Ms. Xiao Zhang Profile

Xiao Zhang

at Beijing Institute of Technology

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Publications (4)

Proceedings Article | 2 September 2009 Paper

Image enhancement of range gated underwater imaging system based on least square error

Hailan Li, Xia Wang, Tingzhu Bai, Weiqi Jin, Xiao Zhang, Lin Zhao

Proceedings Volume 7443, 74432B (2009) https://doi.org/10.1117/12.832187

KEYWORDS: Image enhancement, Imaging systems, Image processing, Image quality, Image segmentation, Laser imaging, Signal to noise ratio, Underwater imaging, Laser systems engineering, Computer simulations

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Proceedings Article | 28 January 2009 Paper

Adaptive enhancement method of infrared image based on scene feature

Xiao Zhang, Tingzhu Bai, Fei Shang

Proceedings Volume 7156, 715633 (2009) https://doi.org/10.1117/12.811810

KEYWORDS: Infrared imaging, Infrared radiation, Image enhancement, Image processing, Image contrast enhancement, Algorithm development, Thermography, Digital image processing, Visible radiation, Surveillance

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Proceedings Article | 28 January 2009 Paper

Scene classification of infrared images based on texture feature

Xiao Zhang, Tingzhu Bai, Fei Shang

Proceedings Volume 7156, 715626 (2009) https://doi.org/10.1117/12.806945

KEYWORDS: Infrared radiation, Infrared imaging, Thermography, Scene classification, Wavelet transforms, Feature extraction, Image classification, Image segmentation, Algorithm development, Image processing

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Scene Classification refers to as assigning a physical scene into one of a set of predefined categories. Utilizing the method texture feature is good for providing the approach to classify scenes. Texture can be considered to be repeating patterns of local variation of pixel intensities. And texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity. Texture describes the structural information of images, so it provides another data to classify comparing to the spectrum. Now, infrared thermal imagers are used in different kinds of fields. Since infrared images of the objects reflect their own thermal radiation, there are some shortcomings of infrared images: the poor contrast between the objectives and background, the effects of blurs edges, much noise and so on. Because of these shortcomings, it is difficult to extract to the texture feature of infrared images. In this paper we have developed an infrared image texture feature-based algorithm to classify scenes of infrared images. This paper researches texture extraction using Gabor wavelet transform. The transformation of Gabor has excellent capability in analysis the frequency and direction of the partial district. Gabor wavelets is chosen for its biological relevance and technical properties In the first place, after introducing the Gabor wavelet transform and the texture analysis methods, the infrared images are extracted texture feature by Gabor wavelet transform. It is utilized the multi-scale property of Gabor filter. In the second place, we take multi-dimensional means and standard deviation with different scales and directions as texture parameters. The last stage is classification of scene texture parameters with least squares support vector machine (LS-SVM) algorithm. SVM is based on the principle of structural risk minimization (SRM). Compared with SVM, LS-SVM has overcome the shortcoming of higher computational burden by solving linear equations, and has been widely used in classification and nonlinear function estimation. Some experimental results are given in the end. The result shows that Gabor wavelet transform is successful to extract the texture feature of infrared image. Compared with other methods the method mentioned in this paper reduces the probability of recognition and enhances the robustness.

Proceedings Article | 8 January 2008 Paper

Pseudo-color coding method of infrared images based on human vision system

Xiao Zhang, Tingzhu Bai, Hailan Li

Proceedings Volume 6835, 68351N (2008) https://doi.org/10.1117/12.756471

KEYWORDS: Infrared imaging, Infrared radiation, RGB color model, Human vision and color perception, Thermography, Visualization, Metals, Image processing, Computer programming, Image display

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