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Curcumin in Food Technology: Trends, Challenges, and Innovative Perspectives Through Bibliometrics. [PDF]
Food Sci NutrBueno VD, Souto AA.
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An Integrated Color and Intensity Co-occurrence Matrix
Pattern Recognition Letters, 2007The paper presents a novel approach for representing color and intensity of pixel neighborhoods in an image using a co-occurrence matrix. After analyzing the properties of the HSV color space, suitable weight functions have been suggested for estimating relative contribution of color and gray levels of an image pixel.
Shamik Sural, A Vadivel, A K Majumdar
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Image retrieval based on the texton co-occurrence matrix
Pattern Recognition, 2008This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of ...
Guang-Hai Liu
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Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition, 2007This paper is concerned with an approach to exploiting information available from the co-occurrence matrices computed for different distance parameter values. A polynomial of degree n is fitted to each of 14 Haralick's coefficients computed from the average co-occurrence matrices evaluated for several distance parameter values.
Antanas Verikas +2 more
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Co-occurrence Matrixes for the Quality Assessment of Coded Images
2007 IEEE International Symposium on Industrial Electronics, 2007Intrinsic nonlinearity complicates the modeling of perceived quality of digital images, especially when using feature-based objective methods. The research described in this paper indicates that models from Computational Intelligence can predict quality and cope with multi-dimensional data characterized by complex perceptual relationships.
REDI J +3 more
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Generalized co-occurrence matrix for multispectral texture analysis
Proceedings of 13th International Conference on Pattern Recognition, 1996We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM).
Markku Hauta-Kasari +3 more
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Rotation Invariant Co-occurrence Matrix Features
2017Grey level co-occurrence matrix (GLCM) has been one of the most used texture descriptor. GLCMs continue to be very common and extended in various directions, in order to find the best displacement for co-occurrence extraction and a way to describe this co-occurrence that takes into account variation in orientation.
PUTZU, LORENZO, DI RUBERTO, CECILIA
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Crowd Detection Based on Co-occurrence Matrix
2013This paper describes a new approach for crowd detection based on the analysis of the gray level dependency matrix (GLDM), a technique already exploited for measuring image texture. New features for characterizing the GLDM have been proposed, and both Adaboost and Bayesian classifiers have been applied to the new feature introduced, and the system has ...
GHIDONI, STEFANO +2 more
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Scene Classification by Feature Co-occurrence Matrix
2015Classifying scenes (such as mountains, forests) is not an easy task owing to their variability, ambiguity, and the wide range of illumination and scale conditions that may apply. Bag of features (BoF) model have achieved impressive performances in many famous databases (such as the 15 scene dataset).
Haitao Lang +4 more
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Image Denoising Based on the Wavelet Co-Occurrence Matrix
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006Image denoising is a well-known problem in signal processing. Wavelet decomposition based approaches have been applied successfully to the image denoising problem. The majority of wavelet thresholding methods do not take the spatial correlation between wavelet coefficients into account.
Zeyong Shan, Selin Aviyente
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