Image Processing of Manganese Nodules Based on Background Gray Value Calculation

To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage,we propose,with reference to features of manganese nodules,a method called“background gray value calculation”.As the result of the image procession with the aid this method,the two problems above are solved eventually,together with acquisition of a segmentable image of manganese nodules.As a result,its comparison with other segmentation methods justifies its feasibility and stability.Judging from simulation results,it is indicated that this method is applicable to repair the target shape in the image,and segment the manganese nodule image in a short time.Also,it could be used to synchronously process a large number of manganese nodules on different conditions in an image,laying a good foundation for automatic underwater manganese nodule survey.Even if the target in the image is slightly distorted,the statistical data of manganese nodules are still accurate.Moreover,other methods cannot be fully applied to the segmentation of manganese nodule images;in another word,the effectiveness and stability of this method are proved.

Image mathematical morphology and image restoration application in detecting underground bin level

By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.

A New Detection Method Based on CFAR and DE for OFPS

Optical fiber pre-warning system （OFPS） is widely utilized in pipeline transport fields. The intrusions of OFPS need to be located. In this system, the original signals consist of noises, interferences, and intrusion signals. Here, noises are background and harmless interferences possessing with high power, and the intrusion signals are the main target of detection in this system. Hence, the study stresses on extracting the intrusion signals from the total ones. The proposed method can be divided into two parts, constant false alarm rate （CFAR） and dilation and erosion （DE）. The former is applied to eliminate noises, and the latter is to remove interferences. According to some researches, the feature of noise background accords with the CFAR spatial detection. Furthermore, the detection results after CFAR can be presented as a binary image of time and space. Besides, interferences are relatively disconnected. Consequently, they can be eliminated by DE which is introduced from the image processing. To sum up, this novel method is based on CFAR and DE which can eliminate noises and interferences effectively. Moreover, it performs a brilliant detection performance. A series of tests were developed in Men Tou Gou of Beijing, China, and the reliability of proposed method can be verified by these tests.