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Neural Computation
Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
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Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Machine Learning
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Semantic segmentation of urban scenes using dense depth maps
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Supervised label transfer for semantic segmentation of street scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Hough transform and 3D SURF for robust three dimensional classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Toward coherent object detection and scene layout understanding
Image and Vision Computing
Supervised classification of multiple view images in object space for seismic damage assessment
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Semantic classification in aerial imagery by integrating appearance and height information
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Semantic parsing of street scenes from video
International Journal of Robotics Research
Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
International Journal of Computer Vision
International Journal of Computer Vision
Object Detection using Geometrical Context Feedback
International Journal of Computer Vision
Joint Optimization for Object Class Segmentation and Dense Stereo Reconstruction
International Journal of Computer Vision
Mobile robot 3D map building and path planning based on multi-sensor data fusion
International Journal of Computer Applications in Technology
Visual dictionary learning for joint object categorization and segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Beyond the line of sight: labeling the underlying surfaces
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Co-inference for multi-modal scene analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Local label descriptor for example based semantic image labeling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Road scene segmentation from a single image
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Semantic structure from motion: a novel framework for joint object recognition and 3d reconstruction
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Semantic road segmentation via multi-scale ensembles of learned features
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
International Journal of Computer Vision
Label transfer exploiting three-dimensional structure for semantic segmentation
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
Fusion of 3D-LIDAR and camera data for scene parsing
Journal of Visual Communication and Image Representation
Multi-view traffic sign detection, recognition, and 3D localisation
Machine Vision and Applications
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We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors.Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance.