Advanced Computer Vision

Advanced Computer Vision Lecture 03 Roger S. Gaborski Roger S. Gaborski

Video Lecture • http://videolectures.net/nips09_torralba_uvs • Paper: VLFeat-An open and portable library of computer vision algorithms • http://vision.ucla.edu/papers/vedaldiF10.pdf Roger S. Gaborski

Object Recognition • Issues: • Viewpoint • Scale • Deformable vs. rigid • Clutter • Occlusion • Intra class variability Roger S. Gaborski

Goal • Locate all instances of automobiles in a cluttered scene Roger S. Gaborski

Acknowledgements • Students (Thesis in RIT Library): • Tim Lebo • Dan Clark • Images used in presentation: • ETHZ Database, UIUC Database Roger S. Gaborski

Object Recognition Approaches • For specific object class: • Holistic • Model whole object • Parts based • Simple parts • Geometric relationship information • We could use a similar approach to match patches representing different image categories (‘sand’ patches located on lower half of beach scenes) Roger S. Gaborski

Training Images and Segmentation Roger S. Gaborski

Implicit Shape Model • Patches – local appearance prototypes • Spatial relationship – where the patch can be found on the object • For a given class w: ISM(w) = (Iw ,Pw ) where Iw is the codebook containing the patches and Pw is the probability distribution that describes where the patch is found on the object • How do we find ‘interesting’ patches? Roger S. Gaborski

Harris Point Operator Roger S. Gaborski

Harris Points Roger S. Gaborski

Segmented Training Mask Segmented mask ensures only patches containing valid car regions are selected A corresponding segmentation patch is also extracted Roger S. Gaborski

Selected Patches Roger S. Gaborski

How is spatial information represented? • Estimate the center of the object using the centroid of the segmentation mask • Displacement between: • Center of patch • Centroid of segmentation mask Roger S. Gaborski

Individual Patch and Displacement Information Roger S. Gaborski

Typical Training Example Roger S. Gaborski

Extracted Training Patches Roger S. Gaborski

Cluster Patches • Many patches will be visually similar • Normalized Grayscale Correlation is used to cluster patches • All patches within a certain neighborhood defined by the NGC are grouped together • The representative patch is determined by mean of the patches • The geometric information for each patch in the cluster is assigned to the representative patch Roger S. Gaborski

Patches Roger S. Gaborski

Wheel Patch Example Roger S. Gaborski

Clusters Opportunity for better clustering method Roger S. Gaborski

Clusters Roger S. Gaborski

Roger S. Gaborski

Object Detection • Harris point operator to find interesting points • Extract patches • Match extracted patches with model patches • Spatial information predicts center of object • Create voting space Roger S. Gaborski

Ideal Voting Space Example Roger S. Gaborski

Multiple Votes Multiple geometric interpretations Roger S. Gaborski

Resolving False Detections Roger S. Gaborski

Localization: Find Corners Roger S. Gaborski

Localization: Model Matching Roger S. Gaborski

Localization: Find Corners Roger S. Gaborski

Model Matching Roger S. Gaborski

Spatial Activation(Hough Space) 9000 different locations Roger S. Gaborski

Hypothesis Candidates 16 candidate locations Roger S. Gaborski

Hypothesis Candidates Roger S. Gaborski

References SEE RESOURCES ON COURSE WEB PAGE: Timothy Lebo and Roger Gaborski, “A Shape model with Coactivation Networks for Recognition and Segmentation,” Eighth International conference on Signal and image Processing, Honolulu, HI. August 2006. Timothy Lebo, “Guiding Object Recognition: A Shape Model with Co-activation Networks,” MS Thesis, RIT, 2005. Daniel Clark, “Object Detection and Tracking using a Parts Based Approach,” MS Thesis, RIT, 2005. Roger S. Gaborski

References Bastian Leibe, Ales Leonardis, and Bernt Schiele, “Combined object categorization and segmentation with an implicit shape model,” ECCV’04 Workshop onStatistical Learning in Computer Vision, May 2004. Shivani Agarwal, Aatif Awan, and Dan Roth, “Learning to detect objects in images via a sparse, part-based representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475–1490, 2004. Roger S. Gaborski

Voting Space Roger S. Gaborski

Model Patches Selected Roger S. Gaborski

True Object Patches Roger S. Gaborski

Identified Objects Roger S. Gaborski

Research Topics • Scene Categories • MATLAB TOOLKIT: http://www.vlfeat.org/ Roger S. Gaborski

Reference Examples Recognizing Indoor Scenes AriadnaQuattoni and Antonio Torralba (http://people.csail.mit.edu/torralba/publications/indoor.pdf) Building the gist of a scene: the role of global image features in recognition AudeOliva and Antonio Torralba Objects as Attributes for Scene Classi¯cation Li-Jia Li, Hao Su, Yongwhan Lim, Li Fei-Fei (http://vision.stanford.edu/documents/LiSuLimFeiFei_ECCV2010.pdf) See references for each paper Roger S. Gaborski

Modeling the shape of the scene: a holistic representation of the spatial envelope http://people.csail.mit.edu/torralba/code/spatialenvelope/ • SIFT flow: dense correspondence across difference scenes http://people.csail.mit.edu/celiu/ECCV2008/ Roger S. Gaborski

Learning and Recognizing Visual Object Categories • http://www.youtube.com/watch?v=w2C-WffS-AE&feature=bf_prev&list=PL9415E136FBEE3016&lf=results_main Deep Learning: Visual Perception with Deep Learning http://www.youtube.com/watch?v=3boKlkPBckA Roger S. Gaborski

Typical Images http://labelme.csail.mit.edu/Images/spatial_envelope_256x256_static_8outdoorcategories/ coast_cdmc976.jpg coast_n708004.jpg coast_n384026.jpg coast_n739047.jpg forest_for142.jpg forest_for42.jpg forest_for38.jpg forest_for58.jpg tallbuilding_a487092.jpg tallbuilding_a803053.jpg tallbuilding_art1350.jpg tallbuilding_art1506.jpg Roger S. Gaborski

Coast Images Roger S. Gaborski

Forest Roger S. Gaborski

Tall Buildings Roger S. Gaborski

Advanced Computer Vision

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