| dc.contributor.advisor | 何瑁鎧 | zh_TW |
| dc.contributor.author (Authors) | 李俊達 | zh_TW |
| dc.creator (作者) | 李俊達 | zh_TW |
| dc.date (日期) | 2006 | en_US |
| dc.date.accessioned | 17-Sep-2009 14:07:04 (UTC+8) | - |
| dc.date.available | 17-Sep-2009 14:07:04 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Sep-2009 14:07:04 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0917530363 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32714 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 91753036 | zh_TW |
| dc.description (描述) | 95 | zh_TW |
| dc.description.abstract (摘要) | 本論文的目的是利用人臉在彩色影像中所提供的多色彩空間資訊，來達成在變異度較大的光源中即時偵測人臉的任務。彩色影像所擁有的原始RGB色彩資訊，經過轉化到正規RGB以及HSV (色調、飽合、明度)等色彩空間後，擁有對光源變化反應減緩的特性。以此特性為基礎，在４個選定的色彩空間中定義８種不同的類赫爾特徵(Haar-like feature)，再利用推進演算法（Boosting algorithm）選出重要性最高的幾組特徵來進行對人臉的特徵。實驗結果顯示依此方法所產生的辨識器可在２點多秒內處理近百萬個次窗口(sub-window)，並對光源變化有相當程度的抵抗力。 | zh_TW |
| dc.description.abstract (摘要) | The main goal of this thesis is to detect human face under varying lighting condition by utilizing multiple color space information in real-time. Images of RGB color space can be converted into normalized RGB and HSV color spaces and thus reduce the interference of lighting condition. Base on this mechanism, we define 8 Haar-like features inside 4 selected color spaces, and then select the important features with boosting algorithm. Experimental results show that detectors constructed with our approach are able to process nearly one million sub-windows within 2.4 seconds, being robust to the changes of lighting conditions. | en_US |
| dc.description.tableofcontents | CHAPTER 1 Introduction 11.1 Previous Detection Approaches 21.2 Proposed Approach 31.3 Organization of the Thesis 4CHAPTER 2 Related Work 62.1 Haar-like Feature 62.2 Skin-Color Based Detection 62.3 Color Space 82.3.1 RGB 82.3.2 CMY and CMYK Color Space 92.3.3 HSV Color Space 112.3.4 Device-Independent Color Spaces 112.3.5 YUV YCrCb and YIQ Color Space 12CHAPTER 3 Rectangle Feature 143.1 Feature Type 153.1.1 Haar-like Feature 153.1.2 DC Color Feature 163.2 Color Space 173.2.1 Normalized RGB Color Space 173.2.2 HSV Color Space 183.3 Proposed Feature 203.3.1 Rectangle Feature Set 203.3.2 Color Conversion 21CHAPTER 4 Detection Framework 254.1 Weak Classification Function 254.2 AdaBoost Algorithm 274.3 Cascade Classifier 30CHAPTER 5 System Framework 335.1 Rectangle Feature 345.2 Training Data 355.3 Weak Learner 365.4 Weak Classifier 365.5 Learning Algorithm 375.6 Strong Classifier 375.7 Cascade Classifier 38CHAPTER 6 Experiment 406.1 Feature Definition 406.2 Implementation 416.2.1 Preprocessing 416.2.2 Cascade Construction 446.3 Detection Result 46CHAPTER 7 Discussion And Conclusion 52Reference 54 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0917530363 | en_US |
| dc.subject (關鍵詞) | 色彩空間 | zh_TW |
| dc.subject (關鍵詞) | 人臉偵測 | zh_TW |
| dc.subject (關鍵詞) | 特徵 | zh_TW |
| dc.subject (關鍵詞) | color space | en_US |
| dc.subject (關鍵詞) | face detection | en_US |
| dc.subject (關鍵詞) | haar-like feature | en_US |
| dc.title (題名) | 彩色影像中的人臉偵測 | zh_TW |
| dc.title (題名) | Face detection in Color Image | en_US |
| dc.type (資料類型) | thesis | en |
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