Learning Hough forest with depth-encoded context for object detection
In this paper, we propose a novel extension to the Class-specific Hough Forest (CHF) framework for object detection and localization. Our approach utilizes depth information during training to build a more discriminative codebook which simultaneously encodes features from the object and the surrounding context. In particular, we augment the CHF with contextual image patches, and design a series of depth-aware uncertainty measures for the binary tests used in CHF training. The new splitting...[Show more]
|Collections||ANU Research Publications|
|Source:||2012 International Conference on Digital Image Computing Techniques and Applications, DICTA 2012|
|01_Wang_Learning_Hough_forest_with_2012.pdf||810.5 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.