Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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Auto parking techniques are attracting more attention these days. In this paper, we develop an image-based method to estimate the depth contour in parking areas. Our algorithm is an extension of the canonical appearance-based models for object recognition. One challenge in object recognition is that limited training dataset can hardly represent all kinds intra-class and inter-class variations. We propose to augment the limited training dataset by on-the-spot learning from test data. The information is obtained by applying a fast block based stereo algorithm to estimate a rough disparity map. New "soft" samples are created to augment the training sample library. We present improved classification performance by using the proposed technique.