'Implicit shape model' presentation slideshows

Part 2: part-based models

Part 2: part-based models. by Rob Fergus (MIT). Problem with bag-of-words. All have equal probability for bag-of-words methods Location information is important. Overview of section. Representation Computational complexity Design choices Recognition Learning Automated methods.

888 views • 73 slides

Image alignment

Image alignment. Image alignment: Motivation. Panorama stitching. Recognition of object instances. Image alignment: Challenges. Small degree of overlap. Occlusion, clutter. Image alignment. Two broad approaches: Direct (pixel-based) alignment Search for alignment where most pixels agree

1.2k views • 58 slides

Fitting: The Hough transform

Fitting: The Hough transform. Review: Corner and blob detection. What are some characteristics of good features? What is the distinguishing characteristic of corners? What are the invariance properties of Harris corners? What is a scale space? What is spatial selection?

644 views • 50 slides

Model Fitting

Model Fitting. Hao Jiang Computer Science Department Oct 8, 2009. Hough transform for circles. For a fixed radius r, unknown gradient direction. Circle: center (a,b) and radius r. Hough space. Image space. Source: K. Grauman. Hough transform for circles.

569 views • 40 slides

Fitting: The Hough transform

870 views • 39 slides

Pedestrian Detection in Crowded Scenes

Pedestrian Detection in Crowded Scenes. Dhruv Batra ECE CMU. Pedestrian Detection in Crowded Scenes.

700 views • 34 slides

Tracking the Invisible: Learning Where the Object Might be

Tracking the Invisible: Learning Where the Object Might be. Helmut Grabner1, Jiri Matas2, Luc Van Gool1,3, Philippe Cattin4 1ETH-Zurich, 2Czech Technical University, Prague, 3K.U . Leuven, 4University of Basel. Outline. 1. Introduction 2. Learning the Motion Couplings

448 views • 34 slides

Object Recognition

Object Recognition. Jeremy Wyatt. Plan. David Marr: the model based approach to vision Model based approaches: Geons, Model Fitting Appearance based approaches: PCA, SIFT, implicit shape model Psychological Evidence: View dependent vs. view independent recognition Summary: who is right?.

605 views • 32 slides

Beyond bags of features: Part-based models

Beyond bags of features: Part-based models. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. visual codeword with displacement vectors. Implicit shape models. Visual codebook is used to index votes for object position.

393 views • 32 slides

CVPR 2009, Miami, Florida

CVPR 2009, Miami, Florida. Object Detection Using a Max-Margin Hough Transform. Subhransu Maji and Jitendra Malik University of California at Berkeley, Berkeley, CA-94720. Overview. Overview of probabilistic Hough transform Learning framework Experiments Summary.

394 views • 24 slides

CVPR 2009, Miami, Florida

CVPR 2009, Miami, Florida. Object Detection Using a Max-Margin Hough Transform. Subhransu Maji and Jitendra Malik University of California at Berkeley, Berkeley, CA-94720. Overview. Overview of probabilistic Hough transform Learning framework Experiments Summary.

376 views • 24 slides

CVPR 2009, Miami, Florida

CVPR 2009, Miami, Florida. Object Detection Using a Max-Margin Hough Transform. Subhransu Maji and Jitendra Malik University of California at Berkeley, Berkeley, CA-94720. Overview. Overview of probabilistic Hough transform Learning framework Experiments Summary.

245 views • 24 slides