Low-Quality Video Images Recognize: A Self-Adapting Segmentation Algorithm

Zheng Hong Liu

doi:10.4028/www.scientific.net/AMM.614.389

Paper Titles

Extraction and Segmentation of Books Call Number Image for Books on the Shelves of Library
p.374

Fuzzy Logic and Classification of Information Sources
p.378

High Speed Data Transfer System Based on the USB3.0 Technology
p.381

Improvement on a Fuzzy C-Means Algorithm Based on Genetic Algorithm
p.385

Low-Quality Video Images Recognize: A Self-Adapting Segmentation Algorithm
p.389

SVMMAP Modeling of SAR Imagery for Unsupervised Segmentation with Bootstrap Sampling
p.393

The Computer Interlocking Software System Reliability Test Based on the Monte Carlo
p.397

The Mean Value Theorem of a New Kind of Fuzzy Integral
p.401

The Optimization Algorithm of Association Rules Mining
p.405

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 614Low-Quality Video Images Recognize: A...

Low-Quality Video Images Recognize: A Self-Adapting Segmentation Algorithm

Abstract:

In this paper, a self-adapting segmentation algorithm for low-quality video Image based on camera information is present in detail. An adaptive threshold value method is used to extract the goal information. Combining features of the goal and video background, an adaptive segmentation algorithm based on hue histogram and saturation histogram is proposed, which can adapt the changing environmental conditions. The experiments have demonstrated the good performance of the self-adapting segmentation algorithm.

You might also be interested in these eBooks

International Conference Machinery, Electronics and Control Simulation

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 614)

Pages:

389-392

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.614.389

Citation:

Cite this paper

Online since:

September 2014

Authors:

Zheng Hong Liu*

Keywords:

Hue Histogram, Low-Quality Images, Saturation Histogram, Self-Adapting Segmentation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Wall R J．The Gray level history for threshold boundary determination in image processing to the scene segmentation problem in human chromosome analysis. Los Angeles, USA: University of California, (1974).

Google Scholar

[2] Tremeau A, Borel N. A region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191-1204.

DOI: 10.1016/s0031-3203(96)00147-1

Google Scholar

[3] Shen X. Segmentation of 2D and 3D image through a hierarchical clustering based on region modeling. Pattern Recognition, 1998, 31(9): 1295-1309.

DOI: 10.1016/s0031-3203(97)00159-3

Google Scholar

[4] Hager G D. A brief reading guide to dynamic vision. The Johns Hopkings University. http: /www. cs. jhu. ed/~hager, 2001, 1-8.

Google Scholar

[5] Bhanu B, Peng J. Adaptive integrated image segmentation and object recognition. IEEE transactions on system, man, and cybernetics, part C, applications and reviews, 2000, 30(4): 427-441.

DOI: 10.1109/5326.897070

Google Scholar

[6] Bruce J, Balch T, Veloso M. Fast and cheap color image segmentation for interactive robots. Available at: http: /www-2. cs. cmu. edu/~mmv/papers/wirevision00. pdf.

Google Scholar

[7] Shi J, Tomasi C. Good features to track. In Proc. Computer Vision and Pattern Recognition, (1994).

Google Scholar