Abstract: This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.
Abstract: Deformable active contours are widely used in
computer vision and image processing applications for image
segmentation, especially in biomedical image analysis. The active
contour or “snake" deforms towards a target object by controlling the
internal, image and constraint forces. However, if the contour
initialized with a lesser number of control points, there is a high
probability of surpassing the sharp corners of the object during
deformation of the contour. In this paper, a new technique is
proposed to construct the initial contour by incorporating prior
knowledge of significant corners of the object detected using the
Harris operator. This new reconstructed contour begins to deform, by
attracting the snake towards the targeted object, without missing the
corners. Experimental results with several synthetic images show the
ability of the new technique to deal with sharp corners with a high
accuracy than traditional methods.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear debris
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self-organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear debris. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.