Colour-based texture segmentation

Barilla, M.E.; Forero, Manuel G.; Spann, Michael

doi:10.1063/1.2361244

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…On the other hand, methods based on multiscale decompositions such as the complex wavelets failed to give an acceptable DM, as revealed by Figs. 6(h) and 7(h), which show the results obtained with such a technique [33,34].…”

Section: Resultsmentioning

confidence: 86%

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Discrimination of isotrigon textures using the Rényi entropy of Allan variances

Gabarda

Cristóbal

2008

J. Opt. Soc. Am. A

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We present a computational algorithm for isotrigon texture discrimination. The aim of this method consists in discriminating isotrigon textures against a binary random background. The extension of the method to the problem of multitexture discrimination is considered as well. The method relies on the fact that the information content of time or space-frequency representations of signals, including images, can be readily analyzed by means of generalized entropy measures. In such a scenario, the Rényi entropy appears as an effective tool, given that Rényi measures can be used to provide information about a local neighborhood within an image. Localization is essential for comparing images on a pixel-by-pixel basis. Discrimination is performed through a local Rényi entropy measurement applied on a spatially oriented 1-D pseudo-Wigner distribution (PWD) of the test image. The PWD is normalized so that it may be interpreted as a probability distribution. Prior to the calculation of the texture's PWD, a preprocessing filtering step replaces the original texture with its localized spatially oriented Allan variances. The anisotropic structure of the textures, as revealed by the Allan variances, turns out to be crucial later to attain a high discrimination by the extraction of Rényi entropy measures. The method has been empirically evaluated with a family of isotrigon textures embedded in a binary random background. The extension to the case of multiple isotrigon mosaics has also been considered. Discrimination results are compared with other existing methods.

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Section: Resultsmentioning

confidence: 86%

“…G. DM after previous single results. H. DM by a complex wavelets method [33,34]. Figure 7(h) courtesy of M. G. Forero.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of isotrigon textures using the Rényi entropy of Allan variances

Gabarda

Cristóbal

2008

J. Opt. Soc. Am. A

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“…Unlike Santosh's work, we propose the use of CIE L*u*v* color space as it is perceptually uniform and suitable for representing human perception because it accurately represents chrominance (components u* and v*) and lighting information (component L*). Additionally, there are illumination standards for different environments depending on the lighting conditions [11], [15]. In our work, texture is modeled based on channel I of the HSI (Hue, Saturation, Intensity) color space.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, color and texture cues are used together as they have proved to achieve better results than when treated in isolation [11]. Moreover, texture is a robust descriptor when is used in different scenarios of indoor environments, particularly when abrupt illumination changes occur.…”

Section: Introductionmentioning

confidence: 98%

Detecting obstacle-free regions for visual robot navigation by inferring scene horizons

Jimenez-Hernandez

Campos

Montes-Venegas

et al. 2010

2010 IEEE International Workshop on Robotic and Sensors Environments

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We present preliminary results of an algorithm for detecting obstacle-free regions in indoor environments using both color and texture information for visual robot navigation. By modeling color information in the L*u*v* color space, a color-based segmentation is performed to find similar regions. This segmentation yields a set of regions that are joined together into single areas using texture information. Texture-based similarity measures between segmented regions are computed using ANOVA (Analysis of Variance), which produces the final obstacle-free regions. These regions are then used as visual information to drive a robot towards the scene horizon, i.e. the boundary between visible free space and unexplored areas. The performance of our algorithm was tested on several sequences of images taken from office corridors.

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“…Visual shading difference is utilized to channel data present in every shading segment. (Tremeau et al [2]) and to recognize among comparative dim scale intensities (Barilla-Perez & Spann [2]). There have been both shading based and composition based vehicle divisions.…”

Section: Color and Texture Based Vehicle Detectionmentioning

confidence: 99%

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

Gaurav

Kumar

Venkatesan

et al. 2016

MATEC Web of Conferences

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Abstract. Vehicle recognition is the chief stride in observing the speeding vehicles in a thruway. The feature arrangements caught by a stationary camera demonstrate to us that there's a requirement for a vehicle location calculation which handles sudden light change furthermore the situations where the closer view converges away from plain sight. This paper gives us a study of different foundation subtraction systems that are utilized for recognizing the vehicles effectively. Vehicles proceeding onward street are of significance on the grounds that issues like movement blockage, monetary waste, sticking on the underpasses and over-extensions (if the vehicle going through is not of the passable size) are connected with them.Index Terms-Vehicle Detection, video sequences, foreground, background, MATLAB, RGB conversion.

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Colour-based texture segmentation

Cited by 3 publications

References 14 publications

Discrimination of isotrigon textures using the Rényi entropy of Allan variances

Discrimination of isotrigon textures using the Rényi entropy of Allan variances

Detecting obstacle-free regions for visual robot navigation by inferring scene horizons

A Narrative Approach to Detect the Vehicles using color, texture and edge based techniques

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