我们提出了两个新的方法来解决低级别计算机视觉任务，即图像共同分割和降噪。 / 在共同分割模型上，我们发现对象对应可以为前景统计估计提供有用的信息。我们的方法可以处理极具挑战性的场景，如变形，角度的变化和显着不同的视角和尺度。此外，我们研究了一种新的能量最小化模型，可以同时处理多个图像。真实和基准数据的定性和定量实验证明该方法的有效性。 / 另一方面，噪音始终和高频图像结构是紧耦合的，从而使得减少噪音非常很难。在我们的降噪模型中，我们建议稍微使图像光学离焦，以减少图像和噪声的耦合。这使得我们能更有效地降低噪音，随后恢复失焦。我们的分析显示，这是可能的，并且用许多例子证明我们的技术，其中包括低光图像。 / We present two novel methods to tackle low level computer vision tasks,i.e., image cosegmentation and denoise . / In our cosegmentationmodel, we discover object correspondence canprovide useful information for foreground statistical estimation. Ourmethod can handle extremely challenging scenarios such as deformation, perspective changes and dramatically different viewpoints/scales. In addition, we develop a novel energy minimization model that can handlemultiple images. Experiments on real and benchmark data qualitatively and quantitatively demonstrate the effectiveness of the approach. / One the other hand, noise is always tightly coupled with high-frequencyimage structure, making noise reduction generally very difficult. In ourdenoise model, we propose slightly optically defocusing the image in orderto loosen this noise-image structure coupling. This allows us to more effectively reduce noise and subsequently restore the small defocus. Weanalytically show how this is possible, and demonstrate our technique on a number of examples that include low-light images. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Qin, Zenglu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 64-71). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.1.1 --- Cosegmentation --- p.1 / Chapter 1.1.2 --- Image Denoise --- p.4 / Chapter 1.2 --- Thesis Outline --- p.7 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Cosegmentation --- p.8 / Chapter 2.2 --- Image Denoise --- p.10 / Chapter 3 --- Cosegmentation of Multiple Deformable Objects --- p.12 / Chapter 3.1 --- Related Work --- p.12 / Chapter 3.2 --- Object Corresponding Cosegmentation --- p.13 / Chapter 3.3 --- Importance Map with Object Correspondence --- p.15 / Chapter 3.3.1 --- Feature Importance Map --- p.16 / Chapter 3.3.2 --- Importance Energy E[subscript i](xp) --- p.20 / Chapter 3.4 --- Experimental Result --- p.20 / Chapter 3.4.1 --- Two-Image Cosegmentation --- p.21 / Chapter 3.4.2 --- ETHZ Toys Dataset --- p.22 / Chapter 3.4.3 --- More Results --- p.24 / Chapter 3.5 --- Summary --- p.27 / Chapter 4 --- Using Optical Defocus to Denoise --- p.28 / Chapter 4.1 --- Related Work --- p.29 / Chapter 4.2 --- Noise Analysis --- p.30 / Chapter 4.3 --- Noise Estimation with Focal Blur --- p.33 / Chapter 4.3.1 --- Noise Estimation with a Convolution Model --- p.33 / Chapter 4.3.2 --- Determining λ --- p.41 / Chapter 4.4 --- Final Deconvolution and Error Analysis --- p.43 / Chapter 4.5 --- Implementation --- p.45 / Chapter 4.6 --- Quantitative Evaluation --- p.47 / Chapter 4.7 --- More Experimental Results --- p.53 / Chapter 4.8 --- Summary --- p.56 / Chapter 5 --- Conclusion --- p.62 / Bibliography --- p.64
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328159 |
| Date | January 2012 |
| Contributors | Qin, Zenglu., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource ([1], viii, 71 leaves) : ill. (chiefly col.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0014 seconds