We present Hough Networks (HNs), a novel method that combines the idea of Hough Forests (HFs) [12] with Convolutional Neural Networks (CNNs) [18] . Similar to HFs we perform a simultaneous classification and regression on densely extracted image patches. But instead of a Random Forest (RF) we utilize a CNN which is able to learn higherorder feature representations and does not rely on any handcrafted features. Applying a CNN on a patch level has the advantage of reasoning about more image details and additionally allows to segment the image into foreground and background. Furthermore, the structure of a CNN supports efficient inference of patches extracted from a regular grid. We evaluate HNs on two computer vision tasks: head pose estimation and facial feature localization. Our method achieves at least state-of-the-art performance without sacrificing versatility which allows extension to many other applications.
Riegler et al. REF proposed a Hough Networks (HNs) which combines the Hough Forests with CNN by performing classification and regression simultaneously.
Hough Networks for Head Pose Estimation and Facial Feature Localization.
BMVC
Computer Science