研究生資訊 | | ||
級別 | 92 | ||
姓名 | 邱淵明 | ||
羅馬拼音 | Yuan-Ming Qiu | ||
指導老師 | 1 | ||
哪裡人 | 台灣 | 學號 | M92G0103 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 以漸進概念引導為搜尋策略之關聯回饋影像內容檢索系統 | ||
論文摘要(中文) | 以內容為基礎的影像檢索(Content-Based Image Retrieval, CBIR)這幾年來成為一個廣泛受到重視的研究課題。一般說來，當使用者以某張影像當作檢索標的時，CBIR系統首先會計算影像中的低階特徵值，並以這些特徵值來當做檢索依據。而系統將試圖在特徵空間中找出靠近查詢圖片之特徵點範圍附近的影像。然而由於低階影像特徵與高階的影像內涵之間存在的語義落差 (semantic gap)，因此檢索結果並不能盡如人意。為了改進檢索結果，許多系統均要求使用者透過對於初次檢索結果與檢索標的之間關聯性以人工方式進行適當評分，如此便可引導系統下一回合檢索的搜尋方向。此種常用的方法即所謂關聯回饋(relevance feedback)。通常使用的關聯回饋是基於查詢點移動(query point movement, QPM)的概念。當使用者對於檢索結果標記相關與不相關時，系統將會移動其查詢點朝向正向相關的特徵向量並遠離負向相關的特徵向量。QPM之主要的缺點為若所有正向相關的檢索結果散佈於數個影像語義的群組中，則下一個回合的檢索方向是這些特徵向量的總和，也因此常常得到更不相關的檢索結果。 在本論文中我們提出一個漸進式的概念導引搜尋策略來進行以內容為基礎的影像檢索。首先我們先將特徵空間以色彩與紋路特徵來當做依據，並以K-means將特徵空間分割成數個子空間。首次的檢索結果依據每一個子空間或稱影像群組的大小比例，以類別瀏覽方式來分配回傳的影像張數。這樣的做法在於讓使用者瞭解目前資料庫包含哪幾種影像語義，並且可以藉由觀察使用者之後的關聯性標記，推測出使用者的檢索要求。一但使用者對於初次檢索結果標記了正向或負向關聯後，我們導入了多查詢點(multi-query)平行處理與模擬退火(simulated annealing)機制來取得下一回合的檢索結果。利用多查詢點平行處理可以提升搜尋準確率。若某一影像群組中含有越多的正向關聯，則此一群組將有更高的機率可以回傳更多的影像。而每一張影像是否被回傳的機率除了取決於其鄰近正向關聯點的多寡外，模擬退火的觀念也被引用。每一張影像被當作回傳影像的機率會隨著使用者的關聯回饋次數增加而逐漸趨於穩定的狀態。 本論文以Java語言實作出一個可以線上操作的影像檢索系統。實驗結果顯示本論文所提之方法的確能夠有效地克服以往QPM的缺點，讓使用者以概念指引方式逐步引導系統搜尋適合的影像。我們同時測試其他的效能指標如平均精確率(average precision rate)、平均回想率(average recall rate)、以及正規化排名總和(normalized rank sum, NRS)等以驗證本系統的效率。 | ||
論文題目(英文) | A Progressive Conception Guided Searching Scheme w | ||
論文摘要(英文) | Content-based image retrieval (CBIR) has become a significant research topic in the visual computing area. Generally speaking, when a query image is submitted to the CBIR system, the features of the query image have been extracted and compared within the image database. Intuitively, the system should return the relevant images near the query images in the feature space. However, the gap between the low-level feature and the high-level semantic meaning of an image makes the retrieval result unsatisfactory. To improve this situation, a user intervention technique called relevance feedback is adopted. The most commonly used relevance feedback is called 「query point movement」 (QPM). The QPM, which means to move the query point to the desired location by using the Rocchio's formula. When the user marks the relevant and non-relevant images on the retrieval images, the QPM moves the succeeding queries away from the negative examples and proceeds towards the positive examples. The QPM has a major drawback that if the marked positive examples are scattered across many clusters; the new query is a mixture of the old queries and therefore an unsatisfactory non-relevant result is caused. In this thesis, we propose a progressive conception guided searching scheme based on the relevance feedback framework. We first perform the feature space subdivision by using K-means algorithm according to the color and texture features. This operation divides the image database into several feature clusters. The retrieved image in the first querying step is constructed in accordance with the proportion of the cluster sizes. This step is called 「first feedback」, which aims to give the user the overview for all images.with category browsing. In the following steps, the user marks some positive and negative examples on the returned images. In order to avoid the mixture of the positive examples that mislead the query direction, the parallel searching mechanism called 「multi-query」 for each positive example is performed. That is, we treat every positive example as a new query point. The clusters with more positive examples would have the larger probability to return more images. Therefore, the relevance feedback which uses positive and negative examples evaluated by the user is utilized to improve the indexing performance. The probability of an image which is returned as a relevant example is computed by means of interpolating the scores given by user during the feedback stage. To achieve a stable retrieval result, we employ simulated annealing as a decision-making mechanism. The proposed method is implemented with an on-line image retrieval system which allows user to retrieve the proper images via a sequcence of progressive relevance feedbacks. The experimental result shows the multi-query mechanism avoids the drawback of the misleading of the query direction. Other performance evaluations such as APR (average precision rate), ARR (average recall rate) and NRS (Normalized Rank Sum) are also carried out to verify the efficiency of the proposed method. | ||
關鍵字 | |||
| 以內容為基礎之影像檢索 ; 關聯回饋 ; 影像處理 ; | |||
研究生資訊 | | ||
級別 | 92 | ||
姓名 | 王瀚 | ||
羅馬拼音 | Han Wang | ||
指導老師 | 1 | ||
哪裡人 | 台北 | 學號 | M92G0201 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 以基因演算法為基礎之不完整橢圓近似物體偵測 | ||
論文摘要(中文) | 橢圓是經常出現在影像中的基本形狀。在電腦視覺與影像處理文獻中，一般使用霍氏轉換（Hough Transform）來偵測橢圓。使用霍氏轉換的優點在於不受邊線不連續或雜訊的影響，但運算速度太慢及需要消耗大量儲存空間為其缺點。 在本文中我們利用橢圓上的五個點可以決定一個橢圓的幾何性質，使用基因演算法從已分類過後的邊線點（edge point）中隨機挑選五個點組成一個染色體（chromosome），並以這五點是否可組成凸邊形（convex hull）來判定這五點是否能組成完整橢圓。之後我們利用最小平方法（Least Square Method）計算出近似的橢圓，經過基因演算法之交配及突變運算來改變組成染色體的點，使近似的結果可以更符合實際的橢圓。 我們所提出的方法可找出影像中不定數量的橢圓，在速度上也比霍氏轉換快；除此之外，藉由調整收斂條件，也可偵測出不完整的橢圓。 | ||
論文題目(英文) | A Genetic Algorithm Based Incomplete Elliptic Obje | ||
論文摘要(英文) | Ellipse is the fundamental geometrical form that usually appears in images. Traditionally, Hough transform is the commonly used method to detect ellipse in computer vision and image processing. Hough transform has the advantage that not to be interfered by discontinuous edges or noise. However, the slow computing speed and the high storage capacity limit the application of this method. In this thesis, an incomplete elliptic object detection based on genetic algorithm is proposed. This method applies the geometrical property that five points would compose an ellipse to construct a chromosome in genetic algorithm. The convex hull constructed by these five points is further used to determine whether it could compose a complete ellipse or not. The least-square method is applied to calculate the approximated ellipse. The ellipse location is changed during the convergence process by the crossover and mutation operators. The proposed method can detect various numbers of ellipses in image. The computing speed is faster than Hough transform. On the other hand, the incomplete ellipses can also be detected by adjusting the convergence condition. | ||
關鍵字 | |||
| - -橢圓偵測 ; 霍氏轉換 ; 最小平方法 ; 基 | |||
研究生資訊 | | ||
級別 | 93 | ||
姓名 | 蔣佳欣 | ||
羅馬拼音 | - - | ||
指導老師 | 1 | ||
哪裡人 | 高雄 | 學號 | M93G0214 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 室內/戶外與建築物/自然風景之影像分類研究 | ||
論文摘要(中文) | 以影像內容來進行相片之檢索與分類最近得到廣泛的重視。最常使用的方法是利用相片之低階影像特徵或是語義註釋來分類。然而這些方法都還不能很準確的進行相片場景分類。因此若是加上相片中的所內含的EXIF資訊，將可提升分類之準確率。EXIF所包含的的閃光燈（Flash Fired）、焦距（Focal Length）、曝光時間（Exposure Time）在分辨室內/戶外相片上具有顯著的辨識力。本篇論文中將整合相片的低階影像特徵值與EXIF資訊，輔以倒傳遞類神經網路（Back- propagation neural network）來進行相片室內/戶外分類。 經過第一步驟戶外/室內的分類後，我們將再針對戶外的相片做進一步的辨別。因為戶外的相片大都包含建築物或是自然風景，而建築物相片相較於自然風景照，其線條構造大都充滿規則性。因此我們將可利用邊線（edge）來作為主要的辨別特徵。首先利用canny edge method找出邊緣（edge），再計算出邊緣的角度，繪製成邊緣直方圖（edge hitogram）。因建築物大都由垂直與水平線條構成，所以繪製出的邊緣直方圖，在接近垂直與水平的邊緣角度上，會呈現出雙峰的特性。因此我們可利用此特性來辨別建築物或是自然風景。 | ||
論文題目(英文) | A Study on Indoor/Outdoor and Building/Landscape D | ||
論文摘要(英文) | The content-base image retrieval and classification has obtained more attention for the recent application. The most commonly used methods are applying low level image features or keywords to classify the image contents. However, these methods are not very accurate. In this thesis, we try to utilize the additional information of digital pictures to raise the classificatoin accuracy. The EXIF information such as Flash Fired, Focal Length and Exposure Time, are the embedded information to record the image information during the picture capture moment. With the additional EXIF information and the low level image features, we can easily classify the pictures from the indoor and outdoor scenes. The back propagation neural network here plays a classifier to recognize the indoor and outdoor scenes. We will proceed the building/landscape classification from the outdoor scene pictures. The most signification feature of a building picture is the amount of the vertical and horizontal edges. First, we use canny edge operator to detect the edges of the picture. Then we calculate the angle of edge and form a edge histogram. The experiment shows the edge histogram of a building picture is bi-modal, i.e. the distribution shape looks like a 「W」. The experimental results shows the proposed methods can efficiently classify the digital pictures from the indoor/outdoor and building/landscape scenes. | ||
關鍵字 | |||
| 室內/戶外相片分類 ; 倒傳遞類神經網路 ; 數位相片內 | |||
研究生資訊 | | ||
級別 | 93 | ||
姓名 | 蔡承穎 | ||
羅馬拼音 | Cheng-Ying Tsai | ||
指導老師 | 1 | ||
哪裡人 | 台南 | 學號 | M93G0201 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 數位相片之人物註釋與辨識系統 | ||
論文摘要(中文) | 輕巧的?位影像擷取設備如?位相機與具備照相功能的手機大?普及，使得?位影像的產生十分??。而相對而?所造成的龐大影像資?之管?、註釋 與檢?也變成研究的重要課題。 本篇?文中，我們針對相片中人物的註釋與辨?問題，以一種半自動的方式提出一個初步可?的解決方法。在人臉偵測及人臉辨?的相關研究中，所需加強的是準確?的提升；而輸入影像的前處?步驟及輸入影像的品質也?可能明顯影響後續處?人臉偵測及辨?的準確性。在本?文中，我們首先尋找相片中人臉部位，?用非線性的YCb'Cr'色彩空間轉換?找出膚色位置，再?用人臉上的特徵?分析判斷此區塊是否為人臉部位。在此我們假設人們在拍攝一系?的相片時，他們所穿的衣服?是?變的，因此衣服的顏色可以用?當作人臉的一種特徵。確定人臉位置後，我們擷取臉部下方的衣服區塊的色彩值?當作人物註釋與辨?的依據。???使衣服區塊因顏色眾多而影響辨?，我們?用K-means 分群法分析此區塊，選出區塊中顏色比重最多者作為樣本特徵。之後再以LVQ 演算法重複訓?，使衣服區塊之顏色樣群分佈?加明確。此時之衣服顏色?可用?當作確認辨?人物的特徵。 我們實作出一個具有註釋能?的?位相片管?系統以驗證本?文所提方法之可?性。系統的操作?程如下所述。首先系統自動定位人臉位置，使用者可將人物名字或描述文字註釋到相片中的人臉位置。在?續拍攝的相片集中，系統會依據之前相片所儲存的人臉衣服對應關係，偵測並辨?相片中的每一個的人臉位置；並根據資?庫中的特徵，對於人臉加以自動註釋。?使用者發現註釋錯誤，則可針對錯誤加以?新。?新後的資訊將會加入資?庫內，用以修正人臉與衣服的對應關係。重複以上述方法，我們將可建構出人物影像特徵與人物?明資?庫，以提供大?相片所需的註釋與管?功能。實驗結果顯示，本?文所採用之影像特徵與其他的註釋項目相互搭配後，可提供影像檢?與相片管?作業中?好的註釋依據。 | ||
論文題目(英文) | The Human Annotation and Recognition System for Di | ||
論文摘要(英文) | The handy equipments such as digital cameras and cellular phones with camera function provide a convienient way to capture images. Therefore, the additional demands such as image management, image annotation, and image retrieval have become the major issues. In this thesis, we will focus ourself on the face annotation problem and propose a feasible solution. The major concern in face detection and recognition problems is the accuracy in recognizing the people in the photo with the exact identity. Many factors could influence the accuracy of face detection and face recognition. In this thesis, we propose a semi-automatic approach to solve these problems. First, we use a non-linear YCb'Cr' color space transformation to detect the location of skin color, and then we search the face positions by using the geometric properties of faces. Here we make an assumption that people generally wear the same clothes in taking a series of photos. While the faces are located, the RGB values of the clothes beneath the faces are used as the features to represent the faces. In order to simplify the color feature of clothes, we use K-means clustering to analyze this cloth block and select the major color as the cloth feature. The LVQ algorithm training process is repeated to make the color distributing between different cloth blocks more clear. We have developed a sytem to verify the proposed method. The sytem is performed as follows. First, the system automatically locates the face position and computes the corresponding cloth feature. User gives a name or text description to this face, the correspondence between the face and the annotation text has been established. The system will detect the face positions in a series of photos and give the detected faces the existent annotation text. If the annotation does not match to the detected faces, user can add a new annotation or correct the wrong matchings. The experiment shows the proposed method can achieve a good performance in face annotation and recognition. It also provides a convient tool to manage and retrieve the huge amount of digital photos. | ||
關鍵字 | |||
| 人臉偵測 ; 臉部識別 ; 學習向量演算法 | |||
研究生資訊 | | ||
級別 | 93 | ||
姓名 | 林彥廷 | ||
羅馬拼音 | - - | ||
指導老師 | 2 | ||
哪裡人 | 台灣 | 學號 | M93G0203 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 數位學習環境下以決策樹為基礎之線上題庫難易度自動分級 | ||
論文摘要(中文) | 隨著網際網路及多媒體資源的蓬勃發展，現今的生活已經和網路息息相關，許多人們經由網路進行溝通及獲取知識，溝通變的無國界，學習更是能夠隨時隨地進行，也因此引發了數位學習 (E-Learning)的熱潮。測驗與評量在學習的領域中一直扮演著重要的角色，其測驗的結果可以反應出學習者的學習成效，讓教師及學習者瞭解是否已達到了學習目標；教學內容需要因材施教，測驗評量也應該如此，一般測驗評量的試題內容不會因為不同的學習者而有所不同，也就是說，不論學習者的學習成效為何，在進行測驗時其測驗的試題內容都是相同的，這樣的測驗並不能有效的瞭解學習者的學習成效，因為不同的測驗試題對於不同的學習者而言，其難易度不盡相同，因此，要如何建構出良好的個人化難易度分級的測驗試題來有效的瞭解學習者的學習成效，是目前需要積極探討的議題。 本篇論文主要是建構出完整的數位測驗平台，讓教師與學習者能夠輕鬆的透過此數位測驗平台完成線上測驗的動作，並且透過決策樹歸納法的應用提出題庫難易度自動分級系統架構，此架構是依照每位學習者的個人背景資料來進行決策樹的建構，以達到個人化測驗試題難易度自動分級的目的，讓每位學習者能夠進行個人化的測驗來瞭解其學習成效，進而達到提高學習者學習效率的目的。應用決策樹歸納法重視的就是其預測訓練集合 (Training Set)以外的資料集的準確率及過度配適 (Over-fitting)的問題，在本篇論文中會針對所建構完成的決策樹探討此問題。 | ||
論文題目(英文) | Automatic Question-Leveling System for E-Learning | ||
論文摘要(英文) | Over the past years, due to the rapid growth of information and communication technologies, the education environment has being enriched with those technologies and become more diversified. Obviously, the Internet-based environment has been rapidly replacing the traditional lecture-based one due to the factor of providing the multimedia-enhanced learning anytime and anywhere. Therefore, we have witnessed an explosive growth of e-Learning. More and more learning contents have been published and shared over the internet. Therefore, how to progress an efficient learning process becomes a critical issue. In this paper, we propose an automatic question-leveling system for e-Learning examination pool using the algorithm of the decision tree induction. The automatic question-leveling system is built to automatically level each question in the examination pool according its difficulty. Thus, an e-Learning system can choose questions that are suitable for each learner according to individual background. And pruning technique is developed to eliminate the dangers of over-fitting in order to select a tree with good prediction performance. | ||
關鍵字 | |||
| 數位學習 ; 決策樹 ; 數位測驗 ; 個人化 | |||
研究生資訊 | | ||
級別 | 93 | ||
姓名 | 蘇志偉 | ||
羅馬拼音 | Chih-W | ||
指導老師 | 2 | ||
哪裡人 | 台灣 | 學號 | M93G0207 |
個人網站 | |||
論文發表 | |||
論文題目(中文) | 行動網頁內容之智慧型處理系統 | ||
論文摘要(中文) | 近???動裝置(Mobile Devices)大?普及化，許多的功能被附加在?動裝置上，其中可以上網的?動裝置也越?越普遍，使其?動裝置可不侷限於地方上的限制，透過無線網?讓?動裝置使用者得到所需要的資訊，但因礙於?動裝置畫面和介面上的問題，使得網頁無法正常的顯示在?動裝置上，造成使用者瀏覽上的?方?，此外行動裝置透過無線網路來上網時，也會有許多的因素造成傳送檔案的不穩定，而降低了傳輸上的能力。 在此本研究基於上述的原因，提出一個於?動裝置使用的網頁處?系統，此系統建構於伺服端上，?動裝置使用者為客戶端，當?動裝置使用者瀏覽網頁時，由伺服端先行處理網頁內容的動作，其中包含網頁中文字和圖片的處理，讓?動裝置使用者在瀏覽網頁時，不會因為?動裝置的各個限制因素，而限制住瀏覽網頁的方式，讓該網頁裡的資訊在行動裝置上做一個較完美的呈現。而本系統架構也加入到數位教材製作工具上，讓製作好的網頁教材透過本架構轉換成行動裝置上所使用的教材，讓老師在製作教材的同時，可不需另外製作行動教材，因而節省製作教材的時間和免於教材資源的浪費。此外系統中加入智慧型下載建議的模組，將影響無線網路的因素利用決策樹做決策分析，由決策的結果可以知道當時的環境是否適合於下載檔案，並且提醒該使用者。 網路上含有大量的資源，透過本系統可讓這些資源在各個裝置上正常瀏覽，瀏覽同時搭配下載建議的使用，可讓使用者在使用?動裝置瀏覽網頁內容時，在最短的時間裡將內容正確的呈現於?動裝置上，在未來系統架構上還可以增加相對應的機制，讓網頁能夠精簡而又包含重要資訊，使其使用者能夠更輕易的在?動裝置上瀏覽任何資訊。 | ||
論文題目(英文) | Intelligent Web Browsing System for Mobile Devices | ||
論文摘要(英文) | Recently, mobile devices become more and more popular and lots of functions are added into. Because the capability of internet access on mobile device is common today, the limit of internet coverage is unrestricted now. Through wireless mobile devices, users could get the information all they need. However, the screen of mobile devices are so small that it is difficult to browse the web pages; moreover, while users are accessing internet through wireless networks, file transmission is often unstable. In this paper, we propose a scheme to provide intelligent download suggestions for mobile learning contents using artificial intelligence. To deliver learning content via mobile devices, including PDA's, cell phones, pocket PCs, or other mobile devices, the file size must be considered. The success of downloading a large file depends on the Internet traffic at the moment. According to the induced decision tree, we can predict whether the downloading would be successful. If not, the server would suggest the user not to download the large file at the moment or to download an alternative file. There are a lot of resources on the web. Through this system, we can browse these materials on each mobile device normally. Cooperate with the download suggestions, users will observe the correct information on their mobile devices in a minute. In the future, the system will develop more corresponding functions. It not only makes the web pages simple and clear, but also let users browse any information on moblie device easily. | ||
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| 內容轉換 ; 行動裝置 ; 決策樹 ; 數位學習 | |||
研究生資訊 | | ||
級別 | 94 | ||
姓名 | 陳秉斌 | ||
羅馬拼音 | - - | ||
指導老師 | 1 | ||
哪裡人 | 台南 | 學號 | m94m0201 |
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級別 | 94 | ||
姓名 | 劉炳宏 | ||
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指導老師 | 2 | ||
哪裡人 | 台灣 | 學號 | m94g0211 |
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級別 | 95 | ||
姓名 | 林暉庭 | ||
羅馬拼音 | Hui Ti | ||
指導老師 | 1 | ||
哪裡人 | 台灣 | 學號 | m95g0101 |
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級別 | 95 | ||
姓名 | 唐祥恩 | ||
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指導老師 | 1 | ||
哪裡人 | 台灣 | 學號 | m95g0223 |
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級別 | 95 | ||
姓名 | 黃高彬 | ||
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指導老師 | 2 | ||
哪裡人 | 台南 | 學號 | m95g0218 |
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級別 | 95 | ||
姓名 | 蔡煒志 | ||
羅馬拼音 | - - | ||
指導老師 | 2 | ||
哪裡人 | 台灣 | 學號 | m95g0213 |
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級別 | 96 | ||
姓名 | 簡境男 | ||
羅馬拼音 | - - | ||
指導老師 | 0 | ||
哪裡人 | 高雄 | 學號 | |
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級別 | 96 | ||
姓名 | 巫紘碩 | ||
羅馬拼音 | - - | ||
指導老師 | 0 | ||
哪裡人 | 台中 | 學號 | |
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