Discriminative Generalized Hough Transform Research Articles

Analysis of the discriminative generalized Hough transform as a proposal generator for a deep network in automatic pedestrian and car detection

Many approaches have been suggested for automatic pedestrian and car detection to cope with the large variability regarding size, occlusion, background variability, and aspect. Current deep learning-based frameworks rely either on a proposal generation mechanism (e.g., “faster R-CNN”) or on inspection of image quadrants/octants (e.g., “YOLO”), which are then further processed with deep convolutional neural networks (CNN). We analyze the discriminative generalized Hough transform (DGHT), which operates on edge images, for pedestrian and car detection. The analysis motivates one to use the DGHT as an efficient proposal generation mechanism, followed by a proposal (bounding box) refinement and proposal acceptance or rejection based on a deep CNN. We analyze in detail the different components of our pipeline. Due to the low false negative rate and the low number of candidates of the DGHT as well as the high accuracy of the CNN, we obtain competitive performance to the state of the art in pedestrian and car detection on the IAIR database with much less generated proposals than other proposal-generating algorithms, being outperformed only by YOLOv2 fine-tuned to IAIR cars. By evaluations on further databases (without retraining or adaptation), we show the generalization capability of our pipeline.

Discriminative generalized Hough transform for object localization in medical images

This paper proposes the discriminative generalized Hough transform (DGHT) as an efficient and reliable means for object localization in medical images. It is meant to give a deeper insight into the underlying theory and a comprehensive overview of the methodology and the scope of applications. The DGHT combines the generalized Hough transform (GHT) with a discriminative training technique for the GHT models to obtain more efficient and robust localization results. To this end, the model points are equipped with individual weights, which are trained discriminatively with respect to a minimal localization error. Through this weighting, the models become more robust since the training focuses on common features of the target object over a set of training images. Unlike other weighting strategies, our training algorithm focuses on the error rate and allows for negative weights, which can be employed to encode rivaling structures into the model. The basic algorithm is presented here in conjunction with several extensions for fully automatic and faster processing. These include: (1) the automatic generation of models from training images and their iterative refinement, (2) the training of joint models for similar objects, and (3) a multi-level approach. The algorithm is tested successfully for the knee in long-leg radiographs (97.6 % success rate), the vertebrae in C-arm CT (95.5 % success rate), and the femoral head in whole-body MR (100 % success rate). In addition, it is compared to Hough forests (Gall et al. in IEEE Trans Pattern Anal Mach Intell 33(11):2188-2202, 2011) for the task of knee localization (97.8 % success rate). Conclusion The DGHT has proven to be a general procedure, which can be easily applied to various tasks with high success rates.