New fuzzy model for morphological colour image processing

Louverdis, G.; Andreadis, Ioannis; Tsalides, Ph.

doi:10.1049/ip-vis:20020380

Cited by 24 publications

(9 citation statements)

References 10 publications

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“…Extension of mathematical morphology to multi or hyperspectral image data is not straightforward. Application of grayscale methods to each band of a multiband image is not recommended due to the existence of inherent correlation between the spectral components of various bands and a possible loss of information due to the introduction of extrinsic pixel data values [21]. The concept of vector-valued signals [22,23] where different components of the vector contain different parts of the signal spectrum is commonly employed for handling multi-channel data by treating the data at each pixel as a vector.…”

Section: Pure and Mixed Pixel Segregationmentioning

confidence: 99%

An Efficient Hybrid Classification System for High Resolution Remote Sensor Data

Tamma¹,

Rao²,

Jaisankar³

2013

AJRS

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The classification of aerial and satellite remote sensing data has become a challenging problem due to the recent advances in remote sensor technology that led to higher spatial and spectral resolutions. This research paper presents novel sensor independent algorithms and techniques for dealing with the challenges of classification of high volume remote sensor data. A fast unsupervised band reduction method is proposed to lower the dimensionality of the input image. The band reduced image is then split into two mutually disjoint pure and mixed pixel subsets by a pixel segregator built using extended mathematical morphology techniques. A novel hierarchical spectral-spatial support vector machine based classifier that adaptively includes the usage of expensive spatial information based on the pixel categorization is proposed. The final thematic map is obtained after merging the classification results of the two subsets and fixed spatial neighborhood homogenization. The accuracy, efficiency and flexibility of the developed system are demonstrated by evaluating the classification results using several hyperspectral and multispectral data sets. The obtained results demonstrate that the proposed method performs significantly better than conventional classifiers while alleviating the computational complexity involved in generating spatial information.

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Section: Pure and Mixed Pixel Segregationmentioning

confidence: 99%

An Efficient Hybrid Classification System for High Resolution Remote Sensor Data

Tamma¹,

Rao²,

Jaisankar³

2013

AJRS

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“…Since HSV is identical to human visual system, many authors suggest fuzzy logic as an effective modeling tool. Modeling of such a color space in fuzzy domain has greater advantage in image processing and several authors have proposed to use fuzzy sets [2], [3]. Chamorro et.…”

Section: Introductionmentioning

confidence: 99%

A Linguistic Color Space for Image Enhancemenet

Chandrasekharan¹,

Sasikumar²

2015

2015 Fifth International Conference on Advances in Computing and Communications (ICACC)

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Color is an evitable feature of image and its prediction is still a critical issue in computer vision and image processing. It is necessary to ensure that the perceived color of an object remains constant under varying conditions. Novelty of this paper lies in introduction of a linguistic color space using Mamdani's fuzzy inference system for better color constancy and image enhancement. We define different types of membership functions with minimum number of inference rules to map RGB components to linguistic color space. Also, psychophysically measured colors are represented in terms of linguistic variables. While evaluating the algorithm, it is clear that this algorithm rivals current state of art performance without the help of training data. In addition, this method can be used for cloud detection of aerial images, thus opening further research in aerial image processing.

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“…In this work we are interested in fuzzy approaches [10,12,14,21,24,19,20], in which each group of colors is in fact a fuzzy subset of colors, called a fuzzy color, and the whole collection of colors defining a fuzzy partition of the color space is called a fuzzy color space. This approach has the advantage that it is able to represent the fact that indistinguishability is a fuzzy, gradual concept for we humans, i.e.…”

Section: Introductionmentioning

confidence: 99%

Histograms for Fuzzy Color Spaces

Chamorro-Martı́nez

Sánchez

Soto-Hidalgo

et al. 2011

Advances in Intelligent and Soft Computing

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In this paper we introduce two kinds of fuzzy histograms on the basis of fuzzy colors in a fuzzy color space and the notion of gradual number by Dubois and Prade. Fuzzy color spaces are a collection of fuzzy sets providing a suitable, conceptual quantization with soft boundaries of crisp color spaces. Gradual numbers assign numbers to values of a relevance scale, typically [0,1]. Contrary to convex fuzzy subsets of numbers (called fuzzy numbers, but corresponding to fuzzy intervals as an assignment of intervals to values of [0,1]), they provide a more precise representation of the cardinality of a fuzzy set. Histograms based on gradual numbers are particularly well-suited for serving as input to another process. On the contrary, they are not the best choice when showing the information to a human user. For this second case, linguistic labels represented by fuzzy numbers are a better alternative, so we define linguistic histograms as an assignment of linguistic labels to each fuzzy color. We provide a way to calculate linguistic histograms based on the compatibility between gradual numbers and linguistic labels. We illustrate our proposals with some examples.

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New fuzzy model for morphological colour image processing

Cited by 24 publications

References 10 publications

An Efficient Hybrid Classification System for High Resolution Remote Sensor Data

An Efficient Hybrid Classification System for High Resolution Remote Sensor Data

A Linguistic Color Space for Image Enhancemenet

Histograms for Fuzzy Color Spaces

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