The optimal housing price modeling : Growing of house price and income

[[abstract]]本研究以CampbellandCocco(2015)模型為基礎，在效用極大化下，從期數、住宅消費對一般住宅消費偏好、貼現因子、風險規避係數、期末財富的重要程度、利率、通膨、名目利率、房價、成長率和物價水準等因素中，討論房屋價格成長與所得成長對於最適購屋價格的影響。結果顯示，房屋價格成長與最適購屋價格有正向的關係；當所得成長率低時，市場上傾向貸款年限低，所得成長率越高，市場上傾向貸款年限高；以及當第一期實質利率越高，市場上傾向將所得投資在非住房消費上。[[abstract]]ThisthesisisbasedonthemodeloftheCampbellandCocco(2015),underthemaximizationofutility,frommanyfactorslikenumberofperiods,residentialconsumptiontothegeneralresidentialconsumptionpreference,discountfactor,riskaversioncoefficient,theimportanceoftheendofwealth,interestrate,inflationinterestrates,houseprices,growthratesandpricelevels,todiscussthehousingpricegrowthandincomegrowthfortheoptimalpurchaseprice.Theresultsshowthatthegrowthofhousepriceandtheoptimalpurchasepricehasapositiverelationship.Onthemarket,peopletendtoshort-termsloanwhentheincomegrowthrateislowandtendtolong-termsloanwhentheincomegrowthrateishigh.Andalsowhenthefirstofrealinterestrateishigher,peopleonthemarkettendtoinvestinnon-housingconsumption

Study on Image-based butterfly recognition using global and local features

[[abstract]]近年來台灣各地都在推動賞蝶觀光旅行，在野外常常看到蝴蝶的行蹤，卻不清楚蝴蝶的種類，一般民眾要查詢蝴蝶的種類會使用圖鑑，而傳統蝴蝶圖鑑以蝴蝶的科別做索引，對於不熟悉蝴蝶科別的民眾，此種查詢方式較無效率。本論文提出「以圖找圖」的搜尋方式，使用全域特徵如 HSV 色彩直方圖、主成分分析，及局部特徵如 SIFT、區域色彩直方圖，進行蝴蝶辨識的相關實驗，透過各種實驗結果找出適合蝴蝶辨識的方法。 本論文使用由「新故鄉文教基金會」的蝴蝶解說班在 2011 年進行野外調查所選取的常見蝴蝶種類共 41 種，而其中三種蝴蝶的雌雄外型上不同，因此總共44種蝴蝶，而每一種蝴蝶10張照片，資料庫蝴蝶照片總數為440張照片。我們在擷取特徵之進行去除背景和影像正規化的前置處理，使用四種特徵擷取方式進行實驗，實驗結果我們所提出的區域色彩直方圖的 Top1平均準確度 64.77%最高。[[abstract]]In recent years, butterfly watching tours are popularized all around Taiwan. We often see butterflies flying in the countryside, but we don’t know what kinds of butterflies they are. People usually recognize butterfly species by looking up in a butterfly handbook, but it is not convenient to carry it and hard to use for the beginners. In this thesis, content-based image retrieval is applied to recognize butterfly species by using several global and local features. The global features include HSV Color Histogram and Principal Component Analysis, and the local features include SIFT and Local Color Histogram. We conducted several experiments to find effective features for recognizing butterfly species. In this thesis, the butterfly database contains 41 common kinds of butterflies according to field researching of the Newhomeland foundation in 2011. In which three kinds of butterflies have different appearances for the males and females. As a result, the butterfly database has 44 butterfly types. In the database, each butterfly type has 10 pictures, so the butterfly database has 440 pictures. We remove the image background and normalize butterfly image before retrieving feature. The aforementioned four features were used in the experiments. The purposed Local Color Histogram has Top 1 average precision of 64.77% that is the highest rate among them.[[note]]碩