本研究嘗試不全以負面假設來看待配戴眼鏡對人臉辨識的影響。吾人將以邊緣偵測圖為基礎，以邊點強度的分析來建立一套定位眼鏡的偵測系統。同時用偵測出的鏡框位置，以邊緣點的強度、密度比較的方式，定位眼睛的位置；並以前述兩套偵測演算，採擷其過程的資訊，進一步地定位鼻子與嘴巴的位置。這些演算形成一個簡易的人臉特徵定位系統，其將可處理配戴眼鏡的人臉；吾人也將進一步地經由其處理過程與結果，分析眼鏡對區域人臉辨識的影響，進而引導出非自然物件可能對人臉辨識的阻礙或輔助。 論文也將以全域比對法中的PCA與ICA演算法作一連串的實驗，剖析眼鏡對於全域辨識的影響；此外，亦用相同的方法來測試非自然物(眼鏡)、光源亮度與人臉角度對於人臉辨識阻礙的程度，以探究是否系統值得花費更大的代價，來移除眼鏡這個被一致認定的人臉辨識障礙，並得以在辨識演算法上獲得更高的效能。 / The objective of this thesis is to investigate the efficacy of face recognition systems when the subjects are wearing glasses. We do not presume that non-facial features such as glasses are nuisances. Instead, we will study whether the inclusion of glasses will have a positive impact on the face detection procedure and how it affects the feature extraction process. We will demonstrate how to use techniques based on local feature analysis to reduce the uncertainties in the matching result due to interferences around the eyes and nose caused by optical glasses. We have also conducted extensive experiments to analyze the effect of glasses on face recognition systems based on global matching strategy. Specifically, we perform both principal component analysis (PCA) and independent component analysis (ICA) on face databases with different percentage of subjects wearing eye glasses. It is concluded that external objects such as glasses will have a negative impact on face recognition using global analysis approaches. However, the adverse influences of illumination and pose are more conspicuous during the recognition process. Therefore, one should take caution when attempting to adapt the global matching scheme to handle the difficulties caused by glasses.

Radio Frequency Identification (RFID) has recently (2005) become a sizzling topic for discussion (Lewis 2004). It is not only the subject of conversations among engineers but also the center of focus for venture capital investors and start-ups. However, behind these glamorous scenes, there are many failures in product launches, unsuccessful business operations, and worth thinking business opportunities. Is it our expectation for this revolutionary technology too high or the business strategy is fundamentally wrong? As with other emerging new technologies, adequate distribution channels are required to bring commercialized RFID products to people’s daily lives. However, as with different commercial products, dissimilar product nature and characteristics require different distribution channel strategy. / Radio Frequency Identification (RFID) has recently (2005) become a sizzling topic for discussion (Lewis 2004). It is not only the subject of conversations among engineers but also the center of focus for venture capital investors and start-ups. However, behind these glamorous scenes, there are many failures in product launches, unsuccessful business operations, and worth thinking business opportunities. Is it our expectation for this revolutionary technology too high or the business strategy is fundamentally wrong? As with other emerging new technologies, adequate distribution channels are required to bring commercialized RFID products to people’s daily lives. However, as with different commercial products, dissimilar product nature and characteristics require different distribution channel strategy.

在不同的光源環境下，顏色的資訊會隨著環境而改變，因此要做到穩定的膚色辨識並不容易。先前的研究指出，人類的膚色大致上可歸納於特定顏色座標系統中的一段範圍內。但是根據我們的實驗，這段範圍會隨著環境光源的改變而產生偏移，因此運用相同的標準，無法在不同的場景下做到準確的膚色辨識與切割。針對這個議題，我們提出利用影像中非色彩的資訊，找出最符合膚色的範圍。具體來說，借重臉部偵測獨立於色度的特性，我們使用臉部偵測的結果，做為調整膚色範圍的依據。實驗所得到的辨識結果，效能與效率都足以運用在以視覺為基礎的人機介面，例如手部與指尖偵測。 / Robust detection of skin color is a difficult task since color information changes under different lighting conditions. Previous research indicated that human skin color is restricted to a small range of values in certain color coordinate systems. However, the ranges tend to shift with varying illumination according to our experiments. It is therefore unattainable to derive a universal standard for skin color detection and segmentation for general scenes. To address this issue, we propose to use achromatic features to identify the proper ranges of skin color in an image. Specifically, we utilize the result of face detection, which is independent of chromatic properties, to guide the process of skin color range selection. Experimental results have validated the efficacy and efficiency of the proposed methodology for vision-based human-computer interface such as hand and finger detection.

本文主要探討中文聲調在口語字彙觸接過程中所扮演的角色。實驗一藉由眼動實驗中的Visual World Paradigm作業，觀察中文聲調影響口語字彙辨識的時序歷程。受試者在聽到指導語和目標字之後，用滑鼠在螢幕上點選聽到的目標字，例如，螢幕上出現的字包含一個目標字：「摸」、一個競爭字(與目標字只有聲調相同：「挖」或是與目標字只有音段相同：「抹」)，以及兩個聲調與音段和目標字完全不同的無關字：「怒」、「菊」。為了觀察目標字、競爭字及無關字在口語字彙處理時的競爭，我們會計算各個字彙的凝視比例。實驗一中由於聲調與目標字相同的競爭字與目標字的第一個音段就開始產生差異，因此未觀察到聲調早期介入的影響。實驗二透過與實驗一相同的實驗程序及方法，操弄目標字和競爭字中聲調和前兩個音段(Cohort)的異同以探測更早期的聲調影響。螢幕呈現包含一個目標字「湯」、一個競爭字(前兩個音段和聲調皆與目標字相同：「胎」，或是只有前兩個音段相同但聲調與目標字不同：「泰」)，以及兩個聲調與音段和目標字完全不同的無關字「剖」、「痕」。結果顯示，聲調在語音訊息前兩個音段時就會產生影響，也就是聲調的影響在語音結束前即有作用。再者，本文發現聲調無法單獨且獨立地對於語音辨識產生影響，此看法與聲調表徵需以“toneme” node獨立地存在於the modified TRACE model的看法不盡相同 (Malins & Joanisse, 2010; Ye & Connine, 1999; Zhao, Guo, Zhou, & Shu, 2011)。 / The present study aims to examine the role of tonal information during Mandarin Chinese spoken character recognition. Two eye-tracking experiments were conducted with the visual world paradigm, which participants heard a Chinese monosyllabic character and used a mouse to click on the corresponding character in a visual array of 4 characters on the screen. Experiment 1 manipulated the relationship between the spoken target characters and written characters on the screen, including a target (e.g., /mɔ1/‘touch’), a tonal competitor (the tone was the same as target except segment: e.g., /wa1/‘dig’) or a segmental competitor (the segmental structure was the same with the target except tone: e.g., /mɔ3/ ‘wipe’), and two unrelated distractors (the segments and tone were different from target: e.g., /nu4/ ‘anger’, and /tɕy2/ ‘chrysanthemum’). The fixation proportions on target, competitors and the unrelated distractors were computed during the unfolding of the auditory target stimuli. The results showed tonal difference was detected before the end of auditory stream. However, no early involvement of tonal information was found, which may due to the tonal competitor and target shared no segment from the first phoneme. In order to examine the earlier tonal processing, Experiment 2 manipulated two types of cohort competitors sharing the initial two segments with the target (e.g., /tʰɑŋ1/ “soup”), a cohort-tone competitor, e.g., /tʰaj1/ “fetus” (both tone and initial two segments are the same with target) and a cohort-only competitor e.g., /tʰaj4/ “peaceful” (initial two segments is the same with the target but with different tone). Result showed that tone affected spoken character recognition while processing the two initial segments. In addition, tone could not affect spoken character processing independently, which might be inconsistent with the assumption that tone is a separate level of representation, called “toneme” node, in the modified TRACE model (Malins & Joanisse, 2010; Ye & Connine, 1999; Zhao et al., 2011).

在台灣，中小企業一直扮演十分重要的角色，對國家經濟成長、創造就業機會有重大貢獻。近年有一群新興族群—微型企業，許多微型創業者在乎能夠結合熱情與專業所長，在自由彈性的工作環境中，實現夢想。小本經營的微型企業與近年來台灣產業環境變遷習習相關，在產業外移、經濟成長緩慢、失業率提升、及台灣產業結構逐步調整之下，沒有合適工作的人、職場中的高薪專業工作者、家庭中的主婦們，形形色色的人開始選擇創業這條路。微型企業主要特徵有二：小規模、低資產。多數微型企業因為規模不大，在面對變動快速的環境，能夠保有靈活應變能力；唯資產不足之原因，讓多數微型企業往往因為資金周轉不靈，而走上失敗或倒閉之途。普遍說來，台灣微型企業面臨「資本額小、體質弱、力量分散」困境。有別於傳統產業與高科技產業的創業，因為規模、產值與創業背景的不同，微型創業者在充滿障礙的創業過程中，每一步不但皆是生死存亡的關鍵，亦沒有想像中容易。創業過程中，創業者面臨機會的決策過程，如何因應創業活動運用既有資源，或進一步尋求所需資源的過程，都是欲踏入微型創業市場的創業者在乎的關鍵點。另，即便是小規模的微型企業，只要有能力辨識機會，依然有能力創造或發掘新市場，創造新價值；或許微型創業者是從自我價值提升開始，但透過營運模式的改變過程，依然可以對社會層次產生新價值。透過本研究，可以瞭解現今台灣社會時興的創業全民運動，微型創業者如何結合所長，因應環境的變化，找到利基市場，取得適用資源，創造新價值，並且瞭解創業動態過程最重要的關鍵因素為何。本研究所要探討的是微型創業者在充滿障礙的創業過程中，機會的辨識與掌握，因應創業過程其創業資源的變化，與微型創業活動價值創造的過程。本研究將Timmons Model(1999) 修改成為「新價值創造」，「創業者」，「機會」，「資源」四部份，依Maes(2003) 所提出之價值創造五元素探討創業者創造新價值之過程，並以Kim(2005) 提出的四項行動工具分析架構，解構新價值創造過程中創業者對機會、資源、與內外在環境的互動過程。 微型創業活動在封閉環境因子與外圍環境因子交互影響之下，形成機會辨識的動態過程，引發創業動機，修正創業活動。有能力從市場上學習產業經驗的創業者或創業團隊，才可以持續提升知識強化與產品內容深度。創業者透過創業活動的修正，逐一建構新創價值。創業過程中透過市場經驗的累積，提升微型企業的產業元素價值；但是創業者必須在高價值產業元素中，考量營運模式的調整，才能真正提升該微型企業的整體價值。因此，「創業者」是微型創業過程中最重要的關鍵元素，由創業者所引發的創業活動最為重要，可能是「創業者與機會」或「創業者與資源」，因產業別不同或當時環境因素的改變，產生對創業活動不同程度的影響。

面對全球性的激烈競爭，現代企業紛紛努力尋找新的資訊科技為企業武器，以求達到提昇作業績效，降低作業成本，縮短作業時間。而RFID是本世紀十大重要資訊科技之ㄧ，廣受企業青睞，舉凡門禁安全管理、貨物追蹤管理、醫療應用，甚至美軍在伊拉克戰爭中的後勤補給支援，都有其應用蹤影。 但些許企業廠商導入之後，其效果不彰，究其原因為企業在導入RFID技術往往忽略了流程整合的重要性，造成結果不如預期。這也代表，縱然導入RDID系統技術需要進行許多的IT 技術研究與規劃，但這不代表可以單純地用「IT 建置轉型」的角度來建置RFID系統，企業還是必須從商業營運的角度來思考RFID能夠創造什麼樣的營運模式。 本研究認為最重要的是企業需要一套具有系統的步驟，針對企業需求與導入RFID失敗之原因，提出以企業流程管理為基礎的RFID導入模式，期望借此一執行模式，幫助企業在導入RFID時，藉由審視目前作業流程，改善、改造作業流程，使RFID與應用流程更緊密結合，達到資訊科技與應用流程整合的最佳效益。 最後，歸納出本研究成果，有下列幾點： 1.以企業流程的觀點出發，建立一個以企業流程管理為基礎的RFID導入模式，詳細說明各步驟之作法以及與應用流程整合的重要性，以提供企業做為參考，降低企業在往後導入RFID的困難，以及獲得最佳效益。 2.採用BPMN為流程模式化標準以及BPMN/BPEL的模式化工具來進行本研究流程之模式化與分析。未來得以藉由BPMN與BPEL之規格對應描述以及使用Web Service的開發工具來建置符合SOA架構之資訊系統。 3.提供血袋處理流程前端之捐血中心未來在導入RFID技術時一個參考模式，以利進行導入。

在資訊科技爆炸的時代，所有的資料都要求能數位化，以便利用資訊科技對數位資料進行分析、整理與應用。對於都市規劃而言，建立數位城市模型即成為目前的重要課題之一。 建立數位城市模型中間最困難的步驟之一，在於處理並數位化古老的紙本地籍資料與建築物平面圖或手繪建築物之地圖，然後進行資訊整合，以建立基本的城市三度空間模型，進而利用更精準的測量技術，來建立精確的數位城市模型。然而要以人工處理並將上述資料數位化來製作基本的三度空間模型，秏時費工且成本太高。有鑑於此，本篇論文提出一套自動化的處理方法，針對附有樓層高度的紙本建築地圖或手繪地圖進行自動化處理，從而建立基本的三度空間模型，作為建立數位城市模型的初步處理。 我們先利用文字辨識的技術對建築物進行分析、擷取並判斷地圖中屬於建築物高度的文字資料。其次利用不同的演算法，對地圖進行細化及骨架粹取，並找出地圖上組成建築物的關鍵節點，然後對節點分群，以區分並判斷不同的建築物，進而建立地圖上各個建築物的平面模型圖。最後將每棟建築物的高度資料及其相對應的平面模型圖加以整合，自動產生該地圖的三度空間模型。 我們隨機選取一張台北地區之建築平面圖以及學校平面圖來檢驗我們提出的方法，測試的結果顯示，我們的方法都能成功的將這些平面圖，自動建立出原圖基本的三度空間模型，可以作為未來建立城市數位模型之參考。 / In the era of information explosion, digital archiving every piece of information becomes a must in order to organize, process, and analyze this information and make further use of the information. Hence, constructing a cyber city model is one of the major issues in urban planning. One of the most difficult steps in constructing a cyber city model is to process and digitize the ancient cadastral information as well as the architecture sketches or the hand drawing maps. By combining this information, we could construct an early stage three dimensional model for the city that would help us in constructing the final model for the cyber city. However, manually processing this information is not cost effectively and automatic processing them might reduce the construction cost dramatically. In this paper, we propose an automatic processing mechanism that could digitize the architecture sketches or the hand drawing maps automatically. Our mechanism will produce an early stage three dimensional model for the specified area that will eventually lead to the construction of a more accurate three dimensional model for the entire city. After the sketches or the maps were scanned, as bitmap images, into the computer, we start with analyzing the architecture sketches and extract the elevation information using traditional methods of character recognition. Then, we use various algorithms to thinning and to extract the skeleton of the image. The critical nodes of each building in the images were identified, isolated, and used to construct the base of each building in a planar diagram. Finally, the elevation information is used along with the planar diagram just constructed to generate an early stage three dimension model for the specified area. We randomly choose an architecture sketch of Taipei City and our campus map to verify our mechanism. The results show that our method could produce the corresponding three dimensional models successfully. These models could be used and help us to construct a more accurate three dimensional model for the entire city.

台灣科技公司以代工為主，隨著代工的毛利逐漸降低，如何進行轉型是每個企業都想瞭解的，但往往會面臨到一個關鍵的問題：什麼才是好的創業機會?要如何辨識機會?如何在複雜變動的環境中發掘、辨識出創業機會(Entrepreneurial Opportunities)的本質。 為此本研究試圖回答以下三個問題： 1. 本文將會進行個案公司在創業機會辨識整體過程的探討，公司在轉型中，影響機會辨識的來源有哪些？如何才能擴大機會辨識的廣度？ 2. 公司在整體創業的過程中，機會辨識的標準是否相同？ 3. 機會選擇間是否會互相影響？如何藉由機會辨識的結果回饋到未來的產品規劃？ 為了回答上述三個問題，本研究將先探討個案公司如何發現轉型的契機，以及在此時期辨識出的機會特徵有哪些，建立了什麼能耐，再探討龍彩科技機會評價過程，最後觀察龍彩科技從機會評價中獲得哪些回饋，以及其對日後產品規劃的影響。最後，本研究提出以下結論： 1. 跨領域的嘗試，有助於思考許多化阻力為助力的商業模式，進而增加機會辨識的廣度。 2. 創業警覺性除了會影響創業者對於機會的辨識程度，也會發展出獨特的商業模型思考。 3. 隨著創業階段的不同，對於機會特徵的認知有所不同，在重要性上也會有所差異。 4. 機會之間並非獨立存在，而會互相影響，所以不能單看個別機會的評價作選擇。

近年來，很多要求高安全性的網站都使用扭曲變形的英文或數字字串作為辨識碼，以避免網站或系統受到大量暴力的攻擊。微軟公司則於2007年提出以貓狗影像的新辨識碼系統—Asirra。對於電腦而言，貓狗影像辨識較字串更為困難。本研究主要針對Asirra的影像資料試圖建構出貓狗影像自動辨識法，藉此來了解此辨識碼系統的有效性。已知影像包含大量雜訊，若使用原始資料則計算困難而且辨識效果差，所以萃取關鍵特徵為重要的研究課題。本文考慮方向梯度直方圖法 (Histograms of Oriented Gradients, HOG) 以及主成分分析 (Principal Components Analysis, PCA) 來篩選重要變數。我們將運用挑選出的特徵建立支持向量機 (Support Vector Machine, SVM) 分類器。在實證分析中，我們發現結合此兩種特徵萃取法，除了能夠大幅降低運算時間，也能得到良好的預測正確率。 / In recent years, many websites, which requires a high standard of security, use CAPTCHA to avoid mass and brutal attacks from hackers. The CAPTCHA considers the use of strings of twisted and deformed English letters or numbers as an identification code. In 2007, the company Microsoft proposed a new image-based recognition system-Assira, which uses dogs and cats images as an identification code. Dogs and cats image recognition is not more difficult than strings of letters or numbers recognition for human, but is more challenging for computers. In this paper, we aim to develop a classification method for images from Asirra. An image is represented by an enormous number of pixels. Only few pixels carry important feature information, most pixels are noise. The abundance of noise leads to computational inefficiency, and even worse, may results in inaccurate recognition. Therefore, in this problem feature extraction is an essential step before a classifier construction. We consider HOG (Histograms of Oriented Gradients) and PCA (Principal Components Analysis) to select important features, and use the features to construct a SVM (Support Vector Machine) classifier. In the real example, we find that combining the two feature detection methods can dramatically reduce computational time and have satisfactory predictive accuracy.

隨著網際網路的蓬勃發展，資訊擷取(Information Extraction)已經成為一個非常重要的技術。資訊擷取的目標為從非結構化的文字資料中，為特定的主題整理出相關之結構化資訊，其所牽涉的問題，包括分析文件的內容，篩選、擷取出相關的文字及其對應的意義。到目前為止，大部份的資訊擷取系統都著重在英文文件上，對於中文文件資訊擷取技術的研究才正在如火如荼的展開，加上全世界至少超過1/5的人說中文，積極投入中文資訊擷取的研究就顯得非常重要。 中文的描述方式與英文有著很大的不同。在英文，詞跟詞之間有著明顯的『空白』，電腦可以很輕易的區隔輸入字串中每個詞。但是在中文，詞跟詞之間並沒有明顯的界限，一般的處理情形為利用詞典，將一個輸入字串中的文字，比對詞典內的詞來當做斷詞的依據，不過由於字組成詞的變化程度相當大，斷詞錯誤的情形仍很可能出現。因此，在本篇研究論文我們提出不做斷詞、不做詞性分析，而利用『型態辨識』的方法搭配『有限狀態自動機』的運作方式，來處理中文資訊擷取的問題。在實驗方面，我們以『總政府人事任免公報』當作測試資料，其精確度高達98%，而回收率也達到了97%。此外，我們也應用到其他不同的資料領域，對於建立跨領域之中文資訊擷取系統有了初步的研究進展，充分印證了本資訊擷取方法處理中文資訊擷取問題的可行性。 / With the explosion of World Wide Web, information extraction has become a major technical area. The goal of information extraction is to transform non-structured text into structured data of specific topic. It involves analyzing, filtering and extracting relevant parts of text and the corresponding meaning. Most information extraction research mainly focuses on English text. On the other hand, research on Chinese information extraction has not received as much attention. Considering the fact that one-fifth population in the world are Chinese-speaking people, Chinese information extraction technology will become increasingly important. Chinese language is different with English in many aspects. In English, words are separated with space such that computers can easily distinguish each word in the input string. In Chinese, there are no spaces between characters to segment them into meaningful words. A general solution is to match characters of the input string to the words in the dictionary to find proper word boundary. Yet, much flexibility and ambiguity exist in the combination of characters into words. Many errors may occur in word segmentation. . In this thesis, we propose an approach to Chinese information extraction based on pattern matching and finite state automata, without relying on word segmentation and part-of-speech tagging. The approach was evaluated with “government personnel directives in official gazettes” as test data, and it achieved performance measure of 98% precision and 97% recall. Moreover, the approach was extended to other data domains. The results have showed initial progress on the research of multiple- domain Chinese information extraction system.