Finding objects in a 3D environment by combining distance measurement and color indexing

A new method is presented for the localization and recognition of three-dimensional objects using color information. In the first processing step, we estimate depth information by either applying a chromatic block matching method to color stereo images or acquiring a range image from a laser scanner. Second, the computed depth maps are segmented to distinguish between the image background and the objects that should be recognized. Assuming that the segmented regions represent single objects in the three-dimensional scene, feature vectors are generated based on color histograms. The Euclidean distance is used as well as the scalar product to measure the similarity between the feature vectors computed from the color image and the feature vectors stored in a database.