Chapter 12 Object Recognition

Nov 10, 2012

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Chapter 12 Object Recognition. Chapter 12 Object Recognition. Chapter 12 Object Recognition. Chapter 12 Object Recognition. Chapter 12 Object Recognition. Chapter 12 Object Recognition.

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Chapter 12 Object Recognition

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Chapter 12 Object Recognition
Chapter 12 Object Recognition
Chapter 12 Object Recognition

Chapter 12 Object Recognition
Chapter 12 Object Recognition
Chapter 12 Object Recognition

OBJECT RECOGNITION

OBJECT RECOGNITION. Presented by Vishal Dalmiya Department of Electrical Engineering University of Texas at Arlington. Milestones. Introduction System components Complexity of object recognition Object Representation Object-centered representation Observer-centered representation

521 views • 32 slides

Object recognition

Object recognition. Methods for classification and image representation. Credits. Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

837 views • 42 slides

Dense Object Recognition

Dense Object Recognition. 2. Template Matching. Face Detection. We will investigate face detection using a scanning window technique:. Think that this task sounds easy?. Training Data. Non-Faces. Faces. 800 random non-face regions 60x60, taken from same data as faces.

1.22k views • 100 slides

Statistical Object Recognition

CS 636 Computer Vision. Statistical Object Recognition. Nathan Jacobs. Slides adapted from Lazebnik. Administrivia. Project 4 Final Project Next Class is Cancelled. Overview. Statistical Recognition generative vs. discriminative learning Bag of Features Models. Statistical Recognition.

832 views • 60 slides

Object Recognition

Object Recognition. Scenario? Landmark Detection (objects and humans) Cluttered Environment Levels of Occlusion Types Color Shape Texture Dynamic confusers 3-D objects and/or 2-D projections. Object Recognition. Tie to SES and Qualitative Spatial Reasoning

496 views • 15 slides

Object Recognition

Object Recognition. By A.Sravya. Object recognition problem. Given some knowledge of how certain objects may appear and an image of a scene possibly containing those objects, report which objects are present in the scene and where. Applications. Image panoramas Image watermarking

964 views • 42 slides

Visual Object Recognition

Visual Object Recognition. Rob Fergus Courant Institute, New York University. http://cs.nyu.edu/~fergus/icml_tutorial/. Agenda. Introduction Bag-of-words models Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition

589 views • 38 slides

Object Recognition

Object Recognition. T. Geometric Task :. Given two configurations of points in the three dimensional space,. find those rotations and translations of one of the point sets which produce “large” superimpositions of corresponding 3-D points.

419 views • 25 slides

Visual Object Recognition

Visual Object Recognition. Bastian Leibe & Computer Vision Laboratory ETH Zurich Chicago, 14.07.2008. Kristen Grauman Department of Computer Sciences University of Texas in Austin. Detection via classification: Main idea. Consider all subwindows in an image

528 views • 33 slides

Visual Object Recognition

Visual Object Recognition. Bastian Leibe & Computer Vision Laboratory ETH Zurich Chicago, 14.07.2008. Kristen Grauman Department of Computer Sciences University of Texas in Austin. Outline. Detection with Global Appearance & Sliding Windows

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Object recognition

Object recognition. Object Classes. Individual Recognition. Is this a dog?. Variability of Airplanes Detected. Variability of Horses Detected. Class Non-class . Class Non-class. Recognition with 3-D primitives. Geons. Visual Class: Common Building Blocks.

679 views • 51 slides

Chapter 3 _ Object Recognition

Chapter 3 _ Object Recognition. + 이선정. Perceptual Organization. Gestaltist appoach Law of Pragnanz “of several geometrically possible organisation that one will actually occur which possess the best, simplest and most stable shape “ (Koffka, 1935). a. b. c. d. e.

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Object Recognition

Object Recognition. So what does object recognition involve?. Verification: is that a bus?. Detection: are there cars?. Identification: is that a picture of Mao?. Object categorization. sky. building. flag. face. banner. wall. street lamp. bus. bus. cars.

1.45k views • 48 slides

Object Recognition

Object Recognition. Article: Distinctive Image Features from Scale -Invariant Keypoints. C. Pantofaru and M. Hebert. CMU Technical Report, September 2005. Xiang Li

286 views • 11 slides

Object Recognition

Object Recognition. Outline: Introduction Representation: Concept Representation: Features Learning & Recognition Segmentation & Recognition. Credits: major sources of material, including figures and slides were:

734 views • 38 slides

Multiclass object recognition

Multiclass object recognition. Ankit Agrawal Jyoti Meena. Objectives. Categorization Identification. I ntroduction. We applied a biologically inspired model of visual object recognition to the multiclass object categorization problem . Our model based on Serre , Wolf, and Poggio .

244 views • 13 slides

Object recognition

Object recognition. Methods for classification and image representation. Credits. Slides by Pete Barnum Slides by Fei-Fei Li Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04 Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

607 views • 42 slides

Object Recognition

Object Recognition. Tom McGrath CIS 601. What is object recognition?. Perception of objects is different for humans than for computers. For humans: perception of familiar items. For computers: perception of familiar patterns. Are they really the same thing?. What do we mean by ‘objects’.

741 views • 60 slides

Object recognition

Object recognition. What is object recognition? Why is it needed? 1.To move around safely avoiding objects 2.To pick and place objects 3.Inspect objects Two major issues: 1.representation of models 2.matching of models to images. Invariants.

262 views • 11 slides

Object Recognition

Object Recognition. Instance Recognition Known, rigid object Only variation is from relative position & orientation (and camera parameters) “Cluttered image” = possibility of occlusion, irrelevant features Generic (category-level) Recognition Any object in a class (e.g. chair, cat)

471 views • 37 slides

Chapter 12 Object Recognition

Chapter 12 Object Recognition. 12.1 Patterns and pattern classes Definition of a pattern class:a family of patterns that share some common properties Feature and descriptor Pattern arrangements used in practice are: vectors, strings and trees Pattern vectors:

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