Object detection and segmentation are two challenging tasks in computer vision, which are usually considered as independent steps. In this paper, we propose a framework which jointly optimizes for both tasks and implicitly provides detection hypotheses and corresponding segmentations. Our novel approach is attachable to any of the available generalized Hough voting methods. We introduce Hough Regions by formulating the problem of Hough space analysis as Bayesian labeling of a random field. This exploits provided classifier responses, object center votes and low-level cues like color consistency, which are combined into a global energy term. We further propose a greedy approach to solve this energy minimization problem providing a pixel-wise assignment to background or to a specific category instance. This way we bypass the parameter sensitive non-maximum suppression that is required in related methods. The experimental evaluation demonstrates that state-of-the-art detection and segmentation results are achieved and that our method is inherently able to handle overlapping instances and an increased range of articulations, aspect ratios and scales.