Robust model fitting method based on global greedy search

Abstract

The invention discloses a robust model fitting method based on global greedy search. The robust model fitting method specifically comprises the steps that a data set is arranged, and parameters are initialized; a label is used for obtaining an inner point of an [m]th model instance of which the class label is m; according to a sampling method of the global greedy search, model hypotheses theta aregenerated on I<m> and input data x or the model hypotheses theta are generated on data of which the class label is 0 in the label according to a sampling method of an HMSS; a new label is obtained according to the model hypotheses theta and the label; m<c> recently generated model hypotheses are merged together to obtain an [m-tilde]<c> model hypothesis, then the [m-tilde]<c> model hypothesis isused for obtaining the new label; and the m<c> generated model hypotheses are output, and according to outputting of the m<c> generated model hypotheses, an image is segmented to complete the model fitting. The robust model fitting method selects data subsets from the inner points to generate more accurate initial model hypotheses, and can be applied to computer visual tasks such as homography matrix estimation, fundamental matrix estimation, two-view plane segmentation and motion segmentation.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a robust model fitting method based on global greedy search. Background technique [0002] Robust model fitting is an important basic research task in computer vision, and it has been widely used in many practical applications, such as: feature matching, image registration, visual tracking, indoor navigation and motion segmentation and other fields. The goal of robust model fitting is to estimate the parameters of the model of interest from the input data. Specifically, given a geometric model (eg, line or fundamental matrix), the parameters of the model instances in the data are estimated by model fitting methods. [0003] In recent decades, a large number of model fitting methods have been proposed. For example, RANSAC [1] is a popular method for model fitting due to its simplicity and ease of implementation. However, the random sampling strategy used in RANSAC is not efficient ...

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Problems solved by technology

[0005] However, several authors (such as [3]) have found that the "fit-and-remove" process has the following flaw: if the model parameters of one model instance are estimated incorrectly, the model parameters of the remaining model instances in the data will be miscalculation

Before performing model fitting, they need to sample a pre-specified number of data subsets, and how to effectively determine the sampling number of pre-specified data subsets is quite difficult

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