Proceedings ArticleDOI
Fuzzy information system based digital image segmentation by edge detection
A. Jayachandran,R. Dhanashakeran,O. Sugel Anand,J. H. M. Ajitha +3 more
- pp 1-5
TLDR
A novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value, which gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness in the curved lines.Abstract:
Edge detection is a critical element in image processing, since edges contain a major function of image information. The function of edge detection is to identify the boundaries of homogeneous regions in an image based on properties such as intensity and texture. Many edge detection algorithms have been developed based on computation of the intensity gradient vector, which, in general, is sensitive to noise in the image. In order to suppress the noise, the operator based on fuzzy technique is introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3×3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.read more
Citations
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Journal ArticleDOI
Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform
TL;DR: A multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images.
Automated Blood Cancer Detection Using Image Processing Based on Fuzzy System
TL;DR: A new automated approach for blood Cancer detection and analysis from a given photograph of patient's cancer affected blood sample is presented, using Wavelet Transformation for image improvement, image segmentation for segmenting the different cells of blood and a fuzzy inference system for Final decision of blood cancer based on the number of different cells.
Edge Detection Using Soft Computing in Matlab
TL;DR: This work follows hybrid algorithm to resolve the edge detection issues with the help of least square method and gradient descent method and involves a neuro fuzzy system with the learning capability of neural network and the advantages of rule- based fuzzy system.
Book ChapterDOI
Retinal Image Classification System Using Multi Phase Level Set Formulation and ANFIS
TL;DR: This work exhibits another directed technique for hemorrhages discovery in advanced retinal images that utilizes an ANFIS plot for pixel association and registers a 5-D vector made out of dim dimension and Cross Section Profie (CSP) Study-constructed highlights for pixel portrayal.
References
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Proceedings Article
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