學習資訊專業學院—資訊教育研究所

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/25

資訊教育研究所之碩士班成立於民國80年，博士班成立於民國86年，目前研究生共約160名。本所原屬資訊教育學系，於95學年度起因應系所組織調整，成為獨立研究所，歸屬教育學院。

本所以『資訊科技教育』和『數位學習』兩個專業領域之研究發展與人才培育為宗旨，課程設計分別針對此兩個專業領域規劃必、選修專業科目，提供學生紮實而嚴謹的學術專業知能及個別化之研究訓練。本所教育目標包括：

1、培育資訊科技教育人才；
2、培育數位學習產業人才；
3、培育資訊科技教育與數位學習研究人才。

本所目前六名專任教師，四位教授，二位副教授，在資訊教育領域均具有豐富之教學與研究經驗且均積極從事研究，每年獲科技部補助研究計畫之平均數量與金額在本校名列前茅。另外，本所教師積極參與國內重大資訊教育政策及課程綱要之制定，積極推動國內資訊教育之發展。

An unsupervised classification technique conceptualized in terms of neural and fuzzy disciplines for the segmentation of remotely sensed images is presented. The process consists of three major steps: 1) pattern transformation; 2) neural classification; 3) fuzzy grouping. In the first step, the multispectral patterns of image pixels are transformed into what we call coarse patterns. In the second step, a delicate classification of pixels is attained by applying an ART neural classifier to the transformed pixel patterns. Since the resultant clusters of pixels are usually too keen to be of practical significance, in the third step, a fuzzy clustering algorithm is invoked to integrate pixel clusters. A function for measuring clustering validity is defined with which the optimal number of classes can be automatically determined by the clustering algorithm. The proposed technique is applied to both synthetic and real images. High classification rates have been achieved for synthetic images. We also feel comfortable with the results of the real images because their spectral variances are even smaller than the spectral variances of the synthetic images examined.

學習資訊專業學院—資訊教育研究所

Browse

Filters

Settings

Sort By

Results per page

Search Results