The R and L values of an object can only be computed from an object’s true gray levels. True gray level refers to the measured gray level of an object when it is not overlapped with any other objects. The problem is objects in a bag almost always overlap with other objects. Being able to identify the object of interest and remove the overlap effects becomes the key issue that needs to be solved.
The discussion in this dissertation focuses on the development of the image-processing system used on this multiple sensor system. This image-processing system is comprised of four steps. The first step is to spatially register images from all the sensing modalities. The second step is to remove noise using the edge-preserving smoothing algorithm. The third step is to segment image into regions with relatively uniform gray levels. The fourth step is to compute the true
gray levels for objects effects. Most of the segmenting x-ray bag
of interest using the mathematical models for removing overlapping research focuses on developing a robust segmentation algorithm for images and developing mathematical models for removing object
overlapping effects. The unique contribution of this dissertation those mathematical models used for removing object-overlapping of the algorithm for determining an object’s true gray levels.
includes the development of effects, and the development
The experimental verification shows that the algorithms for registration, smoothing, and segmentation work well. The algorithm that computes the true gray levels of an object can perform the computation quite precisely in transmission modality. However, the methods that were developed for computing an object’s true gray levels in scatter images are much less accurate.