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Updated: Jun 17, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Simultaneous object classification and segmentation with high-order multiple shape models.

Federico Lecumberry¹, Alvaro Pardo, Guillermo Sapiro

¹Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay. fefo@fing.edu.uy

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 24, 2009

Summary

This summary is machine-generated.

This study introduces a joint framework for classifying and segmenting objects using shape models (SMs). It automatically selects the best SM and accurately segments images, even with occlusions.

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Area of Science:

Computer Vision
Machine Learning
Image Analysis

Background:

Shape models (SMs) capture common features for object representation.
Existing methods require separate classification and segmentation steps.

Purpose of the Study:

To develop a joint classification-segmentation framework for object recognition.
To automatically select the most representative shape model for an incoming object.
To accurately segment images using both image data and selected model features.

Main Methods:

Introduced a novel energy functional for simultaneous classification and segmentation.
Implemented online model selection based on shape similarity during minimization.
Utilized high-order shape models for handling similar classes and variability.
Incorporated position and transformation invariance into the modeling.

Main Results:

Demonstrated successful simultaneous classification and segmentation of complex shapes.
Showcased adaptability to similar object classes and intra-class variations.
Validated performance in images with significant occlusions.
Achieved accurate model selection and image segmentation iteratively.

Conclusions:

The proposed framework effectively integrates classification and segmentation.
Online model selection enhances adaptability and accuracy.
High-order SMs and invariance properties improve robustness for challenging datasets.