Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

We address the problem of estimating statistics in regions of interest (ROIs) containing both whole and partial pixels. Such ROIs arise frequently in vision problems such as segmentation and registration. For example, even where the control points of an ROI, say the vertices of a polygon, are forcibly aligned with the pixel grid, the connecting edges will rarely do so. In medical image analysis, for instance, this can be a cause of significant error. More generally, any cost function that includes statistics estimated from the image will often exhibit irregularities due to such partial pixels. Our proposed solution addresses this problem by correctly accounting for the partial pixel area. Moreover, the method has no arbitrary parameters such as bin widths or kernel sizes. It implicitly addresses the issue of independence and gives rise to continuous density estimates whose quality is, in principle at least, independent of the number of pixels in the ROI. We present results to compare our proposed method with conventional techniques such as weighted histograms and Parzen windowing.

Original publication

DOI

10.5244/C.19.64

Type

Conference paper

Publication Date

01/01/2005