فهرست مقالات Texture Image

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  1 - Automatic Face Recognition via Local Directional Patterns
  Maryam Moghaddam Saeed Meshgini
  Automatic facial recognition has many potential applications in different areas of humancomputer interaction. However, they are not yet fully realized due to the lack of an effectivefacial feature descriptor. In this paper, we present a new appearance based feature desc چکیده کامل

  Automatic facial recognition has many potential applications in different areas of humancomputer interaction. However, they are not yet fully realized due to the lack of an effectivefacial feature descriptor. In this paper, we present a new appearance based feature descriptor,the local directional pattern (LDP), to represent facial geometry and analyze its performance inrecognition. An LDP feature is obtained by computing the edge response values in 8 directions ateach pixel and encoding them into an 8 bit binary number using the relative strength of theseedge responses. The LDP descriptor, a distribution of LDP codes within an image or imagepatch, is used to describe each image. Two well-known machine learning methods, templatematching and support vector machine, are used for classification using the ORL female facialexpression databases. Better classification accuracy shows the superiority of LDP descriptoragainst other appearance-based feature descriptors. Entropy + LDP + SVM is as an improvedalgorithm for facial recognition than previous presented methods that improves recognition rateby features extraction of images. Test results showed that Entropy + LDP + SVM, methodpresented in this paper, is fast and efficient. Innovation proposed in this paper is the use ofentropy operator before applying LDP feature extraction method. The test results showed that theapplication of this method on ORL database images causes 3 percent increases in comparisonwith not using entropy operator. پرونده مقاله
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  2 - Unsupervised Texture Image Segmentation Using MRFEMFramework
  Marzieh Azarian Reza Javidan Mashallah Abbasi Dezfuli
  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 segme چکیده کامل

  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, orientation) values. The output image of this step clarified different textures and then used low pass Gaussian filter for smoothing the image. These two filters were used as preprocessing stage of texture images. In this research, we used K-means algorithm for initial segmentation. In this study, we used Expectation Maximization (EM) algorithm to estimate parameters, too. Finally, the segmentation was done by Iterated Conditional Modes (ICM) algorithm updating the labels and minimizing the energy function. In order to test the segmentation performance, some of the standard images of Brodatz database are used. The experimental results show the effectiveness of the proposed method. پرونده مقاله