E-commerce is a growing market for selling gods and digital images are often used to display the product. However, there is a problem when the color of the object does not match the reality. This can lead to a dissatisfaction of the customer and a return of the product. Returned goods causes a significant loss in revenue for the suppliers. One reason for untruthful rendering of colors in images is due to different temperatures, or colors, of the illumination sources lighting the scene and the object. This effect can be reduced by a method called white balance. In this thesis, an algorithm based on the technique in Hsu et al. was implemented for a more truthful rendering of images of clothes and toys used in e-commerce. The algorithm removes unwanted color casts induced in the image from two different illumination sources. The thesis also marks important details missing in aforementioned paper as well as some drawbacks of the proposed technique, such as high processing time.

<p>Digital image analysis is the field of extracting relevant information from digital images. Recent developments in imaging techniques have made 3-dimensional volume images more common. This has created a need to extend existing 2D image analysis tools to handle images of higher dimensions. Such extensions are usually not straightforward. In many cases, the theoretical and computational complexity of a problem increases dramatically when an extra dimension is added.</p><p>A fundamental problem in image analysis is image segmentation, i.e., identifying and separating relevant objects and structures in an image. Accurate segmentation is often required for further processing and analysis of the image can be applied. Despite years of active research, general image segmentation is still seen as an unsolved problem. This mainly due to the fact that it is hard to identify objects from image data only. Often, some high-level knowledge about the objects in the image is needed. This high-level knowledge may be provided in different ways. For fully automatic segmentation, the high-level knowledge must be incorporated in the segmentation algorithm itself. In interactive applications, a human user may provide high-level knowledge by guiding the segmentation process in various ways.</p><p>The aim of the work presented here is to develop segmentation and analysistools for volume images. To limit the scope, the focus has been on two specic capplications of volume image analysis: analysis of volume images of fibrousmaterials and interactive segmentation of medical images. The respective image analysis challenges of these two applications will be discussed. While the work has been focused on these two applications, many of the results presented here are applicable to other image analysis problems.</p>

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