Statistical models for irregularly sized objects

不规则尺寸物体的统计模型

• 批准号: 508325-2017
• 负责人: Campbell, David
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640449
• 关键词: Statistical; models; irregularly; sized; objects

The primary objective of this project is prediction of a continuous output Y from images acting as covariates Xthrough a nonlinear regression function f() in the equation: Y = f(X) + residual. In order for the regressionmodel to be valid all of the X covariates must have the same dimension. Unlike in most image processingapplications images in this project are of differing sizes. In standard applications irregularly sized rectangularimages can be transformed via compression, stretching, cropping, and padding to bring the images to acommon size. However in our application a transformed image with a high value of Y will be equivalent to anun-transformed image of the same dimension with a low value of Y. In other words, the size normalizingtransformation process induces artifacts that will destroy the regression relationship. This project expands themethods used for image processing to account for this type of problem. Ultimately the insights of theregression model will be used to predict optimal ways in which image X can be modified so as to produce anincreased/decreased value of Y. In this project we try to predict year Y images X from an art database whereart pieces are arbitrarily sized rectangles with any aspect ratio. Compressing an image will distort the imagecontents in ways that could be inconsistent with its time period. Cropping an image removes importantfeatures for prediction. Padding images with extrapolated coloured space makes the image equivalent to adifferent art piece. In such cases an early renaissance piece may become dominated by the transformationfeatures making it equivalent to a recent modern art piece. The secondary goal of this work is to examine howto modify an art work so as to make it equivalent to an earlier or later form of that style. These methods areapplicable to the partner company, Unbounce, in that they will assist them in advising their client customers inhow to modify artwork optimally while maintaining it's overall style.

该项目的主要目标是通过方程Y = f（X）+残差中的非线性回归函数f（）从图像中预测连续输出Y。为了使回归模型有效，所有X协变量必须具有相同的维度。与大多数图像处理应用程序不同，该项目中的图像大小不同。在标准应用程序中，不规则大小的矩形图像可以通过压缩、拉伸、裁剪和填充来转换，以使图像达到共同的大小。然而，在我们的应用中，具有高Y值的变换图像将等效于具有低Y值的相同维度的未变换图像。换句话说，大小标准化转换过程会导致会破坏回归关系的伪影。这个项目扩展了用于图像处理的方法来解决这类问题。最终，回归模型的洞察力将被用于预测最佳的方法，在该方法中，图像X可以被修改，从而产生Y的增加/减少值.在这个项目中，我们试图预测Y年的图像X从艺术数据库，其中艺术作品是任意大小的矩形与任何纵横比。压缩图像将以与其时间段不一致的方式扭曲图像内容。裁剪图像会删除重要的预测特征。用外推的彩色空间填充图像，使图像相当于一个不同的艺术作品。在这种情况下，一件早期文艺复兴时期的作品可能会被转化特征所支配，使其等同于一件近代的现代艺术作品。这项工作的第二个目标是研究如何修改艺术作品，使其等同于该风格的早期或后期形式。这些方法适用于合作伙伴公司，Unbounce，因为它们将帮助他们建议他们的客户如何最佳地修改艺术品，同时保持其整体风格。