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SE Mustererkennung WS 08/09 Horst Bischof 510.103 Seminar Mustererkennung 3SE Horst Bischof, Michael Donoser Thema: 3D Representation for Recognition Zusammenwirken von 3D Information und Erkennung Workshops auf ICCV09,07
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SE Mustererkennung WS 08/09 Horst Bischof Ablauf Wie eine Konferenz 1.Papers einreichen 2.Papers begutachten 3.Papers Vortragen 4.Final Papers + Proceedings
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SE Mustererkennung WS 08/09 Horst Bischof Ablauf 1.Verteilen der Konferenz Aufgaben Suchen der Papers (mindestens 2) 2. Besprechung der gewählten Papers Termin: Mi: 28.10. 14:15 3.Schriftlicher Beitrag: 18.11. Beginn Reviewing 4.Besprechung Reviews: 16.12. 5.Konferenz: 13. 1. (Proceedings fertig) Beurteilung: Vortrag/Schriftliches Dokument
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SE Mustererkennung WS 08/09 Horst Bischof Ablauf Aufgaben: General Chair Editor Proceedings Reviewer alle Session Chairs
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SE Mustererkennung WS 08/09 Horst Bischof Paper List 2.5D Elastic Graph Matching [1] –Graph Matching, Exploiting 3D Info for Face Recognition Scale and Orientation Invariant 3D Interest Point Extraction using HK Curvatures [2] –IP-detection, 3D, using local curvature, 3D recognition Intrinsic Shape Signatures: A shape Descriptor for 3D Object Recognition [3] –3D point clouds, novel descriptor, fast indexing by LST
![SE Mustererkennung WS 08/09 Horst Bischof Paper List 2.5D Elastic Graph Matching [1] –Graph Matching, Exploiting 3D Info for Face Recognition Scale and Orientation Invariant 3D Interest Point Extraction using HK Curvatures [2] –IP-detection, 3D, using local curvature, 3D recognition Intrinsic Shape Signatures: A shape Descriptor for 3D Object Recognition [3] –3D point clouds, novel descriptor, fast indexing by LST](http://images.slideplayer.org/37/10690051/slides/slide_5.jpg "SE Mustererkennung WS 08/09 Horst Bischof Paper List 2.5D Elastic Graph Matching [1] –Graph Matching, Exploiting 3D Info for Face Recognition Scale and Orientation Invariant 3D Interest Point Extraction using HK Curvatures [2] –IP-detection, 3D, using local curvature, 3D recognition Intrinsic Shape Signatures: A shape Descriptor for 3D Object Recognition [3] –3D point clouds, novel descriptor, fast indexing by LST")
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SE Mustererkennung WS 08/09 Horst Bischof Paper List Image Composition for Object Pop-out [4] –Detecting objects in scenes, image retrieval + homography estimation 3D Pose Estimation for Planes [5] –Plane tracking, using point + line features, pose estimation Semantic Classification by Covariance Descriptors within a randomized forest [6] –Covariance descriptors, euclidean representation, passed to random forest classifier, 3D Height
![SE Mustererkennung WS 08/09 Horst Bischof Paper List Image Composition for Object Pop-out [4] –Detecting objects in scenes, image retrieval + homography estimation 3D Pose Estimation for Planes [5] –Plane tracking, using point + line features, pose estimation Semantic Classification by Covariance Descriptors within a randomized forest [6] –Covariance descriptors, euclidean representation, passed to random forest classifier, 3D Height](http://images.slideplayer.org/37/10690051/slides/slide_6.jpg "SE Mustererkennung WS 08/09 Horst Bischof Paper List Image Composition for Object Pop-out [4] –Detecting objects in scenes, image retrieval + homography estimation 3D Pose Estimation for Planes [5] –Plane tracking, using point + line features, pose estimation Semantic Classification by Covariance Descriptors within a randomized forest [6] –Covariance descriptors, euclidean representation, passed to random forest classifier, 3D Height")
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SE Mustererkennung WS 08/09 Horst Bischof Paper List Road Scene Labeling Using SfM Module and 3D Bag of Textons [7] –Semantic Segmentation of street scenes, Textons, CRF Shading Cues for Object Class Detection [8] –kAS, Shading primitive cylinder, shading fit Active View Selection for Object and Pose recognition [9] –3D Object Recognition, HOG + SVM, select best view
![SE Mustererkennung WS 08/09 Horst Bischof Paper List Road Scene Labeling Using SfM Module and 3D Bag of Textons [7] –Semantic Segmentation of street scenes, Textons, CRF Shading Cues for Object Class Detection [8] –kAS, Shading primitive cylinder, shading fit Active View Selection for Object and Pose recognition [9] –3D Object Recognition, HOG + SVM, select best view](http://images.slideplayer.org/37/10690051/slides/slide_7.jpg "SE Mustererkennung WS 08/09 Horst Bischof Paper List Road Scene Labeling Using SfM Module and 3D Bag of Textons [7] –Semantic Segmentation of street scenes, Textons, CRF Shading Cues for Object Class Detection [8] –kAS, Shading primitive cylinder, shading fit Active View Selection for Object and Pose recognition [9] –3D Object Recognition, HOG + SVM, select best view")
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SE Mustererkennung WS 08/09 Horst Bischof Paper List Learning 3D Scene Structure from a single still image [10] –Probabilistic framework, superpixel + constraint for fly-through 3D Object Recognition from range images using pyramid matching [11] –Range images, feature based classification, salient points+3D Desk, pyramid match kernel Depth from Recognition: Inferring meta-data by cognitive feedback [12] –Recognition for 3D Info, ISM for detection, meta-data from training images
![SE Mustererkennung WS 08/09 Horst Bischof Paper List Learning 3D Scene Structure from a single still image [10] –Probabilistic framework, superpixel + constraint for fly-through 3D Object Recognition from range images using pyramid matching [11] –Range images, feature based classification, salient points+3D Desk, pyramid match kernel Depth from Recognition: Inferring meta-data by cognitive feedback [12] –Recognition for 3D Info, ISM for detection, meta-data from training images](http://images.slideplayer.org/37/10690051/slides/slide_8.jpg "SE Mustererkennung WS 08/09 Horst Bischof Paper List Learning 3D Scene Structure from a single still image [10] –Probabilistic framework, superpixel + constraint for fly-through 3D Object Recognition from range images using pyramid matching [11] –Range images, feature based classification, salient points+3D Desk, pyramid match kernel Depth from Recognition: Inferring meta-data by cognitive feedback [12] –Recognition for 3D Info, ISM for detection, meta-data from training images")
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SE Mustererkennung WS 08/09 Horst Bischof Paper List Perspectively Invariant Normal features [13] –Feature Point Descriptor, MSER + depth map Articulated Shape Matching by Robust Alignment of Embedded Representations [14] –Point correspondences, Spectral embedding
![SE Mustererkennung WS 08/09 Horst Bischof Paper List Perspectively Invariant Normal features [13] –Feature Point Descriptor, MSER + depth map Articulated Shape Matching by Robust Alignment of Embedded Representations [14] –Point correspondences, Spectral embedding](http://images.slideplayer.org/37/10690051/slides/slide_9.jpg "SE Mustererkennung WS 08/09 Horst Bischof Paper List Perspectively Invariant Normal features [13] –Feature Point Descriptor, MSER + depth map Articulated Shape Matching by Robust Alignment of Embedded Representations [14] –Point correspondences, Spectral embedding")
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