Abstract

In Computer Vision, the sky color is used for lighting correction, image color enhancement, horizon alignment, image indexing, and outdoor image classification and in many other applications. In this article, for robust color based sky segmentation and detection, usage of lighting correction for sky color detection is investigated. As such, the impact of color constancy on sky color detection algorithms is evaluated and investigated. The color correction (constancy) algorithms used includes Gray-Edge (GE), Gray-World (GW), Max-RGB (MRGB) and Shades-of-Gray (SG). The algorithms GE, GW, MRGB, and SG, are tested on the static filtered sky modeling. The static filter is developed in the LAB color space. This evaluation and analysis is essential for detection scenarios, especially, color based object detection in outdoor scenes. From the results, it is concluded that the color constancy before sky color detection using LAB static filters has the potential of improving sky color detection performance. However, the application of the color constancy can impart adverse effects on the detection results. For images, the color constancy algorithms depict a compact and stable representative of the sky chroma loci, however, the sky color locus might have a shifting and deviation in a particular color representation. Since the sky static filters are using the static chromatic values, different results can be obtained by applying color constancy algorithms on various datasets.

Highlights

  • In Computer Vision, the color based horizon/sky detection is used for lighting enhancement, image color enhancement, horizon alignment, image indexing, outdoor image classification and in countless many other applications [1]

  • It is concluded that the color constancy before sky color detection using LAB static filters has the potential of improving sky color detection performance

  • We show the effect of color constancy algorithms on LAB static sky filter using Gray Edge (GE), Gray World (GW), Max RGB (MRGB) and Shades of Gray (SG)

Read more

Summary

INTRODUCTION

In Computer Vision, the color based horizon/sky detection is used for lighting enhancement, image color enhancement, horizon alignment, image indexing, outdoor image classification and in countless many other applications [1]. The algorithm based on color pixel alone can be executed extremely fast compare to the region based and complex features extraction based detection techniques This helps in the creation of a real-time sky color based detection and image enhancement algorithm. Schmitt et al in [5] use color, shape and position vector as an input feature for the position of the sky detection in an image The performance their method is measured and tested on a number of out-door images and in different weather and lighting conditions. The authors in [6] develop sky color based solar exposure system using the image processing techniques, segmenting the outdoor images recorded under different lighting conditions. The static filter is developed in the LAB color space This evaluation and analysis are essential for detection scenarios, especially, color based object detection in outdoor scenes. Since the sky static filters are using the fixed color space values, different results are obtained by applying color constancy algorithms on different datasets

COLOR CONSTANCY
METHODOLOGY
EXPERIMENTAL EVALUATION
Evaluation
CONCLUSION
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.