臺灣博碩士論文加值系統

人臉偵測主要用來判斷影像中是否存在人臉，並進而辨識其身份。人臉偵測在物聯網的應用上非常的廣泛，像是進出大樓的身分認證與海關的出入境等。然而，人臉偵測系統中的影像處理電路因為電路的高溫、電源切換、資料傳輸、製程上的缺陷以及電路老化等都極有可能使待偵測的影像出現雜訊和錯誤。所幸這些雜訊和錯誤不一定會造成人臉偵測系統失效，舉例來說，若是人臉的結構未因此而被嚴重破壞，則人臉極有可能仍可偵測。換句話說，人臉偵測系統具有容誤特性，有些錯誤仍可接受，而系統也可繼續使用，而延長其使用壽命。
在本論文中我們仔細分析了人臉偵測系統之容誤程度，並發現相較於人類視覺感受，人臉偵測系統具有更高之容誤程度。我們也提出一高效率容誤測試方法，來檢測錯誤之可接受度，可用來評估人臉偵測系統之可靠度。利用5730張錯誤影像所進行實驗的結果顯示此方法有高達97.12%的準確率。
此外，本論文也更進一步提出適用於人臉偵測系統的影像修復方法，以大幅提升錯誤影像之可接受度，並進而增加人臉偵測系統的可靠度。文獻中雖已有一些相關研究，但大部分為針對影像中的雜訊進行修復，並無考慮影像出現錯誤之情況。雖然也有少量研究針對錯誤影像，但其假設影像中僅會出現單一位元發生錯誤。在本論文中，我們不僅針對錯誤影像，也考慮錯誤可能出現多個位元錯誤之情況。我們也探討了過去的方法在處理此情況時將無法適用之議題，並提出一更加強大之方法來有效修復錯誤影像。值得一提的是，我們所開發的方法具有極佳適應性，可根據影像之錯誤程度動態調整適當的修復方法，使品質高的影像仍然維持其品質，品質中等的影像則可大幅增加品質，而品質已過糟之影像則不進行修復，以節省時間與功耗成本。

Face detection is mainly used to determinate wherther the face in the image can be detected or not. Accordingly the identity of the person can be recognized. In IoT (Internet of Things) applications, face detection plays an important role, such as identity recognition for door-accessing in buildings, and customs. However, high temperature, power switch, data transmission, defects, and wear-out of image processing circuits of the face detection system may result in noisy or erroneous images. Fortunately, these noises or errors do not necessarily fail the face detection system. For example, if the structure of the face is not seriously destroyed, the face is still likely detectable. In other words, the face detection system contains the error-tolerance feature where some errors can be accepted. The lifetime of the system can thus be extended.
In the thesis, we carefully analyze the error tolerability of face detection systems, and compare with the human visiual system. Interestingly the comparison results show that the face detection system even has much higher error tolerability. We also propose a high efficiency error-tolerance test method to examine the acceptability of an image. In our experiments a total of 5730 erroneous benchmark images are used to evaluate the test accuracy of the proposed test method. Experimental results show that 97.12% test accuracy is achieved.
In addition, in this thesis we also present an efficient image repair method for face detection systems. The results show that the proposed method can effectively enhance the acceptability of erroneous images, and thus improve the reliability of the face detection system. In the literature there have been a number of related repair works. However most of these researches focus on repairing noisy images. Erroenous images due to circuit aging are seldom considered. Although some work targets such images, single-bit errors are assumed. In the thesis, we consider not only erroneous images, but also the occurrence of multiple-bit errors. We also investigate the issues that the previous approaches cannot deal with such errors effectively. Accordingly we propose a more powerful method to repair erroneous image. It is worth mentioning that our developed method has excellent adaptability where our method can be dynamically reconfigured according to the significance of erroneous images. As a result, the content of high-quality images can still be maintained, while the quality of the moderate-quality images can be significantly increased. As for low-quality images, they will not be repaired in order to save computation time and power consumption.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 概論 1
1.1 研究動機 1
1.3 論文大綱 4
第二章 研究背景及相關文獻回顧 5
2.1 影像容誤 5
2.2 影像品質評估參數 6
2.2.1 Peak Signal Noise Ratio (PSNR) 6
2.2.2 Feature Similarity Index with chrominance (FSIMc) 7
2.3 人臉偵測(Face Dection) 8
2.4 JPEG 2000 12
2.5 單一固接錯誤(Single Stuck-at Fault) 12
2.6 影像錯誤特徵分類 15
2.7影像影像容誤測試方法 18
第三章 應用於人臉偵測之影像容錯評估與測試 24
3.1 簡介 24
3.2 影像品質評估參數與人臉偵測結果探討 25
3.3 PPR影像分級方法與人臉偵測結果探討 30
3.4 點的錯誤特徵偵測 36
3.5 應用於人臉偵測的容誤品質評估方法 39
3.6 結果與討論 41
第四章 有錯電路之錯誤影像修復 42
4.1 簡介 42
4.2 影像修復方法的探討 43
4.3 影像修復方法探討與開發 43
4.3.1 修復流程簡介 43
4.3.2 邊緣偵測(Edge Detection) 44
4.3.3 錯誤偵測(Error Detection) 50
4.3.4 中值濾波器(Median Filter) 50
4.3.5 實驗結果 52
4.4 選擇性啟動影像修復方法的探討與開發 60
4.4.1 SW_E5EM5選擇性啟動影像修復的方法 60
4.4.2 PPR_E5EM5選擇性啟動影像修復的方法 64
4.4.3 實驗結果 65
4.5 結果與討論 72
第五章 硬體實現 73
5.1 硬體架構 73
5.2 硬體實現結果 74
5.2.1 中值濾波器比較次數 74
5.2.2 效能比較 75
第六章 結論與未來展望 77
第七章 參考文獻 78

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