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Articles by Tiejun Zhang
Total Records ( 2 ) for Tiejun Zhang
  Tiejun Zhang , Xianguo Wang , Jianguo Han , Yunwen Wang , Peisheng Mao and Mark Majerus
  Proper between-row and within-row spacing is essential for optimizing alfalfa seed yields and stand longevity. Using three alfalfa (Medicago sativa L.) cultivars (WL232HQ, Derby, and Algonquin), we conducted a field study from 2004 to 2007 to evaluate the effects of three between-row spacing treatments (60, 80, and 100 cm) and four within-row spacing treatments (15, 30, 45, and 60 cm) on seed yield, seed yield components, plant height, basal stem diameter, and lodging. Our hypothesis was that the intermediate between-row and within-row spacing would gradually improve seed yields in later years. The highest seed yields were obtained with 60-cm between-row spacing and 15-cm within-row spacing in 2004, but with 80-cm between-row spacing and 30-cm within-row spacing in 2006 and 2007. The significant year x between-row spacing and year x within-row spacing interactions for seed yield indicated that 80-cm between-row spacing and 30-cm within-row spacing produced the best seed yields as years advanced, and our results confirmed this. With the increase of within-row spacing, stems per square meter decreased, while racemes per stem increased significantly. The effects of between-row and within-row spacing on seeds per pod, however, were not significant in four years. The results suggest that 80-cm between-row spacing and 30-cm within-row spacing can decrease the risk of lodging and optimize seed yields in the third and fourth harvest years.
  Tiejun Zhang , Qi Han , Ahmed A. Abd El-Latif , Xuefeng Bai and Xiamu Niu
  Cartoon pirate uploading is a very serious problem for the image and video-sharing website. In this study, we propose a new method to detect the characters in 2D-cartoon images, aiming at rejecting pirate uploading automatically. We extract the curve in the cartoon image as the main content and then design a local shape feature named Scalable-Shape Context (SSC) to present the local shape of cartoon. Firstly, we use the Harris-Laplace corner detector to find the key points at multi-scale in the cartoon image, most of which are localized at the junctions of curves. Secondly, the scale of each key point is used as a reference scale for Shape Context (SC) to describe the curvilinear structure around the key points. Then, the matching problem between the key points extracted from the input model and testing image is solved as an optimal assignment problem. Finally, a Hough-voting scheme is employed to find the location of the character in the testing image. The experimental results show that the proposed SSC-based detection method is effective in the detection of 2D-cartoon characters.
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