Feature Detection by Color Tensor-Based Photometric Invariants

Xing Sheng Yuan; Zheng Zhi Wang

doi:10.4028/www.scientific.net/AMR.341-342.540

Paper Titles

Research and Design of the Security Area Classification Model of Electric Power Information System
p.519

Simulation and Performance Analysis of OFDM System Based on Simulink
p.524

An Efficient Algorithm for Sequential Pattern Mining with Time Constraints
p.530

Face Detection Based on Color Model and Grayscale Value Distribution
p.535

Feature Detection by Color Tensor-Based Photometric Invariants
p.540

Accuracy Improvement of Graph-Cut Image Segmentation by Using Watershed
p.546

A Kind of Improved GQM Measurement Model for Software Process
p.550

A Novel Physically-Based Method for Curve Deformation
p.555

Image Retrieval Based on Shape Feature and Color Feature
p.560

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 341-342Feature Detection by Color Tensor-Based...

Feature Detection by Color Tensor-Based Photometric Invariants

Abstract:

Although the majority of images are recorded in color format nowadays, computer vision research is still mostly restricted to luminance-based feature detection. In this paper, we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features. The combination of the photometric invariance theory and tensor based features allows for detection of a variety of features such as photometric invariant edges, corners. Experiments show that the proposed features are robust to scene incidental events and perform well in real-world scene.

You might also be interested in these eBooks

Material and Manufacturing Technology II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 341-342)

Pages:

540-545

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.341-342.540

Citation:

Cite this paper

Online since:

September 2011

Authors:

Xing Sheng Yuan, Zheng Zhi Wang

Keywords:

Color Tensor, Feature Detection, Photometric Invariants

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.M. Geusebroek, R. van den Boomgaard, A.W.M. Smeulders, and H. Geerts. Color invariance. IEEE Trans. Pattern Analysis and Machine Intelligent. 23(12): 1338-1350, (2001).

DOI: 10.1109/34.977559

Google Scholar

[2] Joost van de Weijer, Theo Gevers, Jan-Mark Geusebroek. Edge and Corner Detection by Photometric Qausi-Invariants. IEEE Trans. Pattern Analysis and Machine Intelligent. 27(4): 0162-8828, (2005).

DOI: 10.1109/tpami.2005.75

Google Scholar

[3] Jose M. alvarez, Theo Gevers, Antonio M. Lopez. Learning photometric Invariance for Object Detection. International Journal Computer Vision. 90: 46-61, (2010).

Google Scholar

[4] S.A. Shafer, Using Color to Separate Reflection Components, Color Research and Application, vol. 10, no. 4, pp.210-218, Winter (1985).

DOI: 10.1002/col.5080100409

Google Scholar

[5] J. van de Weijer, Theo Gevers, A.W.M. Smeulders. Robust Photometric Invariant Features from the Color tensor. IEEE Trans. Image Processing. 15(1), 118-127, (2006).

DOI: 10.1109/tip.2005.860343

Google Scholar

[6] Silvano Di Zenzo. Note: A note on the gradient of a multi-image. Computer Vision, Graphics, and Image Processing, 33(1): 116-125, (1986).

DOI: 10.1016/0734-189x(86)90223-9

Google Scholar

[7] J. Bigun, G. Granlund, and J. Wiklund. Multidimentional Orientation Estimation With Application to Texture Analysis and Optical Flow. IEEE trans. on pattern analysis and machine intelligence, 13(8): 775-790, (1991).

DOI: 10.1109/34.85668

Google Scholar

[8] C. Harris and M. Stephens. A combined corner and edge detector. In Proc. 4th Aley Vision Conf, volume 15, pages 147-151, (1988).

Google Scholar