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Updated: Jun 10, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Circular blurred shape model for multiclass symbol recognition.

Sergio Escalera¹, Alicia Fornés, Oriol Pujol

¹Centre de Visió per Computador, Campus Universitat Autònoma de Barcelona, Edifici O, 08193 Barcelona, Spain. sescalera@cvc.uab.es

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|August 24, 2010

Summary

This summary is machine-generated.

We introduce a novel circular blurred shape model descriptor for robust symbol detection and classification. This descriptor effectively handles irregular deformations and rotation, outperforming existing methods on symbol datasets.

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Area of Science:

Computer Vision
Pattern Recognition
Image Analysis

Background:

Symbol detection and classification are crucial for object recognition.
Existing descriptors struggle with irregular deformations and rotation.

Purpose of the Study:

To propose a new descriptor for robust symbol detection and classification.
To enhance tolerance to deformations and achieve rotation invariance.

Main Methods:

Feature extraction using a correlogram structure capturing spatial object characteristics.
A circular blurred shape model descriptor with adjustable blurring for deformation tolerance.
Rotation invariance by design.
Validation using cascade classifiers for detection and error-correcting output codes for classification.

Main Results:

Significant improvements over state-of-the-art descriptors on four symbol datasets.
Superior performance in cases with elastic symbol deformations.
Effective in both multiclass symbol recognition and symbol detection.

Conclusions:

The proposed circular blurred shape model descriptor offers superior performance for symbol recognition tasks.
The descriptor's tolerance to deformations and inherent rotation invariance make it highly effective.