In this paper, we propose a technique for extracting salient objects in images using feature maps, regardless of the complexity of images and the position of objects. In order to extract salient objects, the proposed method uses feature maps with edge and color information. We also propose a reference map created by integrating feature maps, and a combination map representing the boundaries of meaningful objects that is created by integrating the reference map and feature maps. Candidate object regions including boundaries of objects from the combination map are extracted by convex hull algorithm. Finally, by applying a segmentation algorithm on the area of candidate regions, object regions and background regions are separated, and real object regions are extracted from the candidate object regions. Experimental results show that the proposed method extracts the salient objects efficiently, with 84.3% precision rate and 81.3% recall rate.

The smart VISIOn chip has a large potential for application in general purpose high speed image processing systems. In order to fabricate smart vision chips including photo detector compactly, we have proposed the application of three dimensional LSI technology for smart vision chips. Three dimensional technology has great potential to realize new neuromorphic systems inspired by not only the biological function but also the biological structure. In this paper, we describe our three dimensional LSI technology for neuromorphic circuits and the design of smart vision chips .

In this paper, the automatic failure analysis method based on the random bit failure causing the major yield drop in DRAM and the analysis system named "SEC FAILURE ANALYSIS SYSTEM" are discussed. This system is developed for the accurate and rapid electrical analysis of the failure in a statistical manner in order to make a quick feedback to the manufacturing process.