Computer Vision: Object Categorization II (Part-based) (Do, 08.01.2015)

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Beschreibung:

Bag-of-Words Model, Implicit Shape Model, Deformable Part-based Model

Kapitel:

00:00:00
Lecture 16: Part-based Models for Object Categorization
00:00:08
Announcements
00:02:14
Course Outline
00:04:33
Topics of This Lecture
00:04:39
Recap: Recognition with Local Features
00:06:07
Recap: Indexing features
00:10:20
Extension: tf-idf Weighting
00:13:40
Recap: Fast Indexing with Vocabulary Trees
00:15:56
Application for Content Based Img Retrieval
00:17:23
Video Google System
00:19:36
Collecting Words Within a Query Region
00:20:06
Example Results
00:20:16
More Results
00:21:09
Recap: Geometric Verification by Alignment
00:22:45
Applications: Aachen Tourist Guide
00:24:31
Applications: Fast Image Registration
00:25:16
Applications: Mobile Augmented Reality
00:26:38
Topics of This Lecture
00:28:46
Recognition of Object Categories
00:31:42
Part-Based Models
00:33:42
Different Connectivity Structures
00:37:12
Topics of This Lecture
00:37:21
Analogy to Documents
00:38:38
Object -> Bag of 'words'
00:40:15
Bag of Visual Words
00:40:49
Similarity, Bags-of-Textons for Texture Repr.
00:46:11
Comparing Bags of Words
00:46:58
Learning/Recognition with BoW Histograms
00:47:48
Recap: Categorization with Bags-of-Words
00:49:05
BoW for Object Categorization
00:55:44
Limitations of BoW Representations
00:56:33
Spatial Pyramid Representation
00:57:46
Summary: Bag-of-Words
00:58:49
Topics of This Lecture
00:59:21
Implicit Shape Model (ISM)
01:00:56
Implicit Shape Model: Basic Idea
01:02:15
Implicit Shape Model - Representation
01:03:53
Implicit Shape Model - Recognition
01:05:41
Example: Results on Cows
01:09:06
Scale Invariant Voting
01:09:57
Detection Results
01:10:33
Detections Using Ground Plane Constraints
01:11:57
Extension: Rotation-Invariant Detection
01:12:42
Sometimes, Rotation Invariance is Needed...
01:13:07
Implicit Shape Model - Segmentation
01:13:51
Example Results: Motorbikes
01:14:22
You Can Try It At Home...
01:15:17
Topics of This Lecture
01:16:25
Starting Point: HOG Sliding-Window Detector
01:17:40
Deformable Part-based Models
01:18:32
2-Component Bicycle Model
01:20:20
Object Hypothesis
01:21:19
Score of a Hypothesis
01:22:26
Recognition Model
01:24:22
Results: Persons
01:24:43
Results: Bicycles
01:25:11
False Positives
01:25:27
Results: Cats
01:25:36
you Can Try It At Home...
01:26:27
References and Further Reading