Monitoring of inclination of land is important to prevent damage from landslide. Low cost and stable system is necessary for this purpose. This paper presents a new inclination measurement system and its application to sensor network of disaster prevention. Our system has a bull's eye level, a digital camera and wireless module. The angle and direction of inclination is measured from the photo of bull's eye level. The achieved resolution was 0.0053degree/pixel using a bull's eye level with a curvature radius of 800 mm. Measurement data was compared with the data from commercial inclinometer.

In this paper, the image segmentation algorithm based on wavelet transform is presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, the segmented partitions emerge when the state of the lives reaches an equilibrium. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the biomedical image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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