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| 研究生： |
劉嘉瑜 |
|---|---|
| 論文名稱： |
中心對稱式延展區域三元化圖型特徵描述子 Center-Symmetric Extended Local Ternary Patterns |
| 指導教授： | 廖文宏 |
| 學位類別： |
碩士
Master |
| 系所名稱： |
理學院 - 資訊科學系 |
| 論文出版年： | 2014 |
| 畢業學年度： | 102 |
| 語文別： | 中文 |
| 論文頁數： | 65 |
| 中文關鍵詞： | 延展式區域三元化圖型 、中心對稱式延展區域三元化圖型 、混合式描述方式 、物件辨識 、特徵描述子 |
| 相關次數： | 點閱：140 下載：27 |
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物件辨識是電腦視覺領域中相當重要的一環，在許多應用中皆可看到物件辨識的身影，而物件辨識的關鍵在於描述物件特徵的描述子。本論文基於「延展式區域三元化圖型」，提出一種新的特徵描述子，稱為「中心對稱式延展區域三元化圖型」，改善繁複的編碼過程，在辨識力、抗噪力，以及處理效率三者之間達到良好的平衡。除此之外，我們也將不同描述子特性加以融合，稱為「混合式描述方式」，實驗結果證實在高雜訊的材質影像中，「混合式描述方式」提升了辨識力以及抗噪力。
Object recognition is an important problem in computer vision. Effective recognition of objects calls for the appropriate selection of feature descriptor. In this thesis, we generalize the “Extended Local Ternary Patterns” (ELTP) to form a novel and compact set of features named Center-Symmetric Extended Local Ternary Patterns (CS-ELTP). The newly defined CS-ELTP requires a simplified encoding procedure and has a lower dimension for a fixed neighborhood region. It achieves good balance among feature dimension, recognition rate and noisy resistance according to our comparative experimental analysis. In addition, we combine binary and ternary patterns to create a hybrid descriptor that possesses the characteristics of both types of descriptor. Experimental results indicate that the hybrid descriptor can improve the performance in noisy conditions while maintaining a reasonable feature size.
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 1
1.3 論文架構 3
第二章 相關研究 4
2.1 Local Binary Patterns, LBP 4
2.2 Center-Symmetric Local Binary Patterns, CS-LBP 8
2.3 Local Ternary Patterns, LTP 9
2.4 Extended Local Ternary Patterns, ELTP 10
2.5 Extended Center-Symmetric Local Ternary Pattern, eCS-LTP 12
2.6 其他各種降維方式 13
2.6.1 合併直方圖相鄰樣式 13
2.6.2 減少樣本數量 14
2.6.3 旋轉不變的特性(rotational-invariance) 14
第三章 中心對稱式延展區域三元化特徵描述子 16
3.1 中心對稱式延展區域三元化特徵描述子 16
3.2 CS-ELTP中的Uniform Patterns 17
3.3 Spectral Clustering分群演算法 18
3.4 分類方法及材質影像來源 19
3.5 CS-ELTP的Uniform pattern 20
3.5.1 Uniform pattern代表的意義 20
3.5.2 LBP中的Uniform pattern 22
3.5.3 CS-ELTP中的Uniform pattern 23
第四章 材質影像分類實驗 27
4.1 原始取樣定義描述子的材質分類 27
4.2 經過降維描述子的材質分類 30
4.3 抗噪力實驗 33
4.3.1 抗噪力實驗一：材質加入雜訊強度20dB的高斯雜訊 33
4.3.2 抗噪力實驗二：材質加入雜訊強度40dB的高斯雜訊 35
4.3.3 抗噪力實驗三：比較降維前後的抗噪力 37
4.3.4 辨識錯誤原因探討 40
第五章 混合式描述子的融合實驗 49
5.1 融合的方式 49
5.2 未降維描述子融合實驗 51
5.3 經降維描述子融合實驗 53
5.4 調整混合式描述子融合比重 55
第六章 結論與未來研究方向 60
參考文獻 66
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