Proceedings ArticleDOI
Feature extraction by grayscale morphological operations-a comparison to DOG filters
H. Boerner
- pp 112-117
TLDR
In this article, a comparison is made between the conventional difference-of-Gaussians (DOG) filter and a morphological filter based on opening with a spherical structuring element in realistic images from X-ray inspection.Abstract:
A comparison is made of the conventional difference-of-Gaussians (DOG) filter and a morphological filter based on opening with a spherical structuring element in realistic images from X-ray inspection. The task is the detection of bright blobs in low-signal/high-noise images containing other objects, such as step edges and corners. The study shows that the morphological filter has a superior selectivity, as it is insensitive to edges, but the resulting images are not free from extra signals. Postprocessing is required to remove undesired responses at sharp, bright corners and similar narrow structures. The morphological filter is more susceptible to noise than its linear competitor, since no averaging is performed. Presmoothing can alleviate this problem, but the choice of the smoothing filter is nontrivial if the advantages of the morphological filter are to be preserved. >read more
Citations
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Dissertation
An image processing approach for cephalometric measurements
TL;DR: A new system is developed to automate cephalometric analysis and measurements using x-ray images that performs better than the existing methods in terms of their identifiably, accuracy, speed, and dynamic range of images.
Book ChapterDOI
Study on the Type Identification of Cheese Yarn Based on Low-Resolution Pictures
TL;DR: In this article, an improved Hough algorithm was used to remove the background of the label and then the weights of the neural network were obtained by training the network with histogram information, and the network is used to judge whether the types of yarns are the same as the samples.
References
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Book
Image Analysis and Mathematical Morphology
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Journal ArticleDOI
Image Analysis Using Mathematical Morphology
TL;DR: The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations.
Journal ArticleDOI
Biomedical Image Processing
TL;DR: For the last five years, the University of Michigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing, finding that quantification by automated image analysis not only increases diagnostic accuracy but also provides significant data not obtainable from qualitative analysis alone.
Journal ArticleDOI
Morphologic edge detection
TL;DR: The blur-minimum morphologic edge operator is defined, its inherent noise sensitivity is less than the dilation or the erosion residue operators, and it is less computationally complex than the facet edge operator.