基于Contourlet变换和分水岭分割的红外图像分割研究

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I. INTRODUCTION
the image. In the recognition process of the image processing, the most fundamental and most difficult aspect is segmentation. Only after good segmentation, the targets can be separated and classed well. Image segmentation is progress of divide the image into a number of parts based on certain criteria, and extracts the regions and goals which we are interested in. In this paper, we use sharp frequency localize contourlet transform and morphology method for denoising the mixture noise, background suppression and enhancement goals. By selecting the appropriate structural elements of the image, extract goals by morphology watershed method. From the experiments, we can find that, the new method gets the ideal result. We introduce the general progress and method of image segmentation in Section I. In Section II, we briefly introduce sharp frequency localization contourlet transform and morphology watershed method. As a solution, we propose in Section III a new method in image segmentation. Vision experiments are presented in Section IV. We conclude the paper in Section V. II. SHARP FREQUECY LOCALIZATION CONTOURLET AND MORPHOLOGY METHODS A. Sharp Frequency Localization Contourlet The original contourlet is constructed by the combination of Laplacian pyramid [1]. We can see that the Laplacian pyramid shown in the diagram is a simplified version of its actual implementation as shown in Fig. 1. However, the frequency division in Fig. 1(b) is obtained by ideal filters. When non-ideal filters are combined with Laplacian pyramid, we show a more realistic illustration of one of the directional filters [2] from the direction filter banks in Fig. 2(a). If the directional filter must first be upsampled by 2 along each dimensions, which as shown in Fig. 2(b). Because of the upsampling, the aliasing components are folded towards the lowpass regions and concentrated mostly along two lines ω2=±π/2. Combining the upsampled DFB was shown in Fig. 2(c). In Fig. 2(d), we can see the resulting contourlet subband. In order to solve this problem, Yue M. LU proposed a new construction of a sharp frequency localization contourlet transform (SFLCT) [3]. Instead of using the Laplacian pyramid, he employed a new pyramid structure for multiscale decomposition, which is shown in Fig. 3. The difference between SFLCT and contourlet transform is that, SFLCT use the new multiscale pyramid and can employ a different set of lowpass and highpass filters for the levels.
Manuscript received May 10, 2010. This work was supported in part by the Jiangsu Province Nature Science Research Plan Projects for Colleges and Universities (grant No. 08KJD120002) and Open fund Project of Jiangsu Province Key Labcation (No. ZK206008). Huimin Lu is with the Kyushu Institute of Technology, Kitakyushu, 804-8550 Japan and Yangzhou University, Yangzhou, 225-009 China (phone: +81-80-4278-5999; fax: +81-93-884-3203; e-mail: yzulhm@ ). Lifeng Zhang is with the Kyushu Institute of Technology, Kitakyushu, 804-8550 Japan. He is now with the Department of Electrical Engineering and Electronics. (phone: +81-93-884-3272; fax: +81-93-884-3202; e-mail: zhang@ elcs.kyutech.ac.jp). Min Zhang is with School of Information Engineering, Yangzhou University, Yangzhou, 225-009 China. Xuelong Hu was with Yangzhou University, Yangzhou, 225-009 China. He is now with the School of Information Engineering (e-mail: huxuelong@). Seiichi Serikawa is with the Department of Electrical Engineering and Electronics, Kyushu Institute of Technology,Kitakyushu 804-8550 Japan. (e-mail: serikawa@elcs.kyutech.ac.jp).
International Conference on Intelligent Control and Information Processing August 13-15, 2010 - Dalian, China
A Method for Infrared Image Segment based on Sharp Frequency Localized Contourlet Transform and Morphology
Huimin Lu, Lifeng Zhang, Min Zhang, Xuelong Hu, and Seiichi Serikawa

Abstract—A method associating sharp frequency localized contourlet transform with morphology to remove mixing noise and suppress background disturbance of infrared images is proposed. This method use sharp frequency localized contourlet transform and a morphology filter to deny the noise in infrared images. And then segment infrared images by regional method of morphology watershed, the target can be isolated easily. During the experiments, we can see that, the submit method can detect and segment the goal in the low SNR infrared image with complicated background effectively. And the result of this method is better than that based on contourlet transform and wavelet transform.