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Hu Y.-L.,Shanghai University | Hu Y.-L.,Key Laboratory of Advanced Display and System Application | Zhou C.,Key Laboratory of Advanced Display and System Application
Journal of Shanghai University | Year: 2010

In this paper, the design and verification process of an automobile-engine-fan control system on chip (SoC) are introduced. The SoC system, SHU-MV08, reuses four new intellectual property (IP) cores and the design flow is accomplished with 0.35 μm chartered CMOS technology. Some special functions of IP cores, the detailed integration scheme of four IP cores, and the verification method of the entire SoC are presented. To settle the verification problems brought by analog IP cores, NanoSim based chip-level mixed-signal verification method is introduced. The verification time is greatly reduced and the first tape-out achieves success which proves the validity of our design. © 2010 Shanghai University and Springer-Verlag Berlin Heidelberg. Source

Shi R.,Shanghai University | Liu Z.,Shanghai University | Liu Z.,Key Laboratory of Advanced Display and System Application | Xue Y.,Shanghai University
ISPACS 2010 - 2010 International Symposium on Intelligent Signal Processing and Communication Systems, Proceedings | Year: 2010

Salient object segmentation is an important technique for many content based applications. This paper presents an unsupervised salient object segmentation method under the graph cut optimization framework. First, we exploit a kernel density estimation based saliency model to generate the saliency map, which provides the useful cues for object segmentation. Then we exploit the saliency map to adaptively define the region cost term, the boundary cost term and their balancing weight in the cost function, which is minimized using graph cut to obtain a binary segmentation of salient objects. Experimental results on a variety of images demonstrate the better segmentation performance of our approach. © 2010 IEEE. Source

Xue Y.,Shanghai University | Shi R.,Key Laboratory of Advanced Display and System Application | Liu Z.,Shanghai University
Optical Engineering | Year: 2011

This paper proposes a novel saliency model using multiple region-based features. The original image is initially segmented into a set of regions using the mean shift algorithm, and region merging is performed to obtain a moderate segmentation result. Then, three types of regional saliency measures are calculated using region-based features including local/global color difference, orientation difference, and spatial distribution, and they are integrated into an overall regional saliency measure for each region. Finally, the pixel-wise saliency map is generated by combining regional saliency measures with the distance-weighted color similarity between each pixel and each region. Experimental results demonstrate that our saliency model achieves an overall better saliency detection performance than previous saliency models, and the saliency maps generated using our model are more suitable for content-based applications such as salient object detection, content-aware image retargeting, and object-based image retrieval. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). Source

Du H.,Shanghai University | Liu Z.,Shanghai University | Liu Z.,Key Laboratory of Advanced Display and System Application | Xue Y.,Shanghai University | Shi R.,Shanghai University
IET Conference Publications | Year: 2011

Seam carving is a new image retargeting method, which can alleviate the distortion on the main content of the image compared to the traditional scaling method. However, the original seam carving method uses the gradient amplitudes of pixels to construct the energy map, but neglects the gradient directions of pixels. Actually, the gradient direction refers to the direction where the pixel has the maximum rate of change. We can use the gradient direction of pixels to construct the direction map, and then naturally generate multiple seams from top to bottom based on the direction map. In addition, we use the priority windows to avoid intersecting seams. Experimental results show that our method can accelerate the speed of seam carving about 98 times while maintaining the quality of the image retargeting result. Source

Shi R.,Shanghai University | Liu Z.,Shanghai University | Liu Z.,Key Laboratory of Advanced Display and System Application | Du H.,Shanghai University | And 2 more authors.
IEEE Signal Processing Letters | Year: 2012

Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the cluttered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. In this letter, we propose a novel approach to detect salient object from saliency map without manually setting any parameters. Region diversity maximization is used as the objective function to direct the object detection, and the optimal window for locating the salient object is obtained using an efficient iterative search scheme. Experimental results on different saliency maps demonstrate the overall better detection performance and computational efficiency of our approach. © 2012 IEEE. Source

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