Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Discriminative Models for Multi-Class Object Layout
International Journal of Computer Vision
Layered Object Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Detection of Multiple Object Instances Using Hough Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting partially occluded objects with an implicit shape model random field
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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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.