Active Color Image Analysis for Recognizing Shadows

Many existing computer vision modules assume that shadows in an image have been accounted for prior to their application. In spite of this, relatively little work has been done on recognizing shadows or on recognizing a single surface material when directly lit and in shadow. This is in part because shadows cannot be infallible recognized until a scene\u27s lighting and geometry are known. However, color is a strong cue to the presence of shadows. We present a general color image segmentation algorithm whose output is amenable to the recovery of shadows as determined by an analysis of the physics of shadow radiance. Then, we show how an observer that can cast its own shadows can infer enough information about a scene\u27s illumination to refine the segmentation results to determine where the shadows in the scene are with reasonable confidence. Having an observer that can actively cast shadows frees us from restrictive assumptions about the scene illumination or the reliance on high level scene knowledge. We present results of our methods on images of complex indoor and outdoor scenes

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory

Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation

We present a computational model and algorithm for detecting diffuse and specular interface reflections and some inter-reflections. Our color reflection model is based on the dichromatic model for dielectric materials and on a color space, called S space, formed with three orthogonal basis functions. We transform color pixels measured in RGB into the S space and analyze color variations on objects in terms of brightness, hue and saturation which are defined in the S space. When transforming the original RGB data into the S space, we discount the scene illumination color that is estimated using a white reference plate as an active probe. As a result, the color image appears as if the scene illumination is white. Under the whitened illumination, the interface reflection clusters in the S space are all aligned with the brightness direction. The brightness, hue and saturation values exhibit a more direct correspondence to body colors and to diffuse and specular interface reflections, shading, shadows and inter-reflections than the RGB coordinates. We exploit these relationships to segment the color image, and to separate specular and diffuse interface reflections and some inter-reflections from body reflections. The proposed algorithm is effications for uniformly colored dielectric surfaces under singly colored scene illumination. Experimental results conform to our model and algorithm within the liminations discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41303/1/11263_2004_Article_BF00128233.pd