Abstract: In image segmentation contour detection is one of the important pre-processing steps in recent days. Contours characterize boundaries and contour detection is one of the most difficult tasks in image processing. Hence it is a problem of fundamental importance in image processing. Contour detection of an image decreases the volume of data considerably and useless information is removed, but the structural properties of the image remain same. In this research, a robust and effective contour detection technique has been proposed using mathematical morphology. Three different contour detection results are obtained by using morphological dilation and erosion. The comparative analyses of three different results also have been done.
Abstract: This paper describes a novel method for automatic
estimation of the contours of weld defect in radiography images.
Generally, the contour detection is the first operation which we apply
in the visual recognition system. Our approach can be described as a
region based maximum likelihood formulation of parametric
deformable contours. This formulation provides robustness against
the poor image quality, and allows simultaneous estimation of the
contour parameters together with other parameters of the model.
Implementation is performed by a deterministic iterative algorithm
with minimal user intervention. Results testify for the very good
performance of the approach especially in synthetic weld defect
images.
Abstract: Detection and tracking of the lip contour is an important
issue in speechreading. While there are solutions for lip tracking
once a good contour initialization in the first frame is available,
the problem of finding such a good initialization is not yet solved
automatically, but done manually. We have developed a new tracking
solution for lip contour detection using only few landmarks (15
to 25) and applying the well known Active Shape Models (ASM).
The proposed method is a new LMS-like adaptive scheme based on
an Auto regressive (AR) model that has been fit on the landmark
variations in successive video frames. Moreover, we propose an extra
motion compensation model to address more general cases in lip
tracking. Computer simulations demonstrate a fair match between
the true and the estimated spatial pixels. Significant improvements
related to the well known LMS approach has been obtained via a
defined Frobenius norm index.