Machine Learning
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
Latent hough transform for object detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Propagative hough voting for human activity recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
An introduction to random forests for multi-class object detection
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Object class detection: A survey
ACM Computing Surveys (CSUR)
Hough-based tracking of non-rigid objects
Computer Vision and Image Understanding
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Hough transform based object detectors learn a mapping from the image domain to a Hough voting space. Within this space, object hypotheses are formed by local maxima. The votes contributing to a hypothesis are called support. In this work, we investigate the use of the support and its backprojection to the image domain for multi-view object detection. To this end, we create a shared codebook with training and matching complexities independent of the number of quantized views. We show that since backprojection encodes enough information about the viewpoint all views can be handled together. In our experiments, we demonstrate that superior accuracy and efficiency can be achieved in comparison to the popular one-vs-the-rest detectors by treating views jointly especially with few training examples and no view annotations. Furthermore, we go beyond the detection case and based on the support we introduce a part-based similarity measure between two arbitrary detections which naturally takes spatial relationships of parts into account and is insensitive to partial occlusions. We also show that backprojection can be used to efficiently measure the similarity of a detection to all training examples. Finally, we demonstrate how these metrics can be used to estimate continuous object parameters like human pose and object's viewpoint. In our experiment, we achieve state-of-the-art performance for view-classification on the PASCAL VOC'06 dataset.