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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

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Shape Matching and Registration by Data-driven EM.

Zhuowen Tu¹, Songfeng Zheng², Alan Yuille³

¹Lab of Neuro Imaging (LONI), Department of Neurology, UCLA, 635 Charles E. Young Drive South, Los Angeles, CA 90095, USA.

Computer Vision and Image Understanding : CVIU

|December 23, 2017

Summary

This summary is machine-generated.

This study introduces a fast hybrid algorithm for shape matching and registration. It combines generative and discriminative models for efficient geometric transformation and detection, achieving results in under a second.

Keywords:

EM registration shape context shape matching soft assign

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Area of Science:

Computer Vision
Geometric Algorithms

Background:

Shape matching, registration, and detection are crucial in computer vision.
Existing generative models (e.g., EM algorithm) face initialization and E-step computation challenges.
Sparse-point and continuous-contour representations are common for shape data.

Purpose of the Study:

To develop an efficient and robust algorithm for shape matching, registration, and detection.
To overcome limitations of traditional generative models by incorporating shape features.
To achieve rapid convergence and high accuracy in geometric transformations.

Main Methods:

Formulated shape matching as probabilistic inference using a generative model and EM algorithm.
Developed a discriminative model leveraging shape features to address generative model limitations.
Created a hybrid algorithm combining both generative and discriminative approaches.
Utilized sparse-point or continuous-contour shape representations.

Main Results:

The hybrid algorithm demonstrates high efficiency and robustness in shape matching and registration.
Shape features significantly improve correspondence solving, requiring typically only four iterations.
Algorithm convergence time is consistently under one second.
Successful validation on standard datasets like MPEG7 and diverse segmentation tasks.

Conclusions:

The hybrid generative-discriminative algorithm offers a significant advancement in shape analysis.
The method provides a fast and accurate solution for geometric transformation tasks.
This approach is effective for various applications including shape matching, registration, and segmentation.