Texture Segmentation Research Papers - Academia.edu (original) (raw)

Texture segmentation can be considered the most important problem, since human can distinguish different textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for research. In this paper... more

Texture segmentation can be considered the most important problem, since human can distinguish different textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract vector feature for each block to classification these block based on these feature. These feature extract using Box Counting Method (BCM). BCM generate single feature for each block and this feature not enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for the image based on new method produce multithresolding, after this use BCM to generate single feature for each slide.

We develop a transformation based on morphological filters that measures the contrast of image texture. This transforma- tion is proportional to texture contrast, but insensitive to its specific type. Though the transformation provides... more

We develop a transformation based on morphological filters
that measures the contrast of image texture. This transforma-
tion is proportional to texture contrast, but insensitive to its
specific type. Though the transformation provides a high re-
sponse in textured areas, it suppresses individual high contrast
features that stand apart from textured areas. It can serve as
an effective texture descriptor for unsupervised or supervised
segmentation of textured regions, provides high accuracy of
localization and does not involve heavy computations. The
method is robust to variations of illumination and works on
different types of images without needing to be tuned. The
only parameter is a scale related parameter. We illustrate the
use of the proposed method on satellite and aerial images.

Международный научно-учебный центр информационных технологий и систем НАН Украины и Министерства образования и науки Украины

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose... more

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For the splitting part, given a connected component of the segmentation map, we initially differentiate whether it is a touching-cell clump or a single nontouching cell. The differentiation is mainly based on the distance between the most likely radial-symmetry center and the geometrical center of the connected component. The boundaries of touching-cell clumps are smoothed out by Fourier shape descriptor before carrying out an iterative, concave-point and radial-symmetry based splitting algorithm. To test the validity, effectiveness and efficiency of the framework, it is applied to follicular lymphoma pathological images, which exhibit complex background and extracellular texture with nonuniform illumination condition. For comparison purposes, the results of the p- oposed segmentation algorithm are evaluated against the outputs of superpixel, graph-cut, mean-shift, and two state-of-the-art pathological image segmentation methods using ground-truth that was established by manual segmentation of cells in the original images. Our segmentation algorithm achieves better results than the other compared methods. The results of splitting are evaluated in terms of under-splitting, over-splitting, and encroachment errors. By summing up the three types of errors, we achieve a total error rate of 5.25% per image.

Gabor features are a common choice for texture analysis. There are several pop- ular sets of Gabor lters. These sets are usually designed based on representation considerations. We propose here an alternative criterion for designing the... more

Gabor features are a common choice for texture analysis. There are several pop- ular sets of Gabor lters. These sets are usually designed based on representation considerations. We propose here an alternative criterion for designing the lters set. We consider a set of lters and their responses to a pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of responses are disjoint in at least one of the responses. We look for the set of Gabor lters maximizing the fraction of separable harmonic signals. The proposed semi-analytical algorithm calculates lters parameters for the optimal set, given the desired number of lters and the frequency range of possible signals. The resulting lters are signican tly dieren t from those traditionally used. We tested the proposed lters both in texture segmentation and texture recogni- tion aspects with commonly used discrimination algorithms for each of the tasks. We show that, as expected, the resulting lters pe...

We propose an algorithm for finding a set of texture features characterizing the most homogeneous texture area of an input image. The found set of features is intended for extraction of this segment. The algorithm processes any input... more

We propose an algorithm for finding a set of texture features characterizing the most homogeneous texture area of an input image. The found set of features is intended for extraction of this segment. The algorithm processes any input images in the absence of any preliminary information about the images and, accordingly, without any learning. The essence of the algorithm is as follows. The image is covered with a number of test windows. In each of them, a degree of texture homogeneity is measured. The test window with maximal degree of homogeneity is determined and a representative patch of pixels is detected. The texture features extracted from the detected representative patch is considered as those that best characterize the most homogeneous texture segment. So, the proposed algorithm facilitates solution of the texture segmentation task by providing a segmentation technique with helpful additional information about the analyzed image. A computer program simulating the algorithm has been created. The program is tested on natural grayscale images.

In this study, region-based segmentation of textural images is investigated. For this purpose, the seeded region growing algorithm is used in feature space. In order to make an accurate segmentation, it is crucial to appropriately select... more

In this study, region-based segmentation of textural images is investigated. For this purpose, the seeded region growing algorithm is used in feature space. In order to make an accurate segmentation, it is crucial to appropriately select the initial seed points as well as to decide where to stop the growing procedure. In the first stage, the boundaries between the textures

A successful class of texture analysis methods is based on multiresolution decompositions. Especially Gabor filters have extensively been used 1 2 3 4 5 6. More recently, decompositions with pyramidal and tree structured wavelet... more

A successful class of texture analysis methods is based on multiresolution decompositions. Especially Gabor filters have extensively been used 1 2 3 4 5 6. More recently, decompositions with pyramidal and tree structured wavelet transforms have been ...

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content... more

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the... more

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results.

In this paper we present a hybrid approach to segment and classify contents of document images. A Document Image is segmented into three types of regions: Graphics, Text and Space. The image of a document is subdivided into blocks and for... more

In this paper we present a hybrid approach to segment and classify contents of document images. A Document Image is segmented into three types of regions: Graphics, Text and Space. The image of a document is subdivided into blocks and for each block five GLCM (Grey Level Co-occurrence Matrix) features are extracted. Based on these features, blocks are then clustered into three groups using K-Means algorithm; connected blocks that belong to the same group are merged. The classification of groups is done using pre-learned heuristic rules. Experiments were conducted on scanned newspapers and images from MediaTeam Document Database

The real-time open road driving way determination is one of the important components of a system for automatic vehicle guidance. To determine the road driving way the authors process the images obtained from a camera sited at the... more

The real-time open road driving way determination is one of the important components of a system for automatic vehicle guidance. To determine the road driving way the authors process the images obtained from a camera sited at the driver's place. They describe the processing of the real images and the neural network system that, after a training phase, is able

We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing... more

We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.

An ongoing challenge in the area of image segmentation is in dealing with scenes exhibiting complex textural characteristics. While many approaches have been proposed to tackle this particular challenge, a related topic of interest that... more

An ongoing challenge in the area of image segmentation is in dealing with scenes exhibiting complex textural characteristics. While many approaches have been proposed to tackle this particular challenge, a related topic of interest that has not been fully explored for dealing with this challenge is stochastic texture models, particularly for characterizing textural characteristics within regions of varying sizes and shapes. Therefore, this paper presents a novel method for image segmentation based on the concept of multi-scale stochastic regional texture appearance models. In the proposed method, a multi-scale representation of the image is constructed using an iterative bilateral scale space decomposition. Local texture features are then extracted via image patches and random projections to generate stochastic texture features. A texton dictionary is built from the stochastic features, and used to represent the global texture appearance model. Based on this global texture appearance model, a regional texture appearance model can then be obtained based on the texton occurrence probability given a region within an image. Finally, a stochastic region merging algorithm that allows the computation of complex features is presented to perform image segmentation based on proposed regional texture appearance model. Experimental results using the BDSD300 segmentation dataset showed that the proposed method achieves a Probabilistic Rand Index (PRI) of 0.83 and an F-measure of 0.77@(0.92, 0.68), and provides improved handling of color and luminance variation, as well as strong segmentation performance for images with highly textured regions when compared to a number of previous methods. These results suggest that the proposed stochastic regional texture appearance model is better suited for handling the texture variations of natural scenes, leading to more accurate segmentations, particularly in situations characterized by complex textural characteristics.

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This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic... more

This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set. Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems