2,073 research outputs found
A comparative evaluation of two algorithms of detection of masses on mammograms
In this paper, we implement and carry out the comparison of two methods of
computer-aided-detection of masses on mammograms. The two algorithms basically
consist of 3 steps each: segmentation, binarization and noise suppression using
different techniques for each step. A database of 60 images was used to compare
the performance of the two algorithms in terms of general detection efficiency,
conservation of size and shape of detected masses.Comment: 9 pages, 5 figures, 1 table, Vol.3, No.1, February 2012,pp19-27;
Signal & Image Processing : An International Journal (SIPIJ),201
Automatic detection of weld defects based on hough transform
Weld defect detection is an important application in the field of Non-Destructive Testing (NDT). These defects are mainly due to manufacturing errors or welding processes. In this context, image processing especially segmentation is proposed to detect and localize efficiently different types of defects. It is a challenging task since radiographic images have deficient contrast, poor quality and uneven illumination caused by the inspection techniques. The usual segmentation technique uses a region of interest ROI from the original image. In this article, a robust and automatic method is presented to detect linear defect from the original image without selection of ROI based on canny detector and a modified `Hough Transform' technique. This task can be subdivided into the following steps: firstly, preprocessing step with Gaussian filter and contrast stretching; secondly, segmentation technique is used to isolate weld region from background and non-weld using Adaptative Thresholding and to extract edges; thirdly, detection, location of linear defect and limiting the welding area by Hough Transform. The experimental results show that our proposed method gives good performance for industrial radiographic images
Image Segmentation with Multidimensional Refinement Indicators
We transpose an optimal control technique to the image segmentation problem.
The idea is to consider image segmentation as a parameter estimation problem.
The parameter to estimate is the color of the pixels of the image. We use the
adaptive parameterization technique which builds iteratively an optimal
representation of the parameter into uniform regions that form a partition of
the domain, hence corresponding to a segmentation of the image. We minimize an
error function during the iterations, and the partition of the image into
regions is optimally driven by the gradient of this error. The resulting
segmentation algorithm inherits desirable properties from its optimal control
origin: soundness, robustness, and flexibility
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