Processing tree point clouds using Gaussian Mixture Models
نویسندگان
چکیده
منابع مشابه
Processing Tree Point Clouds Using Gaussian Mixture Models
While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications. In addition to the issues typically associated with processing point data, such applications raise a number of new complications, such as the complexity of the scenes scanned, along with the sheer volume of data. Consequently, automated pro...
متن کاملBi-Stage Large Point Set Registration Using Gaussian Mixture Models
Point set registration is to determine correspondences between two different point sets, then recover the spatial transformation between them. Many current methods, become extremely slow as the cardinality of the point set increases; making them impractical for large point sets. In this paper, we propose a bi-stage method called bi-GMMTPS, based on Gaussian Mixture Models and Thin-Plate Splines...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملIMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملSpeaker Identification Using Gaussian Mixture Models
In this paper, the performance of Perceptual Linear Prediction (PLP) features has been compared with the performance of Linear Prediction Coefficient (LPC) features for speaker identification. Two classification techniques, Gaussian Mixture Models (GMM) and Vector Quantization (VQ) with Dynamic time wrapping (DTW) are used for classification of speakers based on their speech samples into respec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2013
ISSN: 2194-9050
DOI: 10.5194/isprsannals-ii-5-w2-43-2013