Color Space Selection for Moving Shadow Elimination

Zhao, Shuyun; Zhou, You; Lan, Shiyong; Zhou, Xin

doi:10.1109/icig.2007.54

Cited by 25 publications

(15 citation statements)

References 10 publications

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“…Among the chromacity methods, the most important factor is to choose a colour space with a separation of intensity and chromacity. Several colour spaces such as HSV [7], c1c2c3 [36] and normalised RGB [4] have proved to be robust for shadow detection [38]. We chose the HSV approach proposed by Cucchiara et al [7], since that colour space provides a natural separation between chromacity and luminosity.…”

Section: Chromacity-based Methodsmentioning

confidence: 99%

Shadow detection: A survey and comparative evaluation of recent methods

Sanin

Sanderson

Lovell

2012

Pattern Recognition

297

232

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Section: Chromacity-based Methodsmentioning

confidence: 99%

Shadow detection: A survey and comparative evaluation of recent methods

Sanin

Sanderson

Lovell

2012

Pattern Recognition

297

232

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“…In the context of image processing, another line of work explores the effect of data representations or color spaces on image analysis tasks, also within the domain of remote sensing [44][45][46][47]. This constitutes the selection or creation of low-level image processing where the data is transformed to another representation or color space that is more suited to the given problem.…”

Section: Integrative Approaches To Image Analysismentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Abstract:Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid-and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.

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“…According to Shan et al, 39 the HSV color space is sensitive to illumination and view direction, and is best suited for situations with strong shadows. According to Shan et al, 39 the HSV color space is sensitive to illumination and view direction, and is best suited for situations with strong shadows.…”

Section: Chromaticity-based Methodsmentioning

confidence: 99%

Shadow detection in camera-based vehicle detection: survey and analysis

Barcellos

Gomes

Scharcanski

2016

J. Electron. Imaging

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Shadow detection in camera-based vehicle detection: survey and analysis,"Abstract. The number of vehicles in circulation in modern urban centers has greatly increased, which motivates the development of automatic traffic monitoring systems. Consequently, camera-based traffic monitoring systems are becoming more widely used, since they offer important technological advantages in comparison with traditional traffic monitoring systems (e.g., simpler maintenance and more flexibility for the design of practical configurations). The segmentation of the foreground (i.e., vehicles) is a fundamental step in the workflow of a camera-based traffic monitoring system. However, foreground segmentation can be negatively affected by vehicle shadows. This paper discusses the types of shadow detection methods available in the literature, their advantages, disadvantages, and in which situations these methods can improve camera-based vehicle detection for traffic monitoring. In order to compare the performance of these different types of shadow detection methods, experiments are conducted with typical methods of each category using publicly available datasets. This work shows that shadow detection definitely can improve the reliability of traffic monitoring systems, but the choice of the type of shadow method depends on the system specifications (e.g., tolerated error), the availability of computational resources, and prior information about the scene and its illumination in regular operation conditions. Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Color Space Selection for Moving Shadow Elimination

Cited by 25 publications

References 10 publications

Shadow detection: A survey and comparative evaluation of recent methods

Shadow detection: A survey and comparative evaluation of recent methods

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Shadow detection in camera-based vehicle detection: survey and analysis

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