نتایج جستجو برای: texture colour quality

تعداد نتایج: 818882  

Journal: :Image Vision Comput. 2009
Mohammad Faizal Ahmad Fauzi Paul H. Lewis

The motivation for research on low-quality images comes from a requirement by some museums to respond to queries for pictorial information, submitted in the form of fax messages or other low-quality monochrome images of works of art. The museums have databases of high-resolution images of their artefact collections and the person submitting the query is asking typically whether the museum holds...

Journal: :CoRR 2000
Vitorino Ramos Fernando Muge

Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, a method is described for evolving adaptive procedures for these problems. In many real world applicatio...

2004
Peter Howarth Stefan M. Rüger

We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view. The features were evaluated and tested on retrieval tasks from the Corel and TRECVID2003 image collections. For the latter we also ...

1997
P. Scheunders S. Livens G. Van de Wouwer

In this paper, texture analysis based on wavelet transformations is elaborated. The paper is meant as a practical guideline through some aspects of a wavelet-based texture analysis task. The following aspects of the problem are discussed: discrete and continuous wavelet decompositions, texture features for grey-level textures, extensions to colour texture and rotation-invariant features, and cl...

Journal: :پژوهش های علوم و صنایع غذایی ایران 0
milad fathi seyed. m. a razavi

in this study, potential application of image texture analysis as a non-destructive method for automation and prediction of mechanical properties of carrot chips was investigated. samples were fried at different processing conditions and moisture content, colour parameters (i.e. l*, a*, b* and e) and mechanical properties (i.e. hardness and apparent modulus) were determined. hardness and appar...

2009
Jeremiah Deng Jeremiah D. Deng

Sapstain is considered a defect that must be removed from processed wood. So far, research in automatic wood inspection systems has been mostly limited to dealing with knots. In this paper, we extract a number of colour and texture features from wood pictures. These features are then assessed using machine learning techniques via feature selection, visualization, and finally classification. Apa...

1992
Kevin Richards Geoffrey D. Sullivan

We describe methods for using colour and texture to discriminate cloud and sky in images captured using a ground based colour camera. Neither method alone has proved sufficient to distinguish between different types of cloud, and between cloud and sky in general. Classification can be improved by combining the features using a Bayesian scheme.

2015
António F. Martins Michel Bessant Liana Manukyan Michel C. Milinkovitch Walter Salzburger

While recent imaging techniques provide insights into biological processes from the molecular to the cellular scale, phenotypes at larger scales remain poorly amenable to quantitative analyses. For example, investigations of the biophysical mechanisms generating skin morphological complexity and diversity would greatly benefit from 3D geometry and colour-texture reconstructions. Here, we report...

2000
Janet S. Payne T. J. Stonham

Content-based image retrieval (CBIR) uses image properties such as colour, texture and shape. Although colour has been used successfully, texture can be even more significant, both for human perception and for CBIR. We present the results from a human study with 30 volunteers that ranks the “most like” images for each of the Brodatz textures. In all cases, only a limited number of the other tex...

1997
K. Messer

In this paper two methods for selecting input features for a neural network used to aid iconic retrieval in an image database are presented and compared. The rst method involves training the network on all the feature inputs and then analysing the weight values in an attempt to nd the more important input features. The second borrows a method from statistical feature selection known as the sequ...

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