Cast shadow segmentation using invariant color features

Abstract

Shadows are integral parts of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In many image analysis and interpretation applications, shadows interfere with fundamental tasks such as object extraction and description. For this reason, shadow segmentation is an important step in image analysis. In this paper, we propose a new cast shadow segmentation algorithm for both still and moving images. The proposed technique exploits spectral and geometrical properties of shadows in a scene to perform this task. The presence of a shadow is first hypothesized with an initial and simple evidence based on the fact that shadows darken the surface which they are cast upon. The validity of detected regions as shadows is further verified by making use of more complex hypotheses on color invariance and geometric properties of shadows. Finally, an information integration stage confirms or rejects the initial hypothesis for every detected region. Simulation results show that the proposed algorithm is robust and efficient in detecting shadows for a large class of scenes.

Robust background removal in 4D Studio images

“…Most shadow detection approaches are based on the assumption that pixels in the shadow region are darker but have the same chromaticity. Various colour representations have been exploited for shadow detection (HSV [8], the normalised RGB [9], c1c2c3 [10], YUV [11]). Petit et al [5] use for shadow detection in their real-time 3D reconstruction studio the method based on the approach of Horprasert et al [12].…”

Building Illumination Coherent 3D Models of Large-Scale Outdoor Scenes

“…For this reason, an image-based method for shadow detection is necessary. We employ a user-assisted method, but other shadow segmentation methods (FunkaLea and Bajcsy 1995;Salvador et al 2004) could be used instead. The user selects regions of the image and a pair of luminance thresholds s 0 and s 1 .…”

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

“…The chromaticity-based methods are based on the assumption that shadow pixels become darker as they are blocked from the illumination source but their chromaticity remains the same as the corresponding background pixels, which referred to as color constancy [3] or linear attenuation [4]. These properties have been employed in different colors spaces like HSV [5], YUV [6], normalized RGB [7] and c1c2c3 [8]. Most of these methods are simple to perform and their processing time is the fastest comparatively.…”