fulbright Taiwan online journal

fulbright Taiwan online journal

Day: March 23, 2015

美國求學生活淺談

     窗外飄著細雪,客廳的地毯上放著還沒打包完的行李箱,書桌上散落著期末報告的相關書籍,我又打開電腦默默地敲打著要交給傅爾布萊特的心得報告。這麼忙碌的生活,就是來到美國後的日常生活寫照。總是在準備著下一份報告,總是在規劃著另一個申請計劃,彷彿無止盡的學習,努力,這就是我來到俄亥俄州立大學攻讀博士的實際情況。

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美國體驗

     申請傅爾布萊特華語教學獎助計畫之前,我做了好一陣子的線上華語教學,從各國學生的學習反應、造句、作文,能聽聞各地的奇聞軼事,能領會與語言的妙處。有次我讓學生用手勢比出我所說的中文動詞,我說:「吃。」學生同時作勢拿叉子,只有我一個人伸出食指和中指作勢拿筷子扒飯。華語教學的樂趣讓我期待著這次到美國大學教華語,順道體驗與我迥異的生活和文化。

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My Fulbright Experiences at University of Minnesota

       As a Fulbright Taiwan grantee, I always feel honored to introduce my country and discuss culturaldifferences whenever I meet people. When I attended a three-week Fulbright pre-academic program, which was sponsored by Fulbright IIE at Virginia Tech, I met Fulbright grantees from 26 countries. We not only shared our diverse cultures, but also learned about American culture and academic preparation together. It was a time to effacestereotypes and rethink questions of culture and mutual respect; most importantly, it was the time to try to understand other voices. From religiousto political issues, from culinary habits to the educational system, the topics wecovered ranged widely every day that I spent time with Fulbrighters. In a way,this experience was a challenge because it was necessary to expand my international knowledge so quickly, while being prepared to answer questions about my own country. Overall, it was worthwhile to share my perspectives that could present Taiwanese perspectives or speak for Taiwan in an international context.        After the pre-academic program, I started my PhD coursework at the University of Minnesota where I’m currently studying learning technologies in the fields of curriculum and instruction studies. In my first semester, it’s been

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Hierarchical Image Segmentation based on Sequential Partitioning and Merging

1. Background      In computer vision, image segmentation is the process that decomposes an image into multiple segments. The goal of this process is to partition an image into something more meaningful and easier for subsequent analyses. For example, for the image shown in Figure 1, human eyes can easily recognize that there are two persons walking on a beach. Apparently, they have just finished snorkeling. To computers, however, this image is nothing but an array of pixel data, with each image pixel (picture element) containing three different kinds of color values (Red, Green, and Blue). If we can decompose the image into several smaller regions with each region containing similar colors or textures, it becomes easier for the computer to recognize the possible objects in the image (like humans, diving boots, and beach) and understand the image content.        Over the past few decades, hundreds or even thousands of segmentation algorithms have been proposed, trying to produce segmentation results that are close to what human eyes perceive. Among these segmentation approaches, graph-based methods and clustering-based methods have proven to be quite successful and have been widely used. However, for the beach image in Figure 1, graph-based

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Research & Reflections

fulbright taiwan online journal