以預測市場匯聚資訊支援決策之方法：從機率預測到類別判定

A Method to Support Decision-making Based upon the Information Aggregation of Prediction Markets: from Probabilistic Forecasting to Categorical Forecasting

(3, n)── 門檻式視覺機密分享是 (k, n)──門檻式視覺機密分享的一種特例，不過在典型的 (3, n)──門檻式視覺機密分享的研究會產生設計複雜、還原影像黑白色差不佳、機密影像只適用於黑白影像、像素擴展等問題。為了解決上述幾個研究限制，本研究提出一個應用組合數學公式求解的非擴展型 (3, n)── 門檻式視覺機密分享。相較於其他相關研究，本研究具備下列幾項優點：(1) 設計概念十分單純，易於實作。(2) 還原影像的黑白色差值優於或等於其他 (3, n)──門檻值機密分享的研究成果。(3) 本研究所提出的分享模型適合任何型態的機密影像。(4) 分享投影片的大小將與機密影像相同。

(3, n)-threshold visual secret sharing ((3, n)-VSS) is a special case of (k, n)- threshold visual secret sharing. In previous related (3, n)-VSS researches, it has following drawbacks: complex design method, poor black-white contrast of the restored image, only suitable to binary image, and expanded pixels. In order to solve the above problems, our study proposed a novel approach to design (3, n)-VSS using combinatorics. Compared to other related studies, this study has the following advantages: (1) design concept is simple and easy to implement; (2) the black-white contrast of the restored image is better than other (3, n)-VSS researches; (3) the secret type is more suitable to grayscale and color image; (4) the transparencies (shares) are the same size as the secret image.