Image segmentation is the first step of image processing and image analysis. Texture segmentation is a challenging task in image segmentation applications. Neutrosophy has a natural ability to handle the indeterminate information. In this work, we investigate the texture image segmentation based on Gabor filters (GFs) and neutrosophic graph cut (NGC). We proposed an image segmentation approach,...

This report proposes a new image segmentation method with a multi-resolution mechanism. In this method, the high accuracy on image edge extraction and image texture evaluation is realized. The proposed method employs the edge extraction based on the average values of intensities in a small image part and also the texture evaluation based on the co-occurrence probability of a local image area. I...

Texture segmentation aims at dividing an image into perceptually uniform regions each containing a distinct texture. In images of natural scene, texture in a region can change gradually in scale and orientation due to perspective distortion. A naive segmentation method may erroneously group image patches with the same texture but slowly varying scales and orientations into distinct regions. Thi...

In this paper, we focus on invariant texture segmentation, and propose a new method using circular Gabor filters (CGF) for rotation invariant texture segmentation. The traditional Gabor function is modified into a circular symmetric version. The rotation invariant texture features are achieved via the channel output of the CGF. A new scheme of the selection of Gabor parameters is also proposed ...

This work presents an approach to the extraction of rotation invariant features for texture segmentation using multiscale wavelet frame analysis. The texture is decomposed into a set of bandpass channels by a circularly symmetric wavelet lter, which then gives a measure of edge magnitudes of the texture at di erent scales. The texture is characterized by local energies over small overlapping wi...

Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can ...

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels i...

We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their responses to a set of filters. However, this approach brings with it many open questions, including how to combine texture and intensity information into a common descriptor and how to deal wi...

We develop an approach to image segmentation for natural scenes containing image texture. One general methodology which shows promise for solving this problem is to characterize textured regions via their responses to a set of lters. However, this approach brings with it many open questions, including how to combine texture and intensity information into a common descriptor and how to deal with...

In the last decade, deep learning has contributed to advances in a wide range computer vision tasks including texture analysis. This paper explores a new approach for texture segmentation using deep convolutional neural networks, sharing important ideas with classic filter bank based texture segmentation methods. Several methods are developed to train Fully Convolutional Networks to segment tex...