Multichannel texture analysis using localized spatial filters

A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures     

Vessel enhancement filter using directional filter bank

Vessel enhancement is an important preprocessing step in accurate vessel-tree reconstruction which is necessary in many medical imaging applications. Conventional vessel enhancement approaches used in the literature are Hessian-based filters, which are found to be sensitive to noise and sometimes give discontinued vessels due to junction suppression. In this paper, we propose a novel framework for vessel enhancement for angiography images. The proposed approach incorporates the use of line-like directional features present in an image, extracted by a directional filter bank, to obtain more precise Hessian analysis in noisy environment and thus can correctly reveal small and thin vessels. Also, the directional image decomposition helps to avoid junction suppression, which in turn, yields continuous vessel tree. Qualitative and quantitative evaluations performed on both synthetic and real angiography images show that the proposed filter generates better performance in comparison against two Hessian-based approaches. In average, it is relatively 3.74% and 7.02% less noise-sensitive and performs 5.83% and 6.21% better compared to the two approaches, respectively.     

Invariant feature extraction for 3D texture analysis using the autocorrelation function

A new method for invariant feature extraction on textured images undergoing affine transformations is presented. This is performed by transformation of the autocorrelation function (ACF) followed by determination of an invariant criterion which is the sum of the coefficients of the discrete correlation matrix. Experimental results support the effectiveness of the proposed approach.     

Radiological image retrieval technique using multi-resolution texture and shape features

Multimedia Tools and Applications - Medical image analysis plays a very indispensable role in providing the best possible medical support to a patient. With the rapid advancements in modern medical...     

Invariant image classification using triple-correlation-basedneural networks

Triple-correlation-based neural networks are introduced and used in this paper for invariant classification of 2D gray scale images. Third-order correlations of an image are appropriately clustered, in spatial or spectral domain, to generate an equivalent image representation that is invariant with respect to translation, rotation, and dilation. An efficient implementation scheme is also proposed, which is robust to distortions, insensitive to additive noise, and classifies the original image using adequate neural network architectures applied directly to 2D image representations. Third-order neural networks are shown to be a specific category of triple-correlation-based networks, applied either to binary or gray-scale images. A simulation study is given, which illustrates the theoretical developments, using synthetic and real image data.     

Invariant and adaptive geometrical texture features for defect detection and classification

Automatic texture defect detection is highly important for many fields of visual inspection. We introduce novel, geometrical texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational, Padé, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained, multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.     

Invariant texture classification for biomedical cell specimens via non-linear polar map filtering

A novel texture-based classification scheme for cell specimens that is robust over a range of orientation, scale and contrast values is proposed. We achieve this robustness by first segmenting the cell specimens and for each specimen, we find the largest ellipse that can be contained within it, and from this, we then construct an orientation and scale-invariant polar map. Non-linear filtering by normalized cross-correlation is then performed on the polar map to obtain contrast-invariant similarity maps. Local and global energy measures are finally extracted from these maps and classified using a support vector machine. Experimental results show that the proposed method achieves an average accuracy of about 97% in classifying six species of pollen, fungal and fern spores. In addition, every invariant property was validated through a series of experiments. Unlike conventional wavelet decomposition, Laws filtering and co-occurrence methods, our method shows a consistently high classification accuracy for all classes of cell specimens in an airspora dataset.     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Invariant pattern recognition using multiple filter image representations

In this paper we develop a 4-dimensional representation for patterns based on image decompositions via orientation- and size-specific filters. By retaining image positional information, this encoding scheme reduces pattern rotations, translations, and scale changes to shifts in the filter outputs. The appropriate correlation processes for matching are discussed and the recognition system is illustrated by a number of examples.     

Study of remote sensing image texture analysis and classification using wavelet

In the past one difficulty of texture analysis was the lack of adequate tools to characterize different scales of texture effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms, help to overcome this difficulty. This paper introduces a new approach to characterize texture at multiple scales. The performances of the wavelet transform are measured in terms of sensitivity and selectivity for the classification of 25 types of remote sensing texture relief images under the condition of different wavelet decomposition models, different filter lengths, different resolutions and different mother bodies. The reliability exhibited by texture signatures of wavelet transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.     

Multiscale directional filter bank with applications to structured and random texture retrieval

In this paper, multiscale directional filter bank (MDFB) is investigated for texture characterization and retrieval. First, the problem of aliasing in decimated bandpass images on directional decomposition is addressed. MDFB is then designed to suppress the aliasing effect as well as to minimize the reduction in frequency resolution. Second, an entropy-based measure on energy signatures is proposed to classify structured and random textures. With the use of this measure for texture pre-classification, an optimized retrieval performance can be achieved by selecting the MDFB-based method for retrieving structured textures and a statistical or model-based method for retrieving random textures. In addition, a feature reduction scheme and a rotation-invariant conversion method are developed. The former is developed so as to find the most representative features while the latter is developed to provide a set of rotation-invariant features for texture characterization. Experimental works confirm that they are effective for texture retrieval.     

Multiple classifier system using classification confidence for texture classification

Multimedia Tools and Applications - This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is...     

A multimodal liveness detection using statistical texture features and spatial analysis

Multimedia Tools and Applications - Biometric authentication can establish a person’s identity from their exclusive features. In general, biometric authentication can vulnerable to spoofing...     

Multiple resolution texture analysis and classification

Textures are classified based on the change in their properties with changing resolution. The area of the gray level surface is measured at serveral resolutions. This area decreases at coarser resolutions since fine details that contribute to the area disappear. Fractal properties of the picture are computed from the rate of this decrease in area, and are used for texture comparison and classification. The relation of a texture picture to its negative, and directional properties, are also discussed.     

Texture classification using fuzzy uncertainty texture spectrum

A new method using fuzzy uncertainty, which measures the uncertainty of the uniform surface in an image, is proposed for texture analysis. A grey-scale image can be transformed into a fuzzy image by the uncertainty definition. The distribution of the membership in a measured fuzzy image, denoted by the fuzzy uncertainty texture spectrum (FUTS), is used as the texture feature for texture analysis. To evaluate the performance of the proposed method. supervised texture classification and rotated texture classification are applied. Experimental results reveal high-accuracy classification rates and show that the proposed method is a good tool for texture analysis.     

A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis

Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests land-cover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold cross-validation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the land-use data needed by urban planners for effective planning in developing countries.     

Evaluation of the effects of Gabor filter parameters on texture classification

Gabor filtering is a widely adopted technique for texture analysis. The design of a Gabor filter bank is a complex task. In texture classification, in particular, Gabor filters show a strong dependence on a certain number of parameters, the values of which may significantly affect the outcome of the classification procedures. Many different approaches to Gabor filter design, based on mathematical and physiological consideration, are documented in literature. However, the effect of each parameter, as well as the effects of their interaction, remain unclear. The overall aim of this work is to investigate the effects of Gabor filter parameters on texture classification. An extensive experimental campaign has been conducted. The outcomes of the experimental activity show a significant dependence of the percentage of correct classification on the smoothing parameter of the Gabor filters. On the contrary, the correlation between the number of frequencies and orientations used to define a filter bank and the percentage of correct classification appeared to be poor.     

Recursive contextual classification using a spatial stochastic model

In the past decade, there have been numerous attempts to develop systems for automatic interpretation of digital image data. None of the systems developed for this purpose have made extensive use of context information. Since even manual interpretation of isolated point or area targets is difficult without the use of context, a machine which does not use context has a fundamental limitation.In the course of using contextual information, the first task is to present a stationary stochastic process on a two-dimensional plane. This process is then used as a model, such that correlations between any pair of image cells can be extracted. The model is characterized by a spatial correlation parameter. A flexible coding technique is presented by which the spatial correlation parameter can be estimated. From the coded patterns used for estimating the spatial correlation parameter, a recursive contextual classification procedure is proposed. Some modifications and extensions of the model are specifically developed or substantially refined during this investigation to cover more general situations.Finally, extensive experimental results with remotely sensed multispectral scanner data using the developed model for contextual classification are reported. Both single-stage and recursive contextual classification procedures are tested on real data. The classification results do show the effectiveness and efficiency of the developed contextual classifier.     

Entropy-based texture analysis in the spatial frequency domain

An approach which uses regional entropy measures in the spatial frequency domain for texture discrimination is presented. The measures provide texture discriminating information independent of that contained in the usual summed energy within based frequency domain features. Performance of the entropy features as measured by a between-to-within-class scatter criterion is comparable to that of traditional frequency domain features and gray level co-occurrence contrast features. A method of frequency scaling is introduced to enable the comparison of texture samples of different subimage size. The resulting regional entropy measures are subimage size-invariant subject to certain constraints which arise from properties of the discrete Fourier transform.