71,426 research outputs found
Modern technologiesof restructured meat productes
Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features, and the combinations of multiple algorithms; while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism; and therefore can be used as a useful tool for both structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching, and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the UIUC texture database suggest the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval
Aplicación del análisis en componentes independientes (ICA) a la clasificación de imágenes
El presente trabajo tiene como objeto el desarrollo de un clasificador de texturas y para ello se usa la base de
datos de Columbia-Utrecht (CUReT). El desarrollo del clasificador se apoya en el paper publicado por Manik
Varma y Andrew Zisserman: “A Statistical Approach to Texture Classification from Single Images”.
Como aporte original se emplea el uso de una técnica matemática, llamada, análisis en componentes
independientes (ICA) para conseguir el banco de filtros que permitirán obtener las características de las
texturas. Esto permite poder comparar con los filtros que, los mencionados autores, utilizan en su trabajo.
El presente documento aporta un nuevo enfoque puesto que los filtros obtenidos a partir de ICA son filtros
dependientes de las propias texturasThe purpose of this work is the development of a textures classifier using the Columbia-Utrecht database
(CUReT). The development of the classifier is based on the paper published by Manik Varma and Andrew
Zisserman: "A Statistical Approach to Texture Classification from Single Images".
As original contribution, the use of a mathematical technique, analysis of independent components (ICA), is
used to get the bank of filters that will allow us to get the characteristics of the textures. This allows us to
compare with the filters that the mentioned authors use in their work providing a new approach because the
filters obtained from ICA are filters dependent on the textures themselves.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació
Pixel classification by morphological granulometric features
Pixel classification systems rely on a certain set of features that are sufficient to classify a given pixel into a class defined within a database. Unlike brightness and spectral signature features commonly used in remote sensing applications, texture-based features cannot be defined for a single pixel and must be derived from an area or window surrounding that pixel. In this research, all features are derived from binary morphological granulometries. Once generated, these features comprise a database which can be used to classify images. A Gaussian Maximum Likelihood Classifier is trained with this data base for subsequent classification of both dependent and independent data. Several aspects of these texture-base features require investigation in order to determine their ability to distinguish image textures. Three important aspects are addressed in this study; the effects of maximum noise, the optimal size of the localized window, and the minimum number of optimal features required for accurate classification. A statistical approach has been taken to determine the classification accuracy with varying window size, varying number of features, and varying amounts of four types of maximum noise using granulometric features. Analysis of these investigations indicate four main results. First, classification accuracy in the absence of noise is extremely high. Second, for these textures at the spatial resolution of 75 dpi, classification accuracy decreases dramatically below a window size of 11x11 pixels. Third, the number of features needed for high classification accuracy can be reduced to a fairly small number on the order of 6 features. Finally, these features are generally robust in the presence of maximum noise if the type and amount of noise can be accurately estimated
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
Modeling of evolving textures using granulometries
This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
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