Selecting features for object detection using an AdaBoost-compatible evaluation function
Pattern Recognition Letters
Object detection by global contour shape
Pattern Recognition
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
Learning to generate novel views of objects for class recognition
Computer Vision and Image Understanding
Shape-from-recognition: Recognition enables meta-data transfer
Computer Vision and Image Understanding
Spatio-temporal constraints for on-line 3D object recognition in videos
Computer Vision and Image Understanding
Representing images of a rotating object with cyclic permutation for view-based pose estimation
Computer Vision and Image Understanding
Multi-resolution recognition of 3D objects based on visual resolution limits
Pattern Recognition Letters
Semi---supervised Learning with Constraints for Multi---view Object Recognition
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Learning 3-D object orientation from images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Learning 3D object recognition from an unlabelled and unordered training set
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Toward automated generation of parametric BIMs based on hybrid video and laser scanning data
Advanced Engineering Informatics
Distributed object recognition via feature unmixing
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Backprojection revisited: scalable multi-view object detection and similarity metrics for detections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Manifold topological multi-resolution analysis method
Pattern Recognition
Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automatic occlusion removal from facades for 3D urban reconstruction
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Learning logic rules for scene interpretation based on markov logic networks
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
Object Detection using Geometrical Context Feedback
International Journal of Computer Vision
Viewpoint-aware object detection and continuous pose estimation
Image and Vision Computing
Latent hough transform for object detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
3D2PM - 3d deformable part models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Achievements and challenges in recognizing and reconstructing civil infrastructure
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
View-Invariant object detection by matching 3d contours
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Object class detection: A survey
ACM Computing Surveys (CSUR)
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We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al. After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors