Unsupervised texture segmentation using Gabor filters
نویسندگان
چکیده
منابع مشابه
1 Unsupervised Texture Segmentation Using Gabor Filters 1
We presenf a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. We propose a systematic filter selection scheme which is based on reconstruction of the input image from the fi...
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The human’s capability to distinguish perceptually different textures is difficult to reproduce using machine vision due the variety of textural patterns and illumination conditions. Bovik et al. proposed a multi-channel texture analysis technique that relies on 2-D Gabor filters to isolate regions of perceptually homogeneous texture in an image. Textures are modeled as a pattern dominated by a...
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Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
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Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can ...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 1991
ISSN: 0031-3203
DOI: 10.1016/0031-3203(91)90143-s