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Updated: Jul 31, 2026
12:51
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Published on: December 10, 2013
Combining color and shape information for illumination-viewpoint invariant object recognition.
Aristeidis Diplaros1, Theo Gevers, Ioannis Patras
1Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands. diplaros@science.uva.nl
Summary
This study introduces a novel object recognition scheme combining color and shape invariants for robust performance. The method achieves accurate recognition in complex scenes, even with varying illumination and viewpoints.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Object recognition systems struggle with variations in illumination, viewpoint, and object pose.
- Color and shape information are crucial for object identification but are susceptible to imaging conditions.
Purpose of the Study:
- To develop a robust object recognition scheme by merging color- and shape-invariant features.
- To enhance recognition accuracy and speed under challenging environmental and imaging conditions.
Main Methods:
- Computed color-invariant derivatives to handle photometric changes.
- Derived similarity-invariant shape descriptors to address perspective projections.
- Combined color and shape invariants into a multidimensional color-shape context for indexing.
Main Results:
- The proposed indexing scheme provides high-discriminative information robust against varying imaging conditions.
- The color-shape context enables fast recognition, even with object occlusion and clutter.
- Experimental results demonstrate high accuracy in recognizing rigid objects in complex 3-D scenes.
Conclusions:
- The merged color- and shape-invariant approach offers significant robustness against illumination, viewpoint, pose, and noise.
- This method presents a powerful tool for real-world object recognition applications.
- The color-shape context indexing is effective for efficient and accurate object identification.
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