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RISS 인기검색어
- 검색키워드
- 저자 : Yide Ma (검색결과 3 건)
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Effect of Acrylic Content on the Properties of the Polyurethane/Polyacrylate Composite Emulsion
Guoyan Ma,Yiding Shen,Ruimin Gao,Xiaorong Wang 한국고분자학회 2017 폴리머 Vol.41 No.1
A polyurethane/polyacrylate (PUA) composite emulsion was synthesized by using polyurethane (PU) as seeds with soap-free emulsion polymerization, in which methyl methacrylate (MMA) and butyl acrylate (BA) were used as main acrylic monomers. The effect of acrylic contents and “stiff” and “soft” weight ratio of acrylic monomers on the properties of the films were investigated. The Fourier transform infrared (FTIR) results showed that acrylic monomers were involved in the emulsion copolymerization. The optimum composition of PUA composite formation was obtained when the polyacrylate (PA) content was 20%, in which the weight ratio of MMA and BA was 2/1. With the increment of PA content, the decomposition temperature increased.
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Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN
Yuli Chen,Yide Ma,Dong Hwan Kim,Sung-Kee Park IEEE 2015 IEEE transactions on neural networks and learning Vol.26 No.8
<P>In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods.</P>
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A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
Yuli Chen,Sung-Kee Park,Yide Ma,Ala, R IEEE 2011 IEEE transactions on neural networks Vol.22 No.6
<P>An automatic parameter setting method of a simplified pulse coupled neural network (SPCNN) is proposed here. Our method successfully determines all the adjustable parameters in SPCNN and does not need any training and trials as required by previous methods. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons, and then deduce the sub-intensity range expression of each segment based on the general formulae. Besides, we extract information from an input image, such as the standard deviation and the optimal histogram threshold of the image, and attempt to build a direct relation between the dynamic properties of neurons and the static properties of each input image. Finally, the experimental segmentation results of the gray natural images from the Berkeley Segmentation Dataset, rather than synthetic images, prove the validity and efficiency of our proposed automatic parameter setting method of SPCNN.</P>
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