Texture image segmentation plays an important role in texture analysis. This paper presents an approach to image segmentation by texture classification based on fractional discrimination functions. The idea behind this method is to enhance the texture edge points b y means of image decomposition and contextual filtering in terms of the proposed fractional function. In addition, such function is...

Accurate medical diagnosis requires the segmentation of a large number of medical images. Although manual segmentation provides good results, it is a costly process in terms of both money and time. Automatic segmentation, on the other hand, remains a challenge due to low image contrast and ill-defined boundaries. In this report, we propose a fully automated medical image segmentation framework ...

Texture segmentation is usually regarded as a fast, early, automatic, preattentive process. Nevertheless, naive participants in texture segmentation tasks are usually not able to perform the task explicitly when the textures are presented rather briefly (49 ms) and subsequently masked. In two experiments it was investigated whether texture stimuli were, nevertheless, automatically segmented und...

The paper proposes a generic color-texture feature integration framework. We propose two variants of edge based texture capturing method using filter banks of tensor products obtained from Orthogonal Polynomials (OP) OP3 of order 3 and OP5 of higher order 5 which are applied on Hybrid Color Space (HCS) for color texture feature integration. A region based unsupervised segmentation algorithm is ...

This paper proposes a framework for colour texture segmentation. The framework uses the colour and the texture distributions for discriminating the colour textured regions. The proposed colour texture segmentation method was tested in three different applications. The applications includes Irish Script on Screen (ISOS) images, skin cancer images and Sediment Profile Imagery (SPI). Image texture...

Accurate medical diagnosis requires a segmentation of a large number of medical images. Although the manual segmentation produces good results, it is a costly process (in the terms of money and time). On the other hand, the automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. In this work, we propose a fully automated medical image segmentation f...

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spa...

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block compariso...

Image segmentation is a valuable approach that performs an object-based rather than a pixel-based analysis of high-spatial resolution satellite image. A multiscale approach for segmenting the pan-sharpened multispectral QuickBird-2 image based on vector field model is proposed. The edge features are obtained using the first fundamental form of the multispectral bands. The response of log Gabor ...

The aim of this study is to provide an efficient way to segment the skin cancer images. A novel method is proposed that combines colour and texture for the segmentation of skin lesions from unaffected skin region in an image. The distributions of colour and texture features provide a good discrimination of skin lesions. The evaluation of the proposed method was based on the comparison with the ...