نتایج جستجو برای: fuzzy edge detection

تعداد نتایج: 751565  

2012
Suryakant Neetu Kushwaha

Edge Detection using Fuzzy Logic in Matlab Suryakant, Neetu Kushwaha Department of Computer Science and Engineering, NIT Jalandhar Abstract— This paper proposes the implementation of a very simple but efficient fuzzy logic based algorithm to detect the edges of an image without determining the threshold value. The proposed approach begins by scanning the images using floating 3x3 pixel window. ...

2005
Yasar Becerikli Tayfun M. Karan

An edge detection is one of the most important tasks in image processing. Image segmentation, registration and identification are based on edge detection. In the literature, there is some techniques developed to achive this task such as Sobel, Prewitt, Laplacian and Laplacian of Gaussian. In this paper, a novel knowledge-based approach which have been used to realize control techniques for past...

2007
Atif Bin Mansoor Ajmal S. Mian Adil Khan Shoab A. Khan

This paper proposes a new approach for structure based separation of image objects using fuzzy morphology. With set operators in fuzzy context, we apply an adaptive alpha-cut morphological processing for edge detection, image enhancement and segmentation. A Top-hat transform is first applied to the input image and the resulting image is thresholded to a binary form. The image is then thinned us...

The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy ima...

2002
Lily R. Liang Carl G. Looney

The competitive fuzzy classifier operates on the set of four features extracted from the 3x3 neighborhood of each pixel. These features are the magnitudes of differences between that pixel and its neighboring pixels on four directions. They are input into the competitive fuzzy classifier inputs that connect to five fuzzy set membership functions that represent “white background” or one of the f...

Journal: :Fuzzy Sets and Systems 2008
Florence Jacquey Frédéric Comby Olivier Strauss

The use of omnidirectional vision has increased during these past years. It provides a very large field of view. Nevertheless, omnidirectional images contain significant radial distortions and conventional image processing is not adapted to these specific images. This paper presents an edge detector adapted to the image geometry. Fuzzy sets will be used to take into account all imprecisions int...

Journal: :CoRR 2016
Georgios Drakopoulos Andreas Kanavos Christos Makris Vasileios Megalooikonomou

An essential feature of large scale free graphs, such as the Web, protein-to-protein interaction, brain connectivity, and social media graphs, is that they tend to form recursive communities. The latter are densely connected vertex clusters exhibiting quick local information dissemination and processing. Under the fuzzy graph model vertices are fixed while each edge exists with a given probabil...

2015
Jianning Han Quan Zhang Peng Yang Yifan Gong

Recently, more and more researchers have been paying close attention to tumor image processing techniques, in which the tumor image segmentation technology is regarded as the absolute research focus. Given that there exist only one tumor image segmentation algorithm, no further high quality three-dimensional slice can be provided for the later reconstruction of the three-dimensional reconstruct...

2015
Jiwon Yang Gwanggil Jeon

This paper studies a new color image edge detection method. The fuzzy inference engine is designed to detect edges. Some membership functions in horizontal and vertical directions are used for edge detecting. Experimental results show the presented method provides good performance when it is compared with conventional methods.

2011
Farzad Khalvati Mehdi Kianpour Hamid R. Tizhoosh

Window Memoization is a performance improvement technique for image processing algorithms. It is based on removing computational redundancy in an algorithm applied to a single image, which is inherited from data redundancy in the image. The technique employs a fuzzy reuse mechanism to eliminate unnecessary computations. This paper extends the window memoization technique such that in addition t...

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