نتایج جستجو برای: color co occurrence matrix

تعداد نتایج: 936278  

2014
J. Kavikumar N. S. Manian M. B. K. Moorthy

The main purpose of this paper is to consider the t-best co-approximation and t-best simultaneous co-approximation in fuzzy normed spaces. We develop the theory of t-best co-approximation and t-best simultaneous co-approximation in quotient spaces. This new concept is employed us to improve various characterisations of t-co-proximinal and t-co-Chebyshev sets.

Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...

1996
M.Babu Rao

There is a great need of developing efficient content based image retrieval systems because of the availability of large image databases. A new image retrieval system CTDCIRS (color-texture and dominant color based image retrieval system) to retrieve the images using three features called dynamic dominant color (DDC), Motif co-occurrence matrix (MCM) and difference between pixels of scan patter...

2003
Xavier Lladó Arnau Oliver Maria Petrou Jordi Freixenet Joan Martí

In this paper we investigate the effect of the illuminant tilt rotation over surface textures by analysing a set of image texture features extracted from the co-occurrence matrix. From the behaviour of each texture feature, a simple method able to predict the illuminant tilt angle of test images is developed. Moreover, the method is also used to perform a texture classification invariant to the...

2012
Stefano Ghidoni Grzegorz Cielniak Emanuele Menegatti

This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This i...

Journal: :Journal of Social Structure 2013
Frank Tutzauer

An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. This article introduces a family of affiliation measures that captures the extent of actors‘ affiliations in the network. At one extreme, one might have an actor who attended many events, but none of these events were attended by any of the other actors in th...

2005
Jianfei Yang Takeshi Ohashi Takuo Yasunaga

This paper describes that actomyosin complex particles are automatically selected. We propose a new approach, which combines both gray level co-occurrence matrix to extract texture features and SVM classifier to detect actomyosin complex particles automatically. Experimental results show that detection rate achieves 93.58%, the false positive rate is 3.66%, and the area under the ROC curve (AUC...

2012

In the previous chapter, an integrated approach for texture classification using ILCLBP-T is proposed. In continuation to that, the present chapter derived a new co-occurrence matrix based on textons and texture orientation for rotation invariant texture classification of 2D images. The new co-occurrence matrix is called as Texton and Texture Orientation Co-occurrence Matrix (T&TO-CM). The Co-o...

2010
M Arabi Gayatri Joshi Kavya P Parameswaran Abhik Raj Subedi

Noises are always present in an image posing higher or lower complexity in removal and however it is necessary to remove those noises to obtain a better image. There are various types of filters available that help us to remove those noises; only when a right type of filter is used the best result could be obtained. This paper compares the performance of filters namely median, Weiner, average a...

Journal: :Computers & Geosciences 2013
Christoph Georg Eichkitz Johannes Amtmann Marcellus Gregor Schreilechner

Seismic interpretation can be supported by seismic attribute analysis. Common seismic attributes use mathematical relationships based on the geometry and the physical properties of the subsurface to reveal features of interest. But they are mostly not capable of describing the spatial arrangement of depositional facies or reservoir properties. Textural attributes such as the grey level co-occur...

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