Статті в журналах: "Segmentation texture"

1

Volkova, Natalya P., and Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS." Herald of Advanced Information Technology 3, no. 4 (November 20, 2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.

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The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in the field variations and quasi-harmonics. On its basis, a vector-difference method of texture segmentation has been developed, which is based on the vector transformation of images of spectral and statistical textures based on vector algebra. The stages of the vector-difference method are the following: an evaluation of the spectral texture feature; an evaluation of the statistical texture feature; vector-difference transformation of texture images; a boundary detection of the homogeneous regions. For each pixel of the image in the processing aperture, the features of the spectral and statistical texture are evaluated. Statistical texture evaluation was performed by the quadratic-amplitude transformation. At the stage of vector-difference transformation of texture images, a vector of features of each pixel of an image is constructed, the elements of which are estimates of features of a spectral and statistical texture, and the modulus of the difference of two vectors is calculated. At the stage of boundary detection of homogeneous regions, Canny method was applied. The developed vector-difference texture segmentation method was applied both to model images of spectral-statistical texture and to texture images obtained in technical and medical diagnostics systems, namely, for images of psoriasis disease and wear zones of cutting tools. To compare the segmentation results, frequency-detector and amplitude-detector methods of texture segmentation were applied to these images. The quality of segmentation of homogeneous textured regions was evaluated by the Pratt's criterion and by constructing a confusion matrix. The research results showed that the developed vector-difference texture segmentation method has increased noise tolerance at a sufficient processing speed.

2

Song, Andy, and Vic Ciesielski. "Texture Segmentation by Genetic Programming." Evolutionary Computation 16, no. 4 (December 2008): 461–81. http://dx.doi.org/10.1162/evco.2008.16.4.461.

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This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

3

Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli, and Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification." Sensors 20, no. 18 (September 22, 2020): 5432. http://dx.doi.org/10.3390/s20185432.

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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

4

Rosenholtz, R. "What Statistics Determine Segmentation of Orientation-Defined Textures?" Perception 26, no. 1_suppl (August 1997): 331. http://dx.doi.org/10.1068/v970074.

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Beck suggested that texture segmentation is based upon differences in the first-order statistics of stimulus features such as orientation, size, and contrast. However, this theory does not indicate how these differences might be quantified, or what properties of the statistics might be used. Some alternative models postulate that texture segmentation is determined by the responses of spatial-frequency channels, where the channels contain both a linear filtering mechanism and various nonlinearities. Such models do a good job of predicting human performance, but do not give us much insight into what textures will segment, since the comparison carried out by the model is often obscured by the details of the filtering, nonlinearity, and image-based decision processes. It is suggested here that, for orientation-defined textures (eg in which each ‘texel’ has a single orientation), segmentation is well-described by something like the ‘significance’ of the differences between (1) the mean orientations, and (2) the angular variances of the two textures. The ‘significance’ of the difference in means takes into account the variability in the texture, so that two homogeneous textures with means differing by 30° may easily segment, while two heterogeneous textures with the same difference in mean may not. Furthermore, it is shown that these statistics may be computed in a biologically plausible way, which greatly resembles the typical filter-based approaches to texture segmentation. Thus the connection between statistical theories of texture segmentation and spatial-frequency channel models becomes more transparent.

5

Wang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan. "Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.

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Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.

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<abstract> Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc. </abstract>

7

Goyal, Aparna, and Reena Gunjan. "Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review." E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.

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Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

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Anjaiah, P., K. Rajendra Prasad, and C. Raghavendra. "Effective Texture Features for Segmented Mammogram Images." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 666. http://dx.doi.org/10.14419/ijet.v7i3.12.16450.

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Textures of mammogram images are useful for finding masses or cancer cases in mammography, which has been used by radiologist. Textures are greatly succeed for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) in most commonly used technique for mammogram segmentation. Limitation of this method is that it unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly for finding best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.

9

Tripathi, Bharat, Nidhi Srivastava, and Amod Kumar Tiwari. "Innovative quaternion algebra-based segmentation for improved jpeg color texture analysis." Journal of Autonomous Intelligence 7, no. 5 (April 10, 2024): 1499. http://dx.doi.org/10.32629/jai.v7i5.1499.

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Color texture analysis is a critical component in various computer vision and image processing applications, including object recognition, medical imaging, and remote sensing. Traditional methods like J-Segmentation for color texture segmentation often struggle with capturing complex textures and maintaining color fidelity, especially when dealing with JPEG-compressed images. Quaternion Algebra’s unique ability to represent and manipulate color information in a multidimensional space allows for more accurate feature extraction and segmentation. Our approach Quaternion neural network (QNN) not only improves the segmentation accuracy but also preserves the visual quality of the segmented regions in JPEG images. We demonstrate the effectiveness of our method through extensive experimentation on diverse datasets, showcasing its superiority over existing techniques. The proposed approach not only achieves state-of-the-art results in terms of segmentation accuracy but also offers computational efficiency. This innovation holds great promise for applications in image analysis, computer vision, and medical imaging, where accurate color texture segmentation is paramount.

10

Manokhin, Denys, and Yaroslav Sokolovskyy. "Intracranial Hemorrhage Segmentation using Neural Network and Riesz Fractional Order Derivative-Based Texture Enhancement." Computer Design Systems. Theory and Practice 6, no. 1 (2024): 1–16. http://dx.doi.org/10.23939/cds2024.01.001.

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This paper explores the application of the U-Net architecture for intracranial hemorrhage segmentation, with a focus on enhancing segmentation accuracy through the incorporation of texture enhancement techniques based on the Riesz fractional order derivatives. The study begins by conducting a review of related works in the field of computed tomography (CT) scan segmentation. At this stage also a suitable dataset is selected. Initially it is used to train the UNet, one of the widely adopted deep learning models in the field of medical image segmentation. Training is performed using parallel algorithm based on CUDA technology. The obtained results are compared with the established baseline for this dataset, assessing segmentation accuracy using the Jaccard and Dice coefficients. Subsequently, the study investigates a texture enhancement technique based on the Riesz fractional order derivatives, applied to the CT-scan images from the dataset. This technique aims to capture finer details and subtle textures that may contribute to improved segmentation accuracy. The U-Net model is then retrained and validated on the texture-enhanced images, and the experimental results are analyzed. The study reveals a modest yet notable enhancement in accuracy, as measured by the Jaccard and Dice coefficients, demonstrating the potential of the proposed texture enhancement technique in refining intracranial hemorrhage segmentation.

11

LIU, Guoxiang, and Shunichiro OE. "Texture Image Segmentation by Detecting Texture Edges." IEEJ Transactions on Electronics, Information and Systems 122, no. 5 (2002): 808–15. http://dx.doi.org/10.1541/ieejeiss1987.122.5_808.

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12

Harrison, S. J., and D. R. T. Keeble. "Within-texture alignment improves human texture segmentation." Ophthalmic and Physiological Optics 22, no. 6 (November 2002): 580–81. http://dx.doi.org/10.1046/j.1475-1313.2002.00086_32.x.

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13

Harrison, S. J., and D. R. T. Keeble. "Within-texture collinearity improves human texture segmentation." Vision Research 48, no. 19 (September 2008): 1955–64. http://dx.doi.org/10.1016/j.visres.2008.06.008.

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Xiang, Ming, Zhen Dong Cui, and Yuan Hong Wu. "A Fingerprint Image Segmentation Method Based on Fractal Dimension." Advanced Materials Research 461 (February 2012): 299–301. http://dx.doi.org/10.4028/www.scientific.net/amr.461.299.

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Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations

15

Kumar, Upendra. "Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm." International Journal of Computer Vision and Image Processing 7, no. 1 (January 2017): 44–58. http://dx.doi.org/10.4018/ijcvip.2017010103.

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Considering Retinal image as textured image, its texture based segmentation is required to identify the presence of retinal diseases. This pre-processing is important in automatic detection system for recognizing the abnormality present in the retinal images. Likewise, the proposed system mainly focused on diabetic retinopathy disease caused into eye –retina, generally leads to eye-blindness. Inspired from robust human's texture based segmentation capability, a mathematical model of the eye was formulated. A texture based Gabor filter was applied to get the output feature helping in detecting the abnormality and deriving statistical properties, further used in segmentation and classification. This work deals with the better separation of various clusters of Gabor filter output features, in order to get better segmentation efficiency. This was also followed by formalizing an objective function to tune filter parameters with Gradient descent and further Genetic Algorithm. This paper showed both qualitative and quantitative segmentation results with improved efficiency.

16

Yuan, Jiangye, Deliang Wang, and Anil M. Cheriyadat. "Factorization-Based Texture Segmentation." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 3488–97. http://dx.doi.org/10.1109/tip.2015.2446948.

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17

Appelbaum, G., A. Wade, M. Pettet, V. Vildavski, and A. M. Norcia. "Dynamics of texture segmentation." Journal of Vision 5, no. 12 (December 1, 2005): 12. http://dx.doi.org/10.1167/5.12.12.

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18

Shi, Lilong, and Brian Funt. "Quaternion color texture segmentation." Computer Vision and Image Understanding 107, no. 1-2 (July 2007): 88–96. http://dx.doi.org/10.1016/j.cviu.2006.11.014.

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19

Tuceryan, Mihran. "Moment-based texture segmentation." Pattern Recognition Letters 15, no. 7 (July 1994): 659–68. http://dx.doi.org/10.1016/0167-8655(94)90069-8.

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Fan, Lin, Zi-long Deng, Yan He, Xu-long Zhu, Xing-jun Gao, and Zhe Jin. "The effects of micro-texture shape on serrated chip geometry in the hardened steel AISI D2 cutting process." Surface Topography: Metrology and Properties 10, no. 1 (March 1, 2022): 015031. http://dx.doi.org/10.1088/2051-672x/ac58ad.

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Abstract Five micro-textures were processed on the rake face of PCBN tools: circular pits, elliptical grooves, transverse grooves, composite grooves, and wavy grooves. The effect of different micro-textures on cutting force, the cutting temperature, the micro-morphology of serrated chips, tool wear and surface roughness were investigated utilizing three-dimensional finite element simulation and cutting tests. The results indicates that micro-textured tools can lower cutting force when compared to non-textured tools, while cutting force varies significantly during the cutting process. Micro-texture can decrease the temperature in the adiabatic shear band, changes the temperature distribution of the rake face, reduce the serrated degree, and increase segmentation frequency. However, it is more prone to cracking. The wear resistance and the surface quality of machined surface of the elliptical grooves and wavy grooves micro-texture are better.

21

K., Prabhakar, and Sadyojatha K.M. "Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation." International journal of electrical and computer engineering systems 14, no. 4 (April 26, 2023): 371–79. http://dx.doi.org/10.32985/ijeces.14.4.1.

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In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on the level set that enables efficient segmentation in both low and high-resolution images with automatic selection of the filter size. In the proposed model, the multi-resolution feature obtained from the frequency domain filters enhances the dissimilarity between the regions of crosshatched textures that have low-intensity variations. Then, the resultant images are integrated with a level set-based active contour model that addresses the segmentation of crosshatched texture images. The noise added during the segmentation process is eliminated by morphological processing. The experiments conducted on the Brodatz texture dataset demonstrated the effectiveness of the proposed model, and the obtained results are validated in terms of Intersection over the Union (IoU) index, accuracy, precision, f1-score and recall. The extensive experimental investigation shows that the proposed model effectively segments the region of interest in close correspondence with the original image. The proposed segmentation model with a multi-support vector machine has achieved a classification accuracy of 99.82%, which is superior to the comparative model (modified convolutional neural network with whale optimization algorithm). The proposed model almost showed a 0.11% improvement in classification accuracy related to the existing model.

22

Liu, Xinlin, Viktor Krylov, Su Jun, Natalya Volkova, Anatoliy Sachenko, Galina Shcherbakova, and Jacek Woloszyn. "Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology." Mathematical Biosciences and Engineering 19, no. 7 (2022): 6923–39. http://dx.doi.org/10.3934/mbe.2022326.

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<abstract> An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease. </abstract>

23

Li, Xinhui, Mingjia Li, Yaxing Wang, Chuan-Xian Ren, and Xiaojie Guo. "Adaptive Texture Filtering for Single-Domain Generalized Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1442–50. http://dx.doi.org/10.1609/aaai.v37i2.25229.

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Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.

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Peromaa, T.-L., and P. I. Laurinen. "An Illusory Contour Induces Texture Segmentation." Perception 26, no. 1_suppl (August 1997): 310. http://dx.doi.org/10.1068/v970086.

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It is known that neurons in V1 can signal a contour between two out-of-phase gratings [Grosof et al, 1993 Nature (London)365 550 – 552]. We demonstrate that this type of illusory contour can segregate areas of surfaces without any luminance, contrast, or textural difference between the areas. We have studied the conditions under which the illusory contour induces texture segmentation. The target was a circular contrast-inverted area (diameter 0.22 – 8.3 deg) in the centre of an isotropic narrow-band noise texture (centre spatial frequency 0.4 – 7 cycles deg−1). Generally, segmentation was effortless in low-spatial-frequency textures but gradually disappeared with increasing spatial frequency although the contour remained visible. In a staircase experiment, the highest spatial frequency allowing segmentation was measured for each target size. The task was to tell whether the stimulus contained an object or just a contour. A negative power function relates the target diameter and the highest spatial frequency allowing segmentation. The visibility of the contour was independent of the target size. The illusory contour ‘captures’ the texture inside. However, the process is spatially limited. In a separate experiment, the subject used a cursor to point out how far from the contour the capture spreads. A negative relationship between the spatial spread and the spatial frequency of the texture was found. These findings are consistent with the idea that low-level mechanisms signalling illusory contours are involved in perceptual scene segmentation.

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Palagi, P. M., and A. Guérin-Dugué. "Simulation of Cortex Visual Cells for Texture Segmentation: Foveal and Parafoveal Projections." Perception 25, no. 1_suppl (August 1996): 120. http://dx.doi.org/10.1068/v96l0708.

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The objective of this work is to simulate visual cortical cells, their sensitivities to frequencies and orientations, and their part in texture segmentation. The simulation of these cells is realised through band-pass, oriented filters (Gabor filters), and multiresolution image decomposition. By this means, the filter sensitivities represent cell sensitivities to preferred orientations according to their frequency and orientation bandwidths, and multiresolution represents the different band frequencies. For texture analysis and segmentation, overlaying of band-pass filters is necessary to completely cover the Fourier domain. A continuous sensitivity to frequency and orientation is achieved by the filters overlapping and consequently by their interpolation. We used here four octave frequency bands from 1 to 16 cycles deg−1 and six orientations per band. The results obtained for texture segmentation with these parameters are very promising (up to 97% recognition rate) [Guérin-Dugué and Palagi, 1994 Neural Processing Letters1(1) 25 – 29]. The images analysed cover a multitude of different domains such as psychophysical tests and natural textures of different roughness. In order to create a cortical cell representation closer to neurophysiological data, and to improve texture segmentation results, we represent cell sensitivities by their foveal and parafoveal projections [R L DeValois, K K DeValois, 1988 Spatial Vision (Oxford: Oxford Science Publications)]. Cells receiving projections from the foveal zone are modeled by five octave frequency bands (from 0.5 to 16 cycles deg−1) and six orientations. Cells receiving projections from the parafoveal zone have the same sensitivities but are modeled by four octave frequency bands (from 0.5 to 8 cycles deg−1). By using these two different resolutions, preliminary tests have shown the capability of detecting textured regions by the parafoveal projection and localisation of boundaries by the foveal projection.

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Berezina, S. I., Yu O. Gordienko, and O. I. Solonets. "ANALYSIS OF WAYS OF SOLVING THE SEGMENTATION PROBLEM FOR HIGHLY TEXTURED OBJECTS." Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем, no. 17 (December 30, 2019): 27–40. http://dx.doi.org/10.46972/2076-1546.2019.17.03.

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Increment of speed and reliability of aerospace images processing is directly related to solution of the task of automation of images interpretation process, which is achieved by minimizing search areas, detecting masked objects and defining the dynamics of changes in surveillance areas. The primary stage that in general determines the quality of results received by automated processing and interpretation is thematic segmentation of the image. In the process of thematic segmentation it is necessary to take into account presence of a large number of textured objects. The paper analyzes the ways of solving the segmentation problem for highly textured objects with large range of variation of possible color values. The research included separation of woodlands and single plants from meadows, steppes, etc., which are characterized by similar color characteristics, but differ in texture. It also included separation of residential areas from forests, which are characterized by the same grain size of texture and different color characteristics. The method of texture description, which is based on calculation of the number of differences in brightness per unit area of the image, the method of description and measurement of texture, characterized by the length of the series, the methods of texture description based on calculation of their fractal dimension have been investigated. In order to describe the texture by different methods, first of all, an aperture of the analysis window was defined. That aperture ensures separation of different classes of objects. The analyzed methods of texture description showed that areas of false identification are always present in the result images. It was determined that the best results were obtained with two of the discussed methods. The first one was the method of texture description and measurement based on calculation of the number of brightness differences per unit area of the image. The second one was the method of texture description based on calculation of fractal dimension by searching the area of the pyramid which covers image fragments. To obtain a more accurate segmented map of an image containing highly textured fragments, a combination of the two methods is suggested.

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Salhi, Khalid, El Miloud Jaara, and Mohammed Talibi Alaoui. "Texture Image Segmentation Approach Based on Neural Networks." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 1 (March 19, 2018): 19. http://dx.doi.org/10.3991/ijes.v6i1.8166.

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One of the major problems in texture analysis is segmenting images into different regions based on textures. In this paper, we present a new approach of texture segmentation, which is based on both Kohonen maps and mathematical morphology, using three different texture features, namely, Haralick features based on gray-level co-occurrence matrix (GLCM), fractal features based on fractal dimension using the differential box counting method, and wavelet features based on wavelet transform. These features are used to train the Kohonen Network, which will be represented by the underlying probability density function (PDF). The segmentation of this map’s representation is made by morphological watershed transformation. In the final part of our algorithm, this will help on the segmentation of the textural image, by assigning each pixel to a modal region extracted from the map. Our work covers the results obtained by the three extraction methods taking into consideration the execution time and the error rate.

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Zhu, Jinchao, Xiaoyu Zhang, Shuo Zhang, and Junnan Liu. "Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3599–607. http://dx.doi.org/10.1609/aaai.v35i4.16475.

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Camouflaged objects, similar to the background, show indefinable boundaries and deceptive textures, which increases the difficulty of detection task and makes the model rely on features with more information. Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation. Motivated by the complementary relationship between texture labels and camouflaged object labels, we propose an interactive guidance framework named TINet, which focuses on finding the indefinable boundary and the texture difference by progressive interactive guidance. It maximizes the guidance effect of refined multi-level texture cues on segmentation. Specifically, texture perception decoder (TPD) makes a comprehensive analysis of texture information in multiple scales. Feature interaction guidance decoder (FGD) interactively refines multi-level features of camouflaged object detection and texture detection level by level. Holistic perception decoder (HPD) enhances FGD results by multi-level holistic perception. In addition, we propose a boundary weight map to help the loss function pay more attention to the object boundary. Sufficient experiments conducted on COD and SOD datasets demonstrate that the proposed method performs favorably against 23 state-of-the-art methods.

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SHIRVAIKAR, MUKUL V., and MOHAN M. TRIVEDI. "TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 04 (December 1995): 431–49. http://dx.doi.org/10.1142/s0218488595000220.

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The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed. The texture model is based on sets of gray level cooccurence (GLC) matrices rather than measures extracted from them. The algorithmic constituents for the segmentation scheme: choice of seed regions, normalized match distances between texture models, region homogeneity, and aggregation criteria are systematically developed. The unsupervised algorithm works so that “seed” regions are discovered by an image search process. Initial estimates of the texture model prototypes are automatically computed for each “seed” region, and classification thresholds are based on the variance of the model over the “seed” region. An aggregation process then results in regions being successively classified and segmented “out” of the image. This recursive process of segmentation is continued until all pixels are classified. The segmentation strategy was tested successfully on natural texture mosaics. The results are analytically presented. These experiments demonstrate that the unsupervised process can correctly identify the perceptual constituents of the image based on texture.

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Wang, Zhan, Yun Hui Yan, De Wei Dong, and Ke Chen Song. "Texture Segmentation of Natural Images Based on Active Contour Model." Advanced Materials Research 546-547 (July 2012): 553–58. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.553.

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To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

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Yessenova, Moldir, Gulzira Abdikerimova, Nurgul Baitemirova, Galia Mukhamedrakhimova, Karipola Mukhamedrakhimov, Zeinigul Sattybaeva, Indira Salgozha, and Akbota Yerzhanova. "The applicability of informative textural features for the detection of factors negatively influencing the growth of wheat on aerial images." Eastern-European Journal of Enterprise Technologies 4, no. 2(118) (August 31, 2022): 51–58. http://dx.doi.org/10.15587/1729-4061.2022.263433.

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Automated processing of aerospace information makes it possible to effectively solve scientific and applied problems in cartography, ecology, oceanology, exploration and development of minerals, agriculture and forestry, and many other areas. At the same time, the main way to extract information is to decipher images, which are the main carrier of information about the area. Aerospace images are a combination of natural texture regions and man-made objects. This article discusses methods for analyzing texture images. The main tasks of the analysis of texture areas include the selection and formation of features that describe texture differences, the selection and segmentation of texture areas, the classification of texture areas, and the identification of an object by texture. Depending on the features of the texture areas of the images used, segmentation methods based on area analysis can be divided into statistical, structural, fractal, spectral, and combined methods. The article discusses textural features for the analysis of texture images, and defines informative textural features to identify negative factors for crop growth. To solve the tasks, textural features are used. Much attention is paid to the development of software tools that allow to highlight the features that describe the differences in textures for the segmentation of texture areas. This approach is universal and has great potential on the studied aerospace image to identify objects and boundaries of regions with different properties using clustering based on images of the same surface area taken in different vegetation periods. That is, the question of the applicability of sets of texture features and other parameters for the analysis of experimental data is being investigated.

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S.Md, Mansoor Roomi, Mareeswari M, and Maragatham G. "Segmentation Using ‘New’ Texture Feature." Advanced Computing: An International Journal 7, no. 1/2 (March 31, 2016): 39–49. http://dx.doi.org/10.5121/acij.2016.7205.

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33

Chaudhuri, B. B., and N. Sarkar. "Texture segmentation using fractal dimension." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 1 (1995): 72–77. http://dx.doi.org/10.1109/34.368149.

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34

Tuceryan, M., and A. K. Jain. "Texture segmentation using Voronoi polygons." IEEE Transactions on Pattern Analysis and Machine Intelligence 12, no. 2 (1990): 211–16. http://dx.doi.org/10.1109/34.44407.

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Jacquelin, Christophe, André Aurengo, and Gilles Hejblum. "Evolving descriptors for texture segmentation." Pattern Recognition 30, no. 7 (July 1997): 1069–79. http://dx.doi.org/10.1016/s0031-3203(96)00150-1.

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36

Farran, Emily K., and Kate Wilmut. "Texture segmentation in Williams syndrome." Neuropsychologia 45, no. 5 (2007): 1009–18. http://dx.doi.org/10.1016/j.neuropsychologia.2006.09.005.

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37

Arivazhagan, S., and L. Ganesan. "Texture segmentation using wavelet transform." Pattern Recognition Letters 24, no. 16 (December 2003): 3197–203. http://dx.doi.org/10.1016/j.patrec.2003.08.005.

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38

Hoang, Minh A., Jan-Mark Geusebroek, and Arnold W. M. Smeulders. "Color texture measurement and segmentation." Signal Processing 85, no. 2 (February 2005): 265–75. http://dx.doi.org/10.1016/j.sigpro.2004.10.009.

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39

Jain, Anil K., and Yu Zhong. "Page segmentation using texture analysis." Pattern Recognition 29, no. 5 (May 1996): 743–70. http://dx.doi.org/10.1016/0031-3203(95)00131-x.

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40

Yhann, S. R., and T. Y. Young. "Boundary localization in texture segmentation." IEEE Transactions on Image Processing 4, no. 6 (June 1995): 849–56. http://dx.doi.org/10.1109/83.388089.

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41

Laine, A., and Jian Fan. "Frame representations for texture segmentation." IEEE Transactions on Image Processing 5, no. 5 (May 1996): 771–80. http://dx.doi.org/10.1109/83.499915.

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42

Yu, Sheng-yang, Yan Zhang, Yong-gang Wang, and Jie Yang. "Unsupervised color-texture image segmentation." Journal of Shanghai Jiaotong University (Science) 13, no. 1 (February 2008): 71–75. http://dx.doi.org/10.1007/s12204-008-0071-2.

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43

Sagi, Dov. "The psychophysics of texture segmentation." Spatial Vision 7, no. 1 (1993): 83. http://dx.doi.org/10.1163/156856893x00090.

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44

Kolekar, M. H., S. N. Talbar, and T. R. Sontakke. "Texture Segmentation using Fractal Signature." IETE Journal of Research 46, no. 5 (September 2000): 319–23. http://dx.doi.org/10.1080/03772063.2000.11416172.

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45

Norman, Liam J., Charles A. Heywood, and Robert W. Kentridge. "Texture segmentation without human V4." Visual Cognition 25, no. 1-3 (March 16, 2017): 184–95. http://dx.doi.org/10.1080/13506285.2017.1301612.

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46

Wilkinson, Frances. "Visual texture segmentation in cats." Behavioural Brain Research 19, no. 1 (January 1986): 71–82. http://dx.doi.org/10.1016/0166-4328(86)90049-5.

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47

Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprsarchives-xli-b1-341-2016.

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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.

48

Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprs-archives-xli-b1-341-2016.

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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.

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MUNEESWARAN, K., L. GANESAN, S. ARUMUGAM, and P. HARINARAYAN. "A NOVEL APPROACH COMBINING GABOR WAVELET AND MOMENTS FOR TEXTURE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 03, no. 04 (December 2005): 559–72. http://dx.doi.org/10.1142/s0219691305001020.

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In this work, an effective method has been proposed for texture segmentation, which incorporates the best features of filter bank and statistical approaches. This technique combines the features of Gabor wavelets (filter based) and General Moments (statistical) approaches. The method has been successfully tested for various textures from Brodatz texture collection. The relative performance of this method against the conventional approaches has been analyzed using Fisher Criterion.

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SANEI, SAEID. "TEXTURE SEGMENTATION USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES." International Journal of Computational Intelligence and Applications 04, no. 02 (June 2004): 131–42. http://dx.doi.org/10.1142/s1469026804001197.

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Segmentation of natural textures has been investigated by developing a novel semi-supervised support vector machines (S3VM) algorithm with multiple constraints. Unlike conventional segmentation algorithms the proposed method does not classify the textures but classifies the uniform-texture regions and the regions of boundaries. Also the overall algorithm does not use any training set as used by all other learning algorithms such as conventional SVMs. During the process, the images are restored from high spatial frequency noise. Then various-order statistics of the textures within a sliding two-dimensional window are measured. K-mean algorithm is used to initialise the clustering procedure by labelling part of the class members and the classifier parameters. Therefore at this stage we have both the training and the working sets. A non-linear S3VM is then developed to exploit both sets to classify all the regions. The convex algorithm maximises a defined cost function by incorporating a number of constraints. The algorithm has been applied to combinations of a number of natural textures. It is demonstrated that the algorithm is robust, with negligible misclassification error. However, for complex textures there may be a minor misplacement of the edges.

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