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In this paper we have worked on the problem of automatic ship detection in IR images. Segmentation of IR ship images is always a challenging task because of the intensity inhomogeneity, sea clutters and noise. In our proposed approach, we have shown that efficiency and accuracy of IR ship detection algorithms can be enhanced by only searching around the salient parts of the input image. In order to identify most salient regions, at first we computed the saliency map of the input image using the Graph-Based Visual Saliency (GBVS) algorithm. Next a multilevel thresholding of the saliency map is performed to get the ranked salient regions of the input image. By using the ship size as prior information, top-k regions are further processed to get the fine segmentation of the target. For this purpose we have used spatial constraint based fuzzy c-mean (FCM) segmentation algorithm along with a strategy to choose the cluster selection threshold. Experiments are performed on a data set of 18 diverse and challenging IR ship images, collected from different sources. Results show that our proposed framework is very effective and perform better compare to the methods which directly search the target in entire image.

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This paper describes a methodology for monitoring industrial processes and plant that can be implemented cost-effectively within small-to-medium enterprises. The methodology is based on a network of 8-bit microcontrollers that communicate with each other on a controller area network bus. Ethernet connectivity is provided so that remote users can access the system on the internet. The software models developed for data acquisition nodes and the design of remote user interfaces and supervisory nodes are also explained. The system is aimed at providing specific maintenance guidance and fault identification, rather than gathering data for off-line analysis. Overly complicated processing is avoided to make real-time implementation possible, using 8-bit microcontrollers. The methodology emphasizes the use of process controller signals for fault detection and sensor signals for fault isolation. The suitability of the methodology is explored by acquiring signals from a laboratory-based process rig. Suitable monitoring techniques for the system in time and frequency domains are also discussed.