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A neural network approach to colour constancy.

机译:一种用于色彩恒定的神经网络方法。

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摘要

This thesis presents a neural network approach to colour constancy: a neural network is used to estimate the chromaticity of the illuminant in a scene based only on the image data collected by a digital camera. This is accomplished by training the neural network to learn the relationship between the pixels in a scene and the chromaticity of the scene's illumination. From a computational perspective, the goal of colour constancy is defined to be the transformation of a source image, taken under an unknown illuminant, to a target image, identical to one that would have been obtained by the same camera, for the same scene, under a standard illuminant. A colour constancy algorithm first estimates the colour of the illumination and second corrects the image based on this illuminant estimate. Estimating the illumination in a scene is a difficult task, since it is an inherently underdetermined problem.; Tests were performed on synthesised scenes as well as on natural images, taken with a digital camera. It is expected that theoretical models used for training that closely match the ‘real world’ lead to better estimates of the illuminant in real images. Thus, a natural step was to train the network on data derived from real images instead of synthetic scenes. This approach led to even more accurate estimates, of approximately 5ΔELab. To overcome the fact that the actual illuminant used in the training set images must be accurately known, and therefore must be measured for every image, a novel training algorithm called ‘neural network bootstrapping’ was developed. Experiments indicate that a grey world algorithm provides a relatively good estimation of the illuminant for images with lots of colours. This estimation, in turn, can be used for training the neural network. The final performance of the neural network is better than the performance of the grey world algorithm that was Initially used to train it.; The last part of the thesis deals with the issue of colour correcting images of unknown origin, such as images downloaded from the Internet or scanned from film. We have shown that colour correction of non-linear images can be done in the same way as for linear images and that a neural network is able to estimate the illuminant even when the sensor sensitivity functions and camera balance are unknown.; Using a neural network to estimate the chromaticity of the scene illumination improved upon existing colour constancy algorithms by an increase in both accuracy and stability. Therefore, neural networks provide a viable method for eliminating colour casts in digital photography and for creating illuminant-independent colour descriptors for colour-based object recognition systems.
机译:本文提出了一种用于颜色恒定的神经网络方法:仅基于数字相机收集的图像数据,使用神经网络来估计场景中光源的色度。这是通过训练神经网络来了解场景中像素与场景照明色度之间的关系来实现的。从计算角度来看,色彩恒定的目标定义为将在未知光源下拍摄的源图像转换为目标图像,该目标图像与同一台摄像机针对同一场景获得的图像相同,在标准光源下。颜色恒定性算法首先估计照明的颜色,然后根据此照明估计对图像进行校正。估计场景中的照度是一项艰巨的任务,因为这是固有的不确定性问题。使用数码相机对合成场景以及自然图像进行了测试。可以预期,用于训练的理论模型会与“现实世界”紧密匹配,从而可以更好地估计真实图像中的光源。因此,自然而然的步骤是在网络上训练来自真实图像而不是合成场景的数据。这种方法可以得出更准确的估计值,约为5ΔELab。为了克服必须准确知道训练集图像中使用的实际光源并因此必须对每个图像进行测量的事实,开发了一种称为“神经网络自举”的新颖训练算法。实验表明,灰度世界算法可以为多种颜色的图像提供相对较好的光源估计。该估计又可以用于训练神经网络。神经网络的最终性能优于最初用于训练它的灰色世界算法的性能。本文的最后一部分讨论了来自未知来源的色彩校正图像的问题,例如从Internet下载的图像或从胶片扫描的图像。我们已经表明,非线性图像的颜色校正可以与线性图像相同的方式进行,并且即使传感器灵敏度函数和相机平衡未知,神经网络也能够估计光源。通过使用神经网络来估计场景照明的色度,通过提高准确性和稳定性,在现有的颜色恒定性算法上得到了改善。因此,神经网络提供了一种可行的方法来消除数字摄影中的色偏,并为基于颜色的对象识别系统创建与光源无关的颜色描述符。

著录项

  • 作者

    Cardei, Vlad Constantin.;

  • 作者单位

    Simon Fraser University (Canada).;

  • 授予单位 Simon Fraser University (Canada).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 178 p.
  • 总页数 178
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:47:36

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