Spectral Spatial Gradients (SSGs) have been suggested as features invariant to illumination variation for color-based recognition and indexing. While SSGs have a greater degree of invariance than Spectral Gradients (SGs), they may have a reduced discrimination power between objects since they use only spatial changes of object reflectance. The approach presented is to use the framework of SGs and SSGs to create a recognition method which is invariant to varying illumination without discarding the reflectance distribution information. Two techniques are used to extract the varying illumination. The first is to assume that low-frequency SG variation is due to illumination. The second method uses regions with matching SSGs to get local estimates of illumination. Once the local illumination variation has been extracted, its effect on the SGs can be removed and the resulting Adapted Spectral Gradients (ASGs) have a greater power of discrimination for some objects than SSG features. Experimental results demonstrate cases where ASGs show improved performance.

Most of the recent color recognition/indexing approaches concentrate on establishing invariance to illumination color to improve the utility of color recognition. However, other effects caused by illumination pose and specularity on three-dimensional object surfaces have not received notable attention. We present a chromaticity recognition method that discounts the effects of illumination pose, illumination color and specularity. It utilizes a chromaticity space based on log-ratio of sensor responses for illumination pose and color invariance. A model-based specularity detection/rejection algorithm can be used to improve the chromaticity recognition and illumination estimation for objects including specular reflections.