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A Robust Model Fitting Method Based on Global Greedy Search

A model fitting and model technology, which is applied in the field of robust model fitting based on global greedy search, and can solve the problems of model parameter estimation, model parameter estimation error, difficulty, etc.

Active Publication Date: 2022-02-01
FUJIAN AGRI & FORESTRY UNIV
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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

Method used

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  • A Robust Model Fitting Method Based on Global Greedy Search
  • A Robust Model Fitting Method Based on Global Greedy Search
  • A Robust Model Fitting Method Based on Global Greedy Search

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Embodiment 1

[0062] Please refer to figure 1 , the present invention provides a kind of robust model fitting method based on global greedy search, comprising the following steps:

[0063] S1. Given input data containing N data χ={x 1 ,x 2 ,...,x N}, where N is a natural number. Specify the minimum number of data points k and the number of model instances m that a model instance should contain c .

[0064] S2. Perform initialization: specify the maximum number of iterations t max =10, the number of data points contained in the data subset h=p+2, the current number of iterations t=0, the number of currently generated model instances m=0 and the class label=0.

[0065] S3. If the current number of iterations t is less than t max , then execute step D; otherwise, end the program and output the generated m c a model assumption.

[0066] S4. If the number of currently generated model instances m is less than m c , then execute steps S5 to S9; otherwise, execute steps S10 and S11.

[0...

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Abstract

The invention discloses a robust model fitting method based on global greedy search, specifically: setting a data set and initializing parameters; using a label to obtain the interior point of the mth model instance whose class is m; according to the global greedy search The sampling method generates a model hypothesis on the input data or generates a model hypothesis on the data with a class mark of 0 in the label according to the HMSS sampling method; obtains a new label according to the model hypothesis and label; fuses the most recently generated model hypothesis to obtain Model assumptions, and then use the model assumptions to obtain a new label; output the generated model assumptions, and segment the image according to the output generated model assumptions to complete the model fitting. The invention selects data subsets from interior points to generate more accurate initial model assumptions, and can be applied to computer vision 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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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/231
CPCG06T7/231G06T2207/10004
Inventor 赖桃桃陈日清杨长才
Owner FUJIAN AGRI & FORESTRY UNIV
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