Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

This paper addresses an image matching methodology designed for correspondence problem in computer vision. Firstly, a novel superpixel segmentation model driven by spatially constrained Student's-t mixture model (SMM) is proposed. The tails of Student's t-distribution are heavier than that...

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Bibliographic Details
Main Authors: Pengyu Wang, Hongqing Zhu, Xiaofeng Ling
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Superpixel segmentation
spatially constrained Student’s-<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">t</italic> mixture model
key-superpixel detection
superpixel descriptor
superpixel matching
Online Access:https://ieeexplore.ieee.org/document/8993810/

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