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Comparative analysis of novel preprocessing techniques and deep learning based multi modal feature fusion for diabetic retinopathy grading. [PDF]
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Blood cell image segmentation and classification: a systematic review. [PDF]
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Pattern Recognition Letters, 1994
To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is described for detecting lines and circles. >
Han, J.H., Kóczy, LszlT., Poston, T.
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To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is described for detecting lines and circles. >
Han, J.H., Kóczy, LszlT., Poston, T.
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Randomized fuzzy cell Hough transform
Proceedings of 6th International Fuzzy Systems Conference, 2002Randomized Hough transform (RHT) has been recently proposed as a new and efficient variation of the Hough transform for curve detection. In this paper the RHT is combined with the fuzzy cell Hough transform (FCHT) and a new variation, the randomized fuzzy cell Hough transform (RFCHT) is proposed.
V. Chatzis, I. Pitas
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Fuzzy cell Hough transform for curve detection
Pattern Recognition, 1997In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers.
Vassilios Chatzis, Ioannis Pitas
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The fuzzy Hough transform-feature extraction in medical images
IEEE Transactions on Medical Imaging, 1994Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such
K P, Philip +5 more
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A novel fuzzy Hough transform for shape representation
1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 2002Three techniques of straight line Hough transform are proposed by a generalised mathematical formulation of the Hough transform in terms of aggregation and weight functions. Amongst these techniques, inclusive of the conventional Hough transform, it is shown by experimental results on a set of 3D nonparametric objects, and in a controlled and inherent ...
R. Soodamani, Z.Q. Liu
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On classical and fuzzy Hough transform in colonoscopy image processing
2021 IEEE AFRICON, 2021Hough transform is used to find lines on edge-filtered images that are given in parametric form. As the fuzzy extension of the Hough transform has been proven to be more robust in environments where the lines to be found by them are not strictly following the formula given by the parametric equation of the Hough transform due to noise and weak and ...
Szilvia Nagy +3 more
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A FUZZY HOUGH TRANSFORM APPROACH TO SHAPE DESCRIPTION
International Journal of Image and Graphics, 2002To use the Hough transform to detect shapes we need to accumulate votes for the edge passing a specific bin. Most existing Hough transform techniques use a sharp (crisp) cutoff to determine whether the bin has received a vote or not. This results in considerable errors.
R. SOODAMANI, Z. Q. LIU
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Filtering of meaningful features of fuzzy hough transform
2016 Dynamics of Systems, Mechanisms and Machines (Dynamics), 2016Hough Transform and its modifications are used to find straight lines or simple geometric figures on images. But this cannot prevent us from detecting not interesting objects completely. The paper introduces a novel method of filtering or fusion of straight lines after performing Fuzzy Hough Transform.
E. V. Pugin, A. L. Zhiznyakov
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