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Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Discriminatively Trained And-Or Graph Models for Object Shape Detection.

Liang Lin, Xiaolong Wang, Wei Yang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This summary is machine-generated.

We introduce a novel And-Or graph model for robust object shape recognition. This reconfigurable model effectively detects shapes even with background clutter, outperforming existing methods.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Object shape recognition is crucial for image understanding.
Existing methods struggle with variations in pose, view, and background clutter.
Part-based models offer flexibility but can be complex to train.

Purpose of the Study:

To develop a novel, reconfigurable part-based model for robust object shape recognition.
To introduce a structural optimization algorithm for training the model from weakly annotated data.
To establish a new benchmark dataset for shape recognition research.

Main Methods:

Proposed a four-layer And-Or graph model with reconfigurable or-nodes and deformation-capturing and-nodes.
Developed a structural optimization algorithm for iterative model training and parameter learning.
Utilized weakly annotated data for discriminative training.
Created and released a new shape database with over 1500 annotated instances.

Main Results:

The And-Or graph model achieved robust shape-based object detection.
The model demonstrated superior performance against background clutter compared to state-of-the-art approaches.
The proposed structural optimization algorithm effectively trained the model from limited annotations.

Conclusions:

The And-Or graph model provides an effective and reconfigurable approach for object shape recognition.
The developed training algorithm enables robust model learning from weakly supervised data.
The new shape database facilitates further research in challenging shape recognition tasks.