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研究生:游啟昌
研究生(外文):Chi-Cheng Yu
論文名稱:自動曝光之模糊控制
論文名稱(外文):Automatic Exposure with Fuzzy Control
指導教授:傅楸善傅楸善引用關係
指導教授(外文):Chiou-Shann Fuh
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:54
中文關鍵詞:自動曝光
外文關鍵詞:Automatic Exposure
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In this thesis, we will present an automatic exposure method based on Nikon’s AMP (Automatic-Multiple-Pattern). Our method will add the fuzzy control into the AMP to get smooth transition effect.
AMP needs to collect a lot of information for its database to offer optimal exposures for various conditions. In other words, the database is very huge and needs to cover all conditions, include weather conditions, contrast conditions, subject condition (subject is bright or dark) and so forth. We use fuzzy control to simulate the various conditions. It means we only construct three main frame reference tables, fuzzy control will simulate the ambiguous conditions (conditions not included in the reference table) to get a smooth transition effect. In this thesis, we also implement a subject growing function for dynamically guess where subject is. Our method classifies subject and background to get a better exposure. We also implement an easy evaluative function to estimate what is a well exposed picture in the same scene with different exposure parameters. The experiment results show that our evaluative function selects the best exposed picture similar to most people’s selection.
Chapter 1......................................................................................................................1
1.1 Exposure.........................................................................................................1
1.2 Zone System....................................................................................................2
1.3 Aperture and Shutter.....................................................................................3
1.4 Automatic Exposure.......................................................................................4
1.4.1 Center-Weighted-Metering and Averaged-Metering and Center-Metering...........................................................................................4
1.4.3 Evaluative-Metering.........................................................................10
1.5 High Dynamic Range Image.......................................................................11
1.5.1 Special Sensor....................................................................................11
1.5.2 Synthesis with Multiple Exposed Pictures......................................12
1.6 Light Value and Exposure Value................................................................13
Chapter 2....................................................................................................................16
2.1 Introduction..................................................................................................16
2.2 Exposure Factors.........................................................................................16
2.3 Some Problems of AMP...............................................................................16
2.4 Improvement of AMP..................................................................................17
Chapter 3....................................................................................................................18
3.1 Scene Classification......................................................................................19
3.2 Cut Process...................................................................................................21
3.3 Fuzzy Control...............................................................................................24
3.3.1 Fuzzy Control for Foreground and Background...........................24
3.3.2 Fuzzy Control for Weather Condition............................................29
3.3.3 Fuzzy Control for Contrast Level....................................................30
3.4 Subject Growing...........................................................................................31
Chapter 4....................................................................................................................33
4.1 Introduction..................................................................................................33
4.2 Image Segmentation and Image Information Retrieval...........................33
4.3 Decision Making...........................................................................................36
Chapter 5....................................................................................................................38
5.1 Introduction..................................................................................................38
5.2 Results...........................................................................................................38
Chapter 6 Conclusion and Future Works................................................................52
6.1 Conclusion and Future works.....................................................................52
6.2 Problems of Our Automatic Exposure Method.........................................52
6.3 Future Works................................................................................................52

Reference ....................................................................................................................54
List of Figures

Figure 1. 1: (a) Chessboard effect as seen by human vision or camera nearby. (b) Average luminance of (a) as seen by far-away auto-exposure sensor. ...........................................................................2
Figure 1. 2: Ansel Adam’s zone system and Grayscale Values. ................2
Figure 1. 3: (a) Original picture. (b) Over-exposed picture. ....................5
Figure 1. 4: (a) Original picture. (b) Under-exposed picture . .................5
Figure 1. 5: The CCD is divided into five segments. .................................6
Figure 1. 6: An example reference table. ....................................................7
Figure 1. 7: Three-dimensional reference table. ........................................8
Figure 1. 8: 3D Color matrix metering. .....................................................9
Figure 1. 9: Light sensitivity of human eye with various wavelengths. 10
Figure 1. 10: CCD is divided into fifteen segments. Segments 7, 8, and 9 are focal points. ..................................................................................10
Figure 1. 11: Weight-distribution when subject is in the left focal point...............................................................................................................11
Figure 1. 12: Sensor with different sensitivities and four pixels as a group. ..................................................................................................12
Figure 1. 13: A ~I is a series of picture with different exposures and the difference between A and B is one EV. J is the combination of A~I. ..............................................................................................................13
Figure 3. 1: CCD sensor is divided into 25 segments, blue group is center, and green group is background. .......................................................19
Figure 3. 2: The reference table of AMP. .................................................21
Figure 3. 3: CCD sensor is cut into five segments. ..................................21
Figure 3. 4: Example of Condition 1: (a) Only segment 2 is greater than W1. (b) Segments 2 and 3 are greater than W1. ..............................22
Figure 3. 5: Example of Condition 2: three segments are cut, but one segment’s LV is changed to W1. ........................................................22
Figure 3. 6: Example of Condition 3: (a) Only four segments are cut, but two segments’ LVs are changed to W1. (b) Five segments are cut, but two segments’ LVs are changed to W1. ......................................23
Figure 3. 7: Example of Condition 4: (a) Segment 2 is less than W4. (b) Segments 2 and 3 are less than W4. ..................................................23
Figure 3. 8: Example of Condition 5: (a) Two segments are cut, and one of them is set to W4. (b) Three segments are cut and one is set to W4. ..............................................................................................................24
Figure 3. 9: Reference table for bright subject. .......................................25
Figure 3. 10: Reference table for moderately bright subject. ................25
Figure 3. 11: Reference table for dark subject. .......................................26
Figure 3. 12: The relation between foreground and background. .........27
Figure 3. 13: Membership functions for different subject conditions (red: moderately bright subject, green: bright subject, blue: dark subject). ...............................................................................................28
Figure 3. 14: Membership functions of different weather conditions. ..29
Figure 3. 15: Membership functions for different contrast levels (red: flat contrast, green: some contrast, blue: medium contrast, cyan: strong contrast, magenta: very strong contrast). ............................30
Figure 3. 16: In initial state, segment 1 is the subject. ............................31
Figure 3. 17: Flow chart of our method presented in this thesis......31
Figure 4. 1: Subject is assumed to contain cyan region..........................33
Figure 4. 2: Table of zone numbers and their relative gray values and points..........................................................................................................34
Figure 4. 4: Red (340 degree), green (220 degree), blue (100 degree), cyan (160 degree), yellow (280 degree), and magenta (40 degree) showed on CrCb plane.................................................................................................35
Figure 4. 5: Color and its relative location in CrCb plane.....................36
Figure 5. 1: Result 1...................................................................................38
Figure 5. 2: Result 2...................................................................................39
Figure 5. 3: Result 3...................................................................................40
Figure 5. 4: Result 4...................................................................................41
Figure 5. 5: Result 5...................................................................................42
Figure 5. 6: Result 6...................................................................................43
Figure 5. 7: Result 7...................................................................................44
Figure 5. 8: Result 8...................................................................................45
Figure 5. 9: Result 9...................................................................................46
Figure 5. 10: Result 10...............................................................................47
Figure 5. 11: Result 11...............................................................................48
Figure 5. 12: Result 12...............................................................................49
Figure 5. 13: Result 13...............................................................................50
Figure 5. 14: Result 14...............................................................................51
[1] D. P. Curtin, “Digital Photography, The Textbook, a Free On-Line Course” http://209.196.177.41/, 2004.
[2] S. Hayakawa, “Camera Detecting Luminance from a Plurality of Areas,” United States Patent #5,189,460, 1990.
[3] B. Hitchman, “Using the Zone System in the Field” http://www.apogeephoto.com/mag2-6/mag2-7rh.shtml, 2004.
[4] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, N.J., 1995.
[5] S. K. Nayar and T. Mitsunaga, “High Dynamic Range Imaging: Spatially Varying Pixel Exposures,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, Vol. I, pp. 472-479, 2000.
[6] Nikon Corp., “The Nikon F5 Has 1,005 Eyes,” http://www.nikon.co.jp/main/eng/society/tec-rep/ccd.htm, 2004.
[7] K. Rockwell, “AMP Pattern Classification Table,” http://www.kenrockwell.com/nikon/matrix05.htm, 2003.
[8] K. Rockwell, “What are LV and EV,” http://www.kenrockwell.com/tech/ev.htm, 2004.
[9] T. Takagi, “Three-Dimensional-Multi-Pattern Photo-Metering Apparatus,” United States Patent #4,951,082, 1990.
[10] G. L. Ward, H. Rushmeier, C. Piatko, “A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes,” IEEE Transactions on Visualization and Computer Graphics, Vol. 3, No. 4, pp. 291-306, 1997.
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