavement friction performance evaluation is realized;Active shape model is used to extract pavement macrostructure features, and improved local binary texture method is used to extract pavement microstructure features, to improve the accuracy of vehicle-road interaction friction performance evaluation results;The calculation efficiency is improved, and the accuracy of the model output result is improved by introducing probability sparse self-attention mechanism, distillation mechanism encoder and one-time generation decoder to improve the Transform algorithm.
Owner:SHANGHAI NOLAI TECH DEV CO LTD